case study on insurance company

• January 06, 2017

Digital Transformation Case Study – Insurance

View our case study on how Sutherland digitally transformed a leading insurance company through a best-in-class technology-enabled service solution.

A $4.2 billion international insurer offering a range of specialty insurance products

The client was among the first to capitalize on putting insurers in direct touch with potential customers eager to buy their products via online channels. Even with more information available online, the insurer needed methods to better support customers to address questions and advance the sales process. Therefore, the carrier needed to implement live call handling for quoting, binding, and customer service to ensure all customer questions were satisfactorily answered. To expand its successful UK model across the diversity of business needs in the US, the company looked for a partner with demonstrated capabilities encompassing:

• Policy underwriting experience, specifically for business-to-business property and casualty insurance
• Multi-channel, inbound and outbound customer contact processes
• An integrated technology platform supporting seamless customer service across multiple policy systems
• A strong pool of advisors trained and licensed to back up the online sales platform with responsive support for direct buyers and over 160 partners and wholesalers in 49 states
• Proven call center technology and infrastructure
• Ability to quickly scale based on explosive growth

The overall goal was to drive a seismic shift in how business consumers buy insurance, while establishing the company as a premium brand in the US.

The Sutherland Transformation

Sutherland created a best-in-class service center staffed with a team of dedicated agents licensed in 49 states and the District of Columbia. These agents were specially recruited for this insurer and trained to understand the target customer. We also provided a technology-enabled, service solution with a single interface for all agent needs. Key components include:

• Multichannel customer contact via phone, email, chat, and callback
• Specially trained licensed agents selling customized commercial insurance products
• Integrated front and back-office operations, providing single-system access to advisors for quotes, underwriting, sales, premium processing, and customer service
• Policy underwriting and issuance
• First notice of loss
• Experienced team management and quality assurance personnel
• Dedicated client space in Sutherland’s insurance center of excellence
• Sales operations dashboard and telephony reports to monitor performance and to continuously improve the customer experience with multi-touch alerts, expiring credit card alerts, and new product endorsement tracking to identify individual sales opportunities

All customer contacts are serviced and tracked in the same platform, which integrates with the insurer’s backend policy administration and financial systems to ensure seamless transactions for both the agent and the customer. An integrated third-party customer satisfaction survey provides unbiased customer feedback, helping the company track the growth of their brand in this new geography.

“Sutherland provided a technology-enabled, service solution with a single interface for all agent needs.”

While the online solution provided self-service capabilities and around-the-clock information access, Sutherland’s human-based customer support proved to be a strong differentiator that ensured customer confidence in the carrier’s products. Sutherland managed an action plan to build a direct distribution solution for the insurer. In addition, tight integration with the sales backend systems streamlined timelines, contained costs, and made reporting more robust.

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Services Digital Consulting, Digital CX, Data & Analytics, Business Operations

Industry Insurance

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Digital case study

Insurance claims estimator uses AI for efficiency

How insurance claims estimators can use AI to increase efficiency, reduce cycle time and improve customer experience

Our Role: PwC helped an insurtech company use AI and analytics to speed up their claims estimation process. Industry: Financial Services , Fintech Services: Emerging Tech , Artificial Intelligence Digital , Technology

Can the insurance claims estimate generation process be optimized and scaled to support exponential growth?

The insurance industry has experienced its share of technological disruption over the past ten years. As widespread smartphone adoption made it easier for consumers to submit images of collisions in support of auto insurance claims, insurance carriers were deluged, further bogging down an already frustrating and time consuming process. Where others saw chaos, our client saw an opportunity. They hired a team of experienced estimators and auto body experts and started to evaluate photos of auto damage for carriers as a service.

Drawing on their collective experience, the team would look at the photos and provide insurance companies with a preliminary assessment of the damage to the vehicle. Our client was filling a void in the industry, and their business exploded. They now service over 70 insurance companies and are a significant player in the insurtech space. Their challenge was that there are only so many experienced estimators and auto body experts available for hire, and at the rate the company was growing, they knew their growth would soon be constrained by limited resources.

Our client knew that using artificial intelligence (“AI”) and image recognition technology was a possible solution. It could address their scaling challenge and help them continue to improve customer experience. They built a data science team as a start. But then quickly found that building trust in the AI-assisted system, both for their estimators and customers, was key to making this approach successful.

Analyze the estimation process, leverage insertion points for PwC’s Analytics and AI solutions and focus on building trust with the estimators through “explainable AI”

The process

The client is laser-focused on enhancing customer experience. PwC’s AI and Analytics team realized that improving the efficiency, quality, accuracy and consistency of the estimation process would have a major positive impact on their insurance company clients and policyholders through a decrease in claim resolution time. The initial project scope was to work with the estimation team to look at the estimation process involving images and determine whether an AI model could detect which parts of individual vehicles had been damaged based on submitted photos. Automating this previously manual process was an important first step in our collaboration.

PwC’s Analytics & AI Transformation Solution

Working together with the client’s data science team and estimators, we helped create three AI models. The first was to detect and classify car damages from the images, isolating where in the image there was damage and what type of damage was represented. The second model translated damage into individual parts affected by adding additional information to the analysis, such as parts lists for damaged subassemblies, etc. The third model retrieved images of similar vehicles, both damaged and undamaged, to help estimators evaluate whether or not the part was actually damaged. The estimators were pleasantly surprised with the accuracy and depth of the results. The AI model was even catching details that estimators had missed through the manual process. So far, so good.

Building trust in the AI model

Now for the hard part. The ability of an AI model to accurately interpret images was only half of the equation. The AI model needed to be trusted by the estimators as an important augmentation of their analysis. To address this, we partnered with our client’s data scientists to implement an “explainable AI” (XAI) technique. We then used it in a novel way, showing visually why the model arrived at a particular prediction by making analytical determinations at every stage in the process. By doing this, we greatly increased trust in the model as well and demonstrated its value to the data scientists, the estimators, the insurance companies and the insurance company’s clients

“Our client’s experience is a further indication that it’s not about AI replacing human activity, but rather AI aiding and augmenting human activity. And for that to happen, humans need to trust the machine.” Anand Rao PwC Partner

PwC partnered with our client to use explainable AI to improve customer experience and establish trust in the system for its estimators.

The company’s explainable AI model was a game changer as It enabled the following:

• empowered auto claim estimators to identify where to focus attention during an assessment
• provided approaches for sharing knowledge among the estimator team to accurately determine which group should handle specific estimates
• identified 29% efficiency savings possible with full implementation of proof of concept models across the estimator team
• reduced rework and improved customer experience through reduced cycle times

Through this unique approach to explainable AI, the company has offered a powerful example to the industry of the potential power of AI-assisted processes. They now have concrete validation that an AI-assisted approach works and has the potential to improve other aspects of their overall process. The company has a clear model for growth and a foundation for scalability.

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Partner, PwC US

Francois Ramette

‎‎Partner, PwC US

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Cloud case studies

Insurance company invests boldly in transformation, waepa: building trust with customer centric-capabilities and cloud-enabled insights.

To become a more insights-driven business, WAEPA leveraged cloud and an ecosystem of tools and capabilities to redefine how it serves its members and stakeholders.

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The need to effectively and meaningfully connect with customers through digital channels has never been greater. Insurance customers today expect “one-click,” transactional capabilities just like the ones they enjoy elsewhere in their everyday digital lives. They want to be able to access quotes, product details, personal information, and account services anytime, anywhere, and on any device. At the same time, businesses increasingly require solutions that can help them stay ahead of customers’ growing expectations and demands, allowing them to more effectively leverage their data for rapid insights and improved service.

At Worldwide Assurance for Employees of Public Agencies (WAEPA), a 78-year-old nonprofit provider of life insurance for federal civilian employees and their families, the need for change has accelerated as industry and customer expectations have changed. To increase membership, prepare for expansion opportunities, better serve existing members, and improve business operations, WAEPA needed to invest boldly in transformation, with responsive cloud technology as a central component.

"Insurance customers today expect “one-click,” transactional capabilities just like the ones they enjoy elsewhere in their everyday digital lives."

What happened next

For WAEPA leaders, the guiding vision that emerged was one of organizational digital transformation, not simply moving existing data and processes to the cloud. To become a more customer-centric, insights-driven business, WAEPA leveraged cloud and an extended ecosystem of tools and capabilities to redefine how it serves its members and its internal stakeholders and to position the organization to evolve with future needs.

WAEPA decided to build on its existing Microsoft Azure, Dynamics, and Power Platform investments and footprint, enlisting Deloitte based upon its demonstrated track record of delivering a spectrum of services, from strategic planning to hands-on technology development and operations to commercial, government, state, and local clients.

Deloitte’s approach to developing a modern, intuitive, and mobile-friendly web interface for WAEPA was driven by its Digital Studio’s human-centric design thinking. Deloitte also applied its deep knowledge in transforming digital experiences and leveraged its intellectual property to accelerate the development and launch of WAEPA’s new website and portal.

From a customer-facing perspective, WAEPA’s digital transformation has resulted in improved website navigation and an integrated self-service member portal, among other enhancements. These improvements have allowed WAEPA members to transact business more effectively and efficiently while accessing a more complete picture of their WAEPA relationship and activity.

Internally, the cloud-led transformation has afforded WAEPA a streamlined and more agile IT landscape, as well as greater visibility into IT operations. It also has helped enhance the organization’s customer service operations through new functionality that includes a member message center.

On the analytics front, the Deloitte-led transformation has provided a centralized cloud data repository and dynamic business intelligence dashboards. The result: improved accessibility, accuracy, and completeness of data to support more confident decision-making—from the member-facing frontlines all the way to the boardroom. In addition to providing a “bird’s-eye view” for the highest levels of the organization, the new WAEPA environment supports business intelligence insights across marketing, customer service, finance, IT, and other business functions.

By the numbers

+46,000 members:  WAEPA members include US civilian federal employees and family members who now benefit from the improved experience

+38% web boost:  Website traffic and engagement has increased significantly

+20 self-service capabilities:  Portal enhancements improve the member experience by providing new self-service capabilities to members for managing their policies and membership

+25% increase in applications:  New membership requests are pointing to new growth for WAEPA

+10 data sources become one:  More than 10 disparate data sources have been consolidated into a unified data lake

+20 new KPIs:  New benchmarks and measures now drive key business decisions across the enterprise

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Case Study: A Single Source of Truth to Increase Data Quality and Integrity

This case study explores how a leading Property Casualty Insurance company in North America, with $39.8 billion in revenue, leveraged Capsifi to overcome these challenges by transforming its data architecture and decision- making processes.

23 August 24

A Single Source of Truth: Transforming Property Casualty Insurance with Capsifi

In the competitive and rapidly evolving world of Property Casualty Insurance, businesses face numerous challenges. From underwriting losses and rising costs to evolving risks and competition from InsurTech companies, the landscape is constantly shifting.This case study explores how a leading Property Casualty Insurance company in North America, with $39.8 billion in revenue, leveraged Capsifi to overcome these challenges by transforming its data architecture and decision-making processes.

The Challenge: A Struggling Business Architecture Team:

The company’s Business Architecture team was in its early stages of development, and the demand for data to support decision-making was growing rapidly. The team faced several hurdles:

• Rising costs and slow premium growth were impacting profitability.
• They were burdened with manual architecture activities, which involved maintaining multiple files of content that needed to be periodically aligned. company unknowingly procured new tech, adding complexity instead of value.
• The process was slow, inefficient, and prone to errors, with different versions of analysis provided to different stakeholders.
• The time taken to respond to requests for insights often reduced the value of the information they provided.

With multiple sources of truth and manual reconciliation, the team found it difficult to keep up with the demands for timely and accurate insights, which further complicated their decision-making processes.

The Approach: Leveraging Capsifi to Simplify and Streamline

Recognizing the need for change, the company identified key objectives that would drive significant improvements in their processes. They chose to focus on small, manageable changes that would have a large impact.

• They loaded and aligned available assets, standardized values, and naming conventions, and iterated to produce meaningful visualizations.
• By minimizing parallel testing and retiring manual files and calculation methods, they took calculated risks that paid off.
• Capsifi’s powerful visualizations were able to tell the story that the team had been struggling to communicate. With the new system in place, the team was able to free up time, which allowed them to provide decision-makers with additional insights, further demonstrating their understanding of the business and its priorities. Capsifi’s speed and efficiency in providing data-rich insights naturally built credibility with stakeholders.

Capsifi allows us the ability to perform our analysis faster to support our recommendations for our stakeholders. ​

Manager, Business Architecture​

The Results: Efficiency, Credibility, and Growth

The transformation was profound. Capsifi became the company’s sole source of truth for strategy and operating model information. The efficiencies introduced by the system enabled the team to extend their analysis into areas they previously didn’t have time or tools to explore. As a result:

• The company eliminated 25% of daily data reconciliation tasks, saving 151 hours per month.
• They retired 65 Excel and Visio files that had previously been maintained and aligned manually.
• Capsifi became the system of record across functions, allowing more time for value-adding activities.
• The architecture team expanded their stakeholder base, including strategy leaders and strategic portfolio leaders, providing trusted data and earning their confidence.

Through this partnership with Capsifi, the Property Casualty Insurance company successfully navigated the challenges of a complex and dynamic industry, ultimately positioning themselves for continued growth and success. This case demonstrates the power of Capsifi to transform not only data quality and integrity but also the way a company operates at a fundamental level.

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How a Nordic insurance company automated claims processing

By automating unstructured data, a Nordic insurer increased its operational efficiency and improved customer experience.

• 1. Better question
• 2. Better answer
• 3. Better working world
• How EY can help

The better the question

How do you leverage AI to streamline insurance claims?

An insurer harnessed AI to streamline operations and boost agent capabilities.

N avigating large volumes of unstructured insurance claims data is a substantial challenge for any insurance company looking to automate repetitive yet complex tasks across its operations. The integration of advanced technologies such as automation and artificial intelligence (AI) has become a pivotal driver for the exponential transformation of insurance organizations.

A leading insurance firm in the Nordics wanted to modernize its claims management process. The current workflow required manual processing of each claim request, a time-consuming series of repetitive tasks which had to be performed by its agents. Amplifying the complexity was unstructured data from various sources, requiring detailed examination and categorization. The spectrum of claims data was also broad and varied, originating from multiple sources such as bills, invoices, cash receipts from pharmacies and local clinics, and medical treatment diagnoses with supporting documents.

Each document required thorough analysis by the agents to extract necessary data that could then be categorized based on the relevance for claim processing. As a result, customer service efficiency suffered; the teams struggled to process claims swiftly due to the limited capabilities of existing technology infrastructure that was ill-equipped to facilitate end-to-end automation.

A transformative approach was necessary. The insurance company engaged with EY teams to analyze its existing tech infrastructure and business needs. The goal was to develop an AI-based solution that could streamline their claims management process, automate routine tasks, and free up their agents to concentrate on building stronger customer relationships. By placing humans at the center of this AI-powered transformation, the company sought to augment the agents' capabilities, enhancing their performance and job satisfaction.

This strategy extended beyond a technological upgrade. It was about empowering their people, preparing them for a future where they can collaborate effectively with AI to drive impactful results. This initiative aimed to set the stage for the insurer's expansion into new markets, with their AI-powered claims management solution acting as a catalyst.

The better the answer

Transforming insurance claims with human-centric AI

An EY solution that transforms the insurer’s data process by converging business insights, domain knowledge and AI.

Transforming the insurer's traditional work environment required a robust, AI-powered solution. Manual handling of claim reimbursements involved laborious tasks like opening individual images and PDF scans, analyzing files, and uploading relevant documents to their systems. EY teams stepped in with EY Fabric Document Intelligence, an AI-powered product, to streamline this process.

Developed by EY wavespace™ Madrid and now integrated into EY Fabric, EY Fabric Document Intelligence is a technology that converts semi-structured and unstructured documents into actionable structured data. Built on machine learning and the Python programming language, the tool is hosted on an EY-secured cloud environment. It was scaled and customized to meet the insurer's specific requirements during the project.

The process was simple yet transformative. Agents uploaded the scanned copies to EY Fabric Document Intelligence, which began image cleansing by detecting relevant data. This involved removing image backgrounds, correcting document rotation and reducing noise to enhance the quality of scanned files. The product then performed preprocessing, document analysis and layout analysis. Using optical character recognition (OCR) and natural language processing (NLP), unstructured data was converted and classified. Finally, the structured data was transferred to the insurer's core claims system.

The product processes more documents over time, the system improves and has the potential to provide continuing value and insights for the insurer. Our team provided a solution that did more than just digitize and organize unstructured data. It combined EY business insights and industry knowledge within a strategic technology ecosystem to help modernize the insurer's operations. EY professionals offered comprehensive services, including solution design, system integration, data science, project management and cloud computing knowledge, with a human-centric approach.

Teams from Sweden, Denmark, Spain, the US and the UK collaborated to integrate EY Fabric Document Intelligence with the insurer's legacy system. They helped the insurer understand that AI, when guided by human insight, serves as a powerful enabler to expedite work.

The better the world works

Unlocking exponential transformation through AI-driven modernization

The EY team’s AI solution is driving accelerated claim processing, deepened customer relationships, and an enterprise-wide modernization.

The insurance firm now benefits from near real-time processing of claim documents. Since the solution's implementation, a remarkable 70% of the documents fed into the system are correctly extracted and interpreted. This crucial upgrade not only expedites decision-making but also allows agents to concentrate their efforts on enhancing customer interactions. Agents have more time to spend with their customers in providing personalized advice. This shift toward more meaningful engagements cultivates stronger relationships, maintaining trust and driving additional business value. The solution is designed in a way that it gives the required control of the automation and AI technologies to the insurance firm. The data is entered into the system and the output is driven through a controlled set of confidence levels avoiding blackbox implementation of AI. As the client was closely involved in all decision-making, they have sufficient transparency to the solution.

The AI solution has also been instrumental in facilitating the insurer's global expansion goals. The operational efficiency and customer service enhancements resulting from the implementation are sparking curiosity in other areas of the organization. The insurer is now actively exploring further modernization opportunities, recognizing the long-term business value of aligning with the right technology.

The EY-tailored solution has not only streamlined the insurer's processes but also served as a catalyst for reimagining their entire enterprise. The implementation has demonstrated how advanced AI can fuel exponential transformation in the industry, augment human capabilities, and create significant value. With EY and the insurer collaborating to place humans at the center of this AI transformation, the result is a long-term solution that empowers individuals and drives meaningful impact across the organization.

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AB Insurance

Delivering distinctive solutions.

Translating Our Insurance DNA and Culture of Innovation into Better Outcomes… Responsibly

Case Studies

Learn how we’ve partnered with some of our clients to develop the best solutions to their needs.

Strategic Portfolio Repositioning

Working closely with a leading multiline insurance company, we followed a comprehensive four-step process to reposition its traditional core portfolio. By incorporating more diversified exposures, including illiquid assets, we enhanced book yield while meeting the client’s objectives.

Process and Methodology

• Define objective: yield, risk budget, duration, liquidity, income
• Incorporate investment and noninvestment client constraints into analysis: hedging, regulatory considerations, accounting
• Analyze current allocation and create repositioning plan
• Consultative partnership between client and asset-class experts
• Iterative internal allocation process between subject experts
• Discuss options to determine optimal Strategic Asset Allocation: stress/scenario testing, relative value, capital impacts, ESG
• Review allocation and adjust based on current market dynamics, subject to guidelines and constraints
• Leverage AB’s tech and innovation to take liquidity and market opportunities into account while executing quickly
• Added 50 b.p. of book yield and compliant with guidelines
• Greater diversification: rotated out of munis, US government, IG credit to fund private credit, EMD, high-yield and securitized assets
• Enhanced yield with illiquid assets

For illustrative purposes only. There can be no assurance that any investment objectives will be achieved.

Blended Emerging-Market Debt Solution

We worked with a midsize life insurer that was seeking alternate approaches to increasing its book yield. After assessing the client’s needs, we designed a diversified investment-grade portfolio of emerging-market debt that blended hard-currency corporate and sovereign bonds.

Introduced EMD to prospect who was seeking alternative ways to boost book yield while preserving quality. Met and conducted due diligence with AB team over 18- to 24-month span.

Employed iterative process to determine client constraints and diversification requirements; continued due diligence with AB team.

Produced diversified investment-grade portfolio with blend of hard-currency corporates and sovereigns. Small bucket for opportunistic high yield.

All portfolio statistics, characteristics and holdings are subject to change. There can be no assurance that any investment objectives will be achieved. As of January 31, 2022

Insurance Case Studies: The Ultimate Guide

How do you show your prospects exactly what your company can do for them? With a case study. Insurance case studies provide buyers with the solid facts, figures and performance examples they need to make a purchasing decision.

What Is a Case Study?

A case study is a powerful marketing tool that is often used in the middle or bottom of the sales funnel, to help buyers develop a preference for your offering. Case studies increase buying confidence and validate the buying decision. They prove that your product or service performs as promised.

Case studies change the conversation from telling to showing. Instead of just telling prospects how great your brand is, you can show prospects exactly what your brand is capable of achieving. This gives prospects a more concrete understanding of what to expect and enables them to step into the shoes of a satisfied customer.

The Elements of a Case Study

An effective case study contains key elements that align with the buyer’s journey:

• Who is the customer? If you don’t have permission to name the customer, describe the customer by industry, size and other relevant details.
• What problem were they trying to solve? When B2B buyers are shopping for a product, they’re trying to solve a specific problem. Identify this problem so other prospects with similar problems can relate. Sometimes, there’s more than one problem.
• What other solutions did they consider? B2B buyers usually consider multiple options before deciding. This is a key part of the process, and including the details can help emphasize why your company stood out.
• Why did they choose your solution? State exactly what your company offered that other competitors couldn’t.
• What was the implementation process like? Switching B2B vendors can be a major undertaking. Describe the process so your prospects will know what to expect. Of course, you want to put your company in a positive light and focus on the positives, but you can include hiccups that occurred in the process and how you handled them. Prospects know that things don’t always go perfectly according to plan, and this shows that you’re competent and able to overcome any barriers that arise.
• How has the company benefited? In the beginning of the case study, you described the client’s problem. Now touch back on this and show how the problem has been solved. Include specific information about how the company has benefited from your product or service. For example, how much time or money has the client saved? How are they better prepared? How have you helped increase their revenues?
• What are the key takeaways for others in a similar situation? Convince prospects that they should follow the client’s example and partner with your company. Summarize your argument with key takeaways from the case study.

Case Study Dos and Don’ts

To squeeze the most value from your case study, you need to follow some best practices.

• Don’t just list bulleted facts. You might be able to pull a list of bulleted facts from your case study to use in other types of content, such as social media posts, but your actual case study should be at least two pages long to tell the story in a meaningful way.
• Do use names. Your case study will carry a lot more weight if the featured customer is named. You can also use anonymous case studies, but they are not quite as credible.
• Do appeal to emotions. Think of your case study as a story. You’re showing your prospects what it’s like to work with your company, so you need to build a narrative. This is also a good place to build an emotional argument by showing how your company can help clients deal with pain points and reduce problems.
• Don’t make your case study longer than it needs to be. Your prospects are busy, and they probably won’t have time to read an overly verbose document. Provide enough information to create a compelling story, but don’t get bogged down with unnecessary details. An effective case study will often be around two pages.
• Do include quotes. You can say that your company is great, but it sounds more convincing if one of your customers says it. The featured customer may be willing to provide you with quotes and their logo to include in your case study. As a bonus, this is also free publicity for them.
• Do get permission. If you’re naming a customer, you will need to secure their permission and allow them the opportunity to review and approve the final piece before it is used. Be sure to save their written approval in case questions arise in the future.

Case Studies Should Be Part of Your Content Library

Creating a case study can take some time and effort, but they are worth it.

According to the 2022 Edelman Trust Barometer , 63% of people worry business leaders are trying to mislead people with false or exaggerated statements. If you just say that your company can help people solve their problems, your prospects might not believe you. You need to build credibility, and a case study that provides concrete examples can help.

A case study is also a fantastic way to differentiate yourself from your competition. As mentioned earlier, case studies are often used in more advanced stages of the customer journey, when prospects are already familiar with your company and need information that’s going to guide their purchasing decision. You want to create preference for your company so you can close the sale, and a case study can help you do that.

Your Case Study Can Convert Leads for Years

Some content has a short life, but not case studies. Although creating a case study will require some effort, once you have it, you can use it for as long as you continue to offer the featured products and services, and the featured company is still a customer.

• Feature your case study on your website. You can create a landing page for your case study or case studies so prospects can find them easily. They can be offered as instant downloads or you can generate leads by gating them with a form.
• Promote your case study in social media posts. Spread the word about your case study on social media. You can always just write a post and link to the case study, but also consider designing graphics for the case study. A LinkedIn carousel post is another great way to highlight your case study on social media.
• Encourage prospects to download the study as a call to action in your blog posts . This is a practical way to encourage your audience to learn more about your company.
• Link to case studies in email nurturing campaigns . If you’re nurturing a prospect that has downloaded other content, a case study link can entice additional engagement.
• Build case studies into your sales processes . Sales professionals love using case studies as handouts as they provide a great springboard for meaningful conversations.
• Use content to develop webinars or videos . You can even use case study content as the foundation for very powerful webinars or videos.

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Insurance 2030—The impact of AI on the future of insurance

Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. His digital personal assistant orders him a a vehicle with self-driving capabilities for a meeting across town. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into “active” mode. Scott’s personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott’s assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a “pay-as-you-live” basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account.

When Scott pulls into his destination’s parking lot, his car bumps into one of several parking signs. As soon as the car stops moving, its internal diagnostics determine the extent of the damage. His personal assistant instructs him to take three pictures of the front right bumper area and two of the surroundings. By the time Scott gets back to the driver’s seat, the screen on the dash informs him of the damage, confirms the claim has been approved, and reports that a mobile response drone has been dispatched to the lot for inspection. If the vehicle is drivable, it may be directed to the nearest in-network garage for repair after a replacement vehicle arrives.

While this scenario may seem beyond the horizon, such integrated user stories will emerge across all lines of insurance with increasing frequency over the next decade. In fact, all the technologies required above already exist, and many are available to consumers. With the new wave of deep learning techniques, such as convolutional neural networks, 1 Convolutional neural networks contain millions of simulated “neurons” structured in layers. artificial intelligence (AI) has the potential to live up to its promise of mimicking the perception, reasoning, learning, and problem solving of the human mind (Exhibit 1). In this evolution, insurance will shift from its current state of “detect and repair” to “predict and prevent,” transforming every aspect of the industry in the process. The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience.

As AI becomes more deeply integrated in the industry, carriers must position themselves to respond to the changing business landscape. Insurance executives must understand the factors that will contribute to this change and how AI will reshape claims, distribution, and underwriting and pricing. With this understanding, they can start to build the skills and talent, embrace the emerging technologies, and create the culture and perspective needed to be successful players in the insurance industry of the future.

Four AI-related trends shaping insurance

AI’s underlying technologies are already being deployed in our businesses, homes, and vehicles, as well as on our person. The disruption from COVID-19 changed the timelines for the adoption of AI by significantly accelerating digitization for insurers. Virtually overnight, organizations had to adjust to accommodate remote workforces, expand their digital capabilities to support distribution, and upgrade their online channels. While most organizations likely didn't invest heavily in AI during the pandemic, the increased emphasis on digital technologies and a greater willingness to embrace change will put them in a better position to incorporate AI into their operations.

Four core technology trends, tightly coupled with (and sometimes enabled by) AI, will reshape the insurance industry over the next decade.

Explosion of data from connected devices

In industrial settings, equipment with sensors have been omnipresent for some time, but the coming years will see a huge increase in the number of connected consumer devices. The penetration of existing devices (such as cars, fitness trackers, home assistants, smartphones, and smart watches) will continue to increase rapidly, joined by new, growing categories such as clothing, eyewear, home appliances, medical devices, and shoes. Experts estimate there will be up to one trillion connected devices by 2025. 2 World Economic Forum, 2015. The resulting avalanche of new data created by these devices will allow carriers to understand their clients more deeply, resulting in new product categories, more personalized pricing, and increasingly real-time service delivery.

Experts estimate there will be up to one trillion connected devices by 2025.

Increased prevalence of physical robotics

The field of robotics has seen many exciting achievements recently, and this innovation will continue to change how humans interact with the world around them. Additive manufacturing, also known as 3-D printing, will radically reshape manufacturing and the commercial insurance products of the future. By 2025, 3-D-printed buildings will be common, and carriers will need to assess how this development changes risk assessments. In addition, programmable, autonomous drones; autonomous farming equipment; and enhanced surgical robots will all be commercially viable in the next decade. By 2030, a much larger proportion of standard vehicles will have autonomous features, such as self-driving capabilities. Carriers will need to understand how the increasing presence of robotics in everyday life and across industries will shift risk pools, change customer expectations, and enable new products and channels.

Open-source and data ecosystems

As data becomes ubiquitous, open-source protocols will emerge to ensure data can be shared and used across industries. Various public and private entities will come together to create ecosystems in order to share data for multiple use cases under a common regulatory and cybersecurity framework. For example, wearable data could be ported directly to insurance carriers, and connected-home and auto data could be made available through Amazon, Apple, Google, and a variety of consumer device manufacturers.

Advances in cognitive technologies

Convolutional neural networks and other deep learning technologies currently used primarily for image, voice, and unstructured text processing will evolve to be applied in a wide variety of applications. These cognitive technologies, which are loosely based on the human brain’s ability to learn through decomposition and inference, will become the standard approach for processing the incredibly large and complex data streams that will be generated by “active” insurance products tied to an individual’s behavior and activities. With the increased commercialization of these types of technologies, carriers will have access to models that are constantly learning and adapting to the world around them—enabling new product categories and engagement techniques while responding to shifts in underlying risks or behaviors in real time.

The state of insurance in 2030

AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Advanced technologies and data are already affecting distribution and underwriting, with policies being priced, purchased, and bound in near real time. An in-depth examination at what insurance may look like in 2030 highlights dramatic changes across the insurance value chain.

Distribution

The experience of purchasing insurance is faster , with less active involvement on the part of the insurer and the customer. Enough information is known about individual behavior, with AI algorithms creating risk profiles, so that cycle times for completing the purchase of an auto, commercial, or life policy will be reduced to minutes or even seconds. Auto and home carriers have enabled instant quotes for some time but will continue to refine their ability to issue policies immediately to a wider range of customers as telematics and in-home Internet of Things (IoT) devices proliferate and pricing algorithms mature. Many life carriers are experimenting with simplified issue products, but most are restricted to only the healthiest applicants and are priced higher than a comparable fully underwritten product. As AI permeates life underwriting and carriers are able to identify risk in a much more granular and sophisticated way, we will see a new wave of mass-market instant issue products.

Smart contracts enabled by blockchain instantaneously authorize payments from a customer’s financial account. Meanwhile, contract processing and payment verification are eliminated or streamlined, reducing customer acquisition costs for insurers. The purchase of commercial insurance is similarly expedited as the combination of drones, IoT, and other available data provides sufficient information for AI-based cognitive models to proactively generate a bindable quote.

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Highly dynamic, usage-based insurance (UBI) products proliferate and are tailored to the behavior of individual consumers. Insurance transitions from a “purchase and annual renewal” model to a continuous cycle, as product offerings constantly adapt to an individual’s behavioral patterns. Furthermore, products are disaggregated substantially into microcoverage elements (for example, phone battery insurance, flight delay insurance, different coverage for a washer and dryer within the home) that consumers can customize to their particular needs, with the ability to instantaneously compare prices from various carriers for their individualized baskets of insurance products. New products emerge to cover the shifting nature of living arrangements and travel. UBI becomes the norm as physical assets are shared across multiple parties, with a pay-by-mile or pay-by-ride model for car sharing and pay-by-stay insurance for home-sharing services, such as Airbnb. 3 Some insurtech companies are already designing these types of products; Slice, for example, provides variable commercial insurance specifically tailored for home sharing.

The role of insurance agents has changed dramatically by 2030. The number of agents is reduced substantially as active agents retire and remaining agents rely heavily on technology to increase productivity. The role of agents transitions to process facilitators and product educators. The agent of the future can sell nearly all types of coverage and adds value by helping clients manage their portfolios of coverage across experiences, health, life, mobility, personal property, and residential. Agents use smart personal assistants to optimize their tasks as well as AI-enabled bots to find potential deals for clients. These tools help agents to support a substantially larger client base while making customer interactions (a mix of in-person, virtual, and digital) shorter and more meaningful, given that each interaction will be tailored to the exact current and future needs of each individual client.

Underwriting and pricing

In 2030, underwriting as we know it today ceases to exist for most personal and small-business products across life and property and casualty insurance. The process of underwriting is reduced to a few seconds as the majority of underwriting is automated and supported by a combination of machine and deep learning models built within the technology stack. These models are powered by internal data as well as a broad set of external data accessed through application programming interfaces and outside data and analytics providers. Information collected from devices provided by mainline carriers, reinsurers, product manufacturers, and product distributors is aggregated in a variety of data repositories and data streams. These information sources enable insurers to make ex ante decisions regarding underwriting and pricing, enabling proactive outreach with a bindable quote for a product bundle tailored to the buyer’s risk profile and coverage needs.

Regulators review AI-enabled, machine learning–based models, a task that requires a transparent method for determining traceability of a score (similar to the rating factor derivations used today with regression-based coefficients). To verify that data usage is appropriate for marketing and underwriting, regulators assess a combination of model inputs. They also develop test policies for providers when determining rates in online plans to ensure the algorithm results are within approved bounds. Public policy considerations limit access to certain sensitive and predictive data (such as health and genetic information) that would decrease underwriting and pricing flexibility and increase antiselection risk in some segments.

Price remains central in consumer decision making, but carriers innovate to diminish competition purely on price. Sophisticated proprietary platforms connect customers and insurers and offer customers differentiated experiences, features, and value. In some segments, price competition intensifies, and razor-thin margins are the norm, while in other segments, unique insurance offerings enable margin expansion and differentiation. In jurisdictions where change is embraced, the pace of pricing innovation is rapid. Pricing is available in real time based on usage and a dynamic, data-rich assessment of risk, empowering consumers to make decisions about how their actions influence coverage, insurability, and pricing.

Claims processing in 2030 remains a primary function of carriers , but more than half of claims activities have been replaced by automation. Advanced algorithms handle initial claims routing, increasing efficiency and accuracy.

IoT sensors and an array of data-capture technologies, such as drones, largely replace traditional, manual methods of first notice of loss. Claims triage and repair services are often triggered automatically upon loss. In the case of an auto accident, for example, a policyholder takes streaming video of the damage, which is translated into loss descriptions and estimate amounts. Vehicles with autonomous features that sustain minor damage direct themselves to repair shops for service while another car with autonomous features is dispatched in the interim. In the home, IoT devices will be increasingly used to proactively monitor water levels, temperature, and other key risk factors and will proactively alert both tenants and insurers of issues before they arise.

Automated customer service apps handle most policyholder interactions through voice and text, directly following self-learning scripts that interface with the claims, fraud, medical service, policy, and repair systems. The turnaround time for resolution of many claims is measured in minutes rather than days or weeks. Human claims management focuses on a few areas: complex and unusual claims, contested claims where human interaction and negotiation are empowered by analytics and data-driven insights, claims linked to systemic issues and risks created by new technology (for example, hackers infiltrate critical IoT systems), and random manual reviews of claims to ensure sufficient oversight of algorithmic decision making.

Claims organizations increase their focus on risk monitoring, prevention, and mitigation. IoT and new data sources are used to monitor risk and trigger interventions when factors exceed AI-defined thresholds. Customer interaction with insurance claims organizations focuses on avoiding potential loss. Individuals receive real-time alerts that may be linked with automatic interventions for inspection, maintenance, and repair. For large-scale catastrophe claims, insurers monitor homes and vehicles in real time using integrated IoT, telematics, and mobile phone data, assuming mobile phone service and power haven’t been disrupted in the area. When power goes out, insurers can prefile claims by using data aggregators, which consolidate data from satellites, networked drones, weather services, and policyholder data in real time. This system is pretested by the largest carriers across multiple catastrophe types, so highly accurate loss estimations are reliably filed in a real emergency. Detailed reports are automatically provided to reinsurers for faster reinsurance capital flow.

How insurers can prepare for accelerating changes

The rapid evolution of the industry will be fueled by the extensive adoption and integration of automation, deep learning, and external data ecosystems. While no one can predict exactly what insurance might look like in 2030, carriers can take several steps now to prepare for change.

1. Get smart on AI-related technologies and trends

Although the tectonic shifts in the industry will be tech-focused, addressing them is not the domain of the IT team. Instead, board members and customer-experience teams should invest the time and resources to build a deep understanding of these AI-related technologies. Part of this effort will require exploring hypothesis-driven scenarios in order to understand and highlight where and when disruption might occur—and what it means for certain business lines. For example, insurers are unlikely to gain much insights from limited-scale IoT pilot projects in discrete parts of the business. Instead, they must proceed with purpose and an understanding of how their organization might participate in the IoT ecosystem at scale. Pilots and proof-of-concept (POC) projects should be designed to test not just how a technology works but also how successful the carrier might be operating in a particular role within a data- or IoT-based ecosystem.

Future of insurance: Unleashing growth through new business building

2. develop and begin implementation of a coherent strategic plan.

Building on the insights from AI explorations, carriers must decide how to use technology to support their business strategy. The senior leadership team’s long-term strategic plan will require a multiyear transformation that touches operations, talent, and technology. Some carriers are already beginning to take innovative approaches such as starting their own venture-capital arms, acquiring promising insurtech companies, and forging partnerships with leading academic institutions. Insurers should develop a perspective on areas they want to invest in to meet or beat the market and what strategic approach—for example, forming a new entity or building in-house strategic capabilities—is best suited for their organization.

This plan should address all four dimensions involved in any large-scale, analytics-based initiative—everything from data to people to culture (Exhibit 2). The plan should outline a road map of AI-based pilots and POCs and detail which parts of the organization will require investments in skill building or focused change management. Most important, a detailed schedule of milestones and checkpoints is essential to allow the organization to determine, on a regular basis, how the plan should be modified to address any shifts in the evolution of AI technologies and significant changes or disruptions within the industry.

In addition to being able to understand and implement AI technologies, carriers also need to develop strategic responses to coming macrolevel changes. As many lines shift toward a “predict and prevent” methodology, carriers will need to rethink their customer engagement and branding, product design, and core earnings. Auto accidents will be reduced through use of vehicles with self-driving capabilities, in-home flooding will be prevented by IoT devices, buildings will be reprinted after a natural disaster, and lives will be saved and extended by improved healthcare. Likewise, vehicles will still break down, natural disasters will continue to devastate coastal regions, and individuals will require effective medical care and support when a loved one passes. As these changes take root, profit pools will shift, new types and lines of products will emerge, and how consumers interact with their insurers will change substantially.

All of these efforts can produce a coherent analytics and technology strategy that addresses all aspects of the business, with a keen eye on both value creation and differentiation.

3. Create and execute a comprehensive data strategy

Data is fast becoming one of the most—if not the most—valuable asset for any organization. The insurance industry is no different: how carriers identify, quantify, place, and manage risk is all predicated on the volume and quality of data they acquire during a policy’s life cycle. Most AI technologies will perform best when they have a high volume of data from a variety of sources. As such, carriers must develop a well-structured and actionable strategy with regard to both internal and external data. Internal data will need to be organized in ways that enable and support the agile development of new analytics insights and capabilities. With external data, carriers must focus on securing access to data that enriches and complements their internal data sets. The real challenge will be gaining access in a cost-efficient way. As the external data ecosystem continues to expand, it will likely remain highly fragmented, making it quite difficult to identify high-quality data at a reasonable cost. Overall, data strategy will need to include a variety of ways to obtain and secure access to external data, as well as ways to combine this data with internal sources. Carriers should be prepared to have a multifaceted procurement strategy that could include the direct acquisition of data assets and providers, licensing of data sources, use of data APIs, and partnerships with data brokers.

4. Create the right talent and technology infrastructure

In augmented chess, average players enabled by AI tend to do better than expert chess players enabled by the same AI. The underlying reason for this counterintuitive outcome depends on whether the individual interacting with AI embraces, trusts, and understands the supporting technology. To ensure that every part of the organization views advanced analytics as a must-have capability, carriers must make measured but sustained investments in people. The insurance organization of the future will require talent with the right mindsets and skills . The next generation of successful frontline insurance workers will be in increasingly high demand and must possess a unique mix of being technologically adept, creative, and willing to work at something that will not be a static process but rather a mix of semiautomated and machine-supported tasks that continually evolve. Generating value from the AI use cases of the future will require carriers to integrate skills, technology, and insights from around the organization to deliver unique, holistic customer experiences. Doing so will require a conscious culture shift for most carriers that will rely on buy-in and leadership from the executive suite. Developing an aggressive strategy to attract, cultivate, and retain a variety of workers with critical skill sets will be essential to keep pace. These roles will include data engineers, data scientists, technologists, cloud computing specialists, and experience designers. To retain knowledge while also ensuring the business has the new skills and capabilities necessary to compete, many organizations will design and implement reskilling programs. As a last component of developing the new workforce, organizations will identify external resources and partners to augment in-house capabilities that will help carriers secure the needed support for business evolution and execution. The IT architecture of the future will also be radically different from today’s. Carriers should start making targeted investments to enable the migration to a more future-forward technology stack that can support a two-speed IT architecture .

Rapid advances in technologies in the next decade will lead to disruptive changes in the insurance industry. The winners in AI-based insurance will be carriers that use new technologies to create innovative products, harness cognitive learning insights from new data sources, streamline processes and lower costs, and exceed customer expectations for individualization and dynamic adaptation. Most important, carriers that adopt a mindset focused on creating opportunities from disruptive technologies—instead of viewing them as a threat to their current business—will thrive in the insurance industry in 2030.

Ramnath Balasubramanian and Ari Libarikian are senior partners in McKinsey’s New York office, and Doug McElhaney is a partner in the Washington, DC, office.

The authors would like to acknowledge the contributions of Gijs Biermans, Bayard Gennert, Nick Milinkovich, and Erik Summers.

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ServiceNow | Insurance industry case study

Harmonizing IT processes for an international insurance firm

A leading insurance company

Harmonizing IT processes

The challenge

A leading insurance company wanted to implement an ITSM platform that could streamline processes across different business units and vendors worldwide. At the time, their existing systems had fallen out of sync and were highly customized, creating delays, frustration, and unnecessary expense. Problems were arising in areas from onboarding to event management, and performance reporting became almost an impossible task.

Our solution

During the first phase of the project, we looked closely at the existing architecture, focusing our efforts on understanding the processes that were already in place, what was not working, and what changes were required to create an effective solution. To achieve this, we conducted a series of in-depth workshops with users at business centers across the globe, testing, reviewing, and re-engineering our platform to suit their different concerns, needs, and priorities.

Success through collaboration

Our solution was based directly on our close collaboration with users on-site. Working with them, we were able to establish a single global process for all of their core IT infrastructure library (ITIL) procedures, including global reporting and metrics. Not only did this streamline the user experience, it also gave the company broad visibility of data across their different units and supplier landscape. To support self-service, we also designed an extensive and detailed organizational change management (OCM) strategy, which included a highly successful train-the-trainer program. The enterprise-wide solution automated routine processes wherever possible and reduced the items in their service catalog by more than half, resulting in a streamlined and positive end-user experience.

Short time frames, lasting change

The client’s challenge was for us to implement the breadth of ServiceNow’s ITSM platform across a complex, global organization in an extremely short window of time. To meet that challenge, we drew on our team of KPMG professionals and developers, some with over nearly a decade of experience in implementing ServiceNow. By combining this know-how, our business knowledge, and ServiceNow’s technology, we were able to work collaboratively with this international client to help aggressively transform the way they deliver IT services, creating a unified platform that could support and drive large-scale organizational change well into the future.

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IT overhaul for US insurer

The challenge

After providing automobile insurance across the U.S. for over a century, a national insurance carrier found itself lagging on the technology front. Disparate and antiquated IT systems across all areas, from policy and claims administration platforms to phone systems, led to significantly higher costs than the industry standard. Frequent outages and long recovery times disrupted the business and its customers.

Realizing it needed a radical technology overhaul, the company hired a new chief information officer (CIO) to lead its IT transformation. The CIO engaged Cognizant to stabilize the existing IT infrastructure and to develop and implement an enterprise-wide digital architecture.

Our approach

Cognizant collaborated with the CIO throughout a six-year transformational journey, developing an architectural roadmap that would deliver a customer-centric platform as well as enable digital and data capabilities. Cognizant began stabilizing the company’s legacy systems, fixing bugs, extending functionality and improving overall reliability to create a bridge to the new digital architecture.

Once a stable IT environment was achieved, the team consolidated and replaced all policy and claims administration systems, installed a new customer relationship management (CRM) system, and implemented data analytics and digital platforms that would engage customers and agents within a flexible, web services-oriented architecture. The company simultaneously upgraded its underlying insurance products, creating additional disruption in an already challenging initiative.

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Making insurance simpler, personal and proactive yields big benefits

The benefits the insurance company achieved through its extensive digital transformation spanned the entire enterprise, including policy and claims administration, data and analytics, digital solutions for customers and agents, CRM, human resources and accounting. Digital solutions—including web-based customer self-service capabilities—increased customer satisfaction and reduced the cost of customer service, leading to web portal sales of approximately $260 million in annual premiums.

reduction in IT costs

reduction in claims processing time

increase in the company’s Net Promoter Score

improvement in fraud prevention and detection

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ICICI Prudential Life Insurance: Using Vision API to efficiently process thousands of documents

About ICICI Prudential Life Insurance

ICICI Prudential Life Insurance aims to lead the Indian insurance field through quality products and a hassle-free claim settlement experience. A customer-centric company, it offers long-term savings and protection plans to meet customers’ needs at every stage of life.

ICICI Prudential Life Insurance significantly enhances the speed and efficiency of its underwriting and approval processes by harnessing the agility and machine learning capabilities of Vision API.

Google cloud results.

• Helps enable instant document approval with optical character recognition by Vision API
• Processes 100,000 documents in 20 minutes with automated document processing product Recognic, powered by Vision API and Apigee
• Helps increase the number of applications processed by 30% within the same timeframe

Cuts document processing time from 10 minutes to 10 seconds

The insurance landscape in India has seen significant changes in recent years with the adoption of new technology. As one of the major insurance providers in the country, ICICI Prudential Life Insurance has aimed to lead in this transformation journey. "There has been a data explosion across India over the past few years, together with a high mobile penetration rate. Today, about 60% of our customers approach us via mobile, for example, which was certainly not the case before," says Alpesh Karnik, SVP, IT, at ICICI Life Insurance.

Consumer expectations have also evolved, with easier access to information and online services. "Consumers today are more informed on the importance of investing in insurance products, so there’s much more of a pull factor when it comes to sales, but they also want to be able to get these products quickly and easily," adds Alpesh. To meet the demands of these consumers, ICICI Prudential Life Insurance realized it needed to make its processes even faster and more efficient. Looking to upgrade its infrastructure, the company turned to Google Cloud .

“The biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others.”

Serving customers better by speeding up processes with Google Cloud

ICICI Prudential Life Insurance's distributors were already using tablets to input customer data faster and more efficiently, but many of the company’s solutions still required a team at the back end to manually sift through documents for approval. This meant that customers needed to wait five or six hours, or sometimes until the next working day, to know if their documents were approved or needed revision.

That all changed after partnering with Google Cloud Premier Partner Searce to take advantage of its AI/ML powered automated document processing product Recognic , which is built on Google Cloud. Developed using the optical character recognition (OCR) capabilities of Cloud Vision , Recognic reads, understands, and validates documents at scale, enabling organizations that handle massive amounts of paperwork to digitize these documents and then accurately store and index them.

“Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources."

“In the case of ICICI Prudential, the biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others,” Alpesh adds.

Alpesh explains that if the details on the application form match the documents provided, the case doesn’t need to go to the underwriter for further checks and can go directly to policy issuance. “Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources."

ICICI Prudential Life Insurance is also working with Searce to build deep learning models into Recognic so that it can overcome template barriers and input data from a variety of forms. This is particularly helpful for financial and medical documents underwriting because unlike a passport or driving license, financial documents have a higher structural complexity.

As customer data becomes more important in the work of ICICI Prudential Life Insurance, so does protecting it, and the company is taking every measure to safeguard the security and privacy of its customers’ information. “Details of customers' contactability are automatically removed by Google Cloud after processing is complete. This step in the workflow gives us the confidence that data is not stored at any level of the optical character recognition process," says Alpesh.

Partnering with the right teams for dedicated support

In achieving the best solution for its business goals, ICICI Prudential Life Insurance recognizes the importance of its decision to work with partners that truly understand the insurance business. "There are many intricacies involved in this business, and it’s clear that both Google Cloud and Searce really took the time to understand our underwriting processes before coming up with a solution," says Alpesh. He adds that during the implementation process, all findings were well documented and queries were responded to quickly.

"We didn't want to take any shortcuts deploying Recognic, but at the same time, we didn't want to draw out the implementation process. The excellent support from both Google Cloud and Searce throughout the journey was reassuring for us as they were always thinking ahead."

Future-proofing the organization through machine learning and AI

In the coming years, Alpesh foresees the insurance industry to be even more agile than it is today. "I doubt elaborate processes such as underwriting or operations checks will need to be done manually in the future. Everything will be done through machine learning and AI.” In light of this, ICICI Prudential Life Insurance is doing everything it can to prepare, as customers’ expectations are set to keep evolving. "We have to be prepared for the future, and I believe that with Google Cloud, we can do it.”

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About Searce

Searce is a niche cloud consulting business with futuristic tech in its DNA, focused on “realizing the Next in the Now” for its clients. Specializing in cloud data engineering, AI/ML, and advanced cloud InfraTech such as Anthos and Kubernetes, Searce is one of the top partners for Google Cloud globally, with 3,000+ clients successfully moved to cloud.

LIC Case Study | Success Story of Life Insurance Corporation of India

Ria Puneyani

Sales of life insurance policies are a vital source of revenue for any life insurance company and their primary motivation for doing business. Because today's business operations are so intertwined, claim settlement services significantly influence life insurance policy sales. People can use life insurance plans to cover a variety of hazards throughout life.

The insurance industry grew rapidly in the first two decades of the twentieth century. In 1938, it increased from 44 firms with a total business-in-force of Rs.22 crore to 176 companies with a total business-in-force of Rs.298 crore. The call for the life insurance sector to be nationalised had been voiced before, but it gained traction in 1944 when a measure to modify the Life Insurance Act 1938 was filed in the Legislative Assembly. However, it was not until 1956 that life insurance was nationalised in India when the Life Insurance Corporation was passed by the Indian Parliament on June 19.

Origin of LIC LIC's Objectives Growth of LIC LIC's at Present LIC's Products and Services LIC Services for its Employees LIC's Marketing Strategy Conclusion FAQs

Origin of LIC

The Life Insurance Corporation of India was established on September 1, 1956, by the Ministry of Finance of the Government of India, with the goal of making life insurance more widely available, particularly in rural areas, with the goal of reaching all insurable persons in the country and providing adequate financial cover at a reasonable cost.

LIC's Objectives

The primary goal of LIC is to promote life insurance across the country, particularly in rural regions and among the socially and economically disadvantaged, to reach all insurable individuals and provide them with appropriate financial protection against death at a fair cost.

Maximise people's savings mobilisation by making insurance-linked savings sufficiently appealing. Another goal is to function as trustees for the insured public in their individual and collective capacities, meeting the community's diverse life insurance demands as the social and economic environment changes.

LIC intends to involve all employees to the best of their abilities to advance the insured public's interests by delivering prompt and courteous service.

Growth of LIC

In 1956, LIC had 5 zone offices, 33 divisional offices, and 212 branch offices in addition to its corporate office. Because life insurance contracts are long-term contracts that require a range of services during the policy's life, LIC felt the necessity to extend operations and open a branch office at each district headquarters in subsequent years.

The LIC was reorganised, and it created a considerable number of new branch offices. It shifted servicing tasks to branches due to the reorganisation, and departments were declared accounting units. It had a significant impact on the company's success. You can observe that from about INR 200 crores in new business in 1957, the company only exceeded INR 1000 crores in 1969-70, and it took another ten years for LIC to reach the INR 2000 crore barrier. However, after reorganisation in the early 1980s, LIC had already surpassed INR 7000 crores in Sum Assured on new policies by 1985-86.

LIC's at Present

LIC has practically monopolised the solicitation and sale of life insurance plans in India, having existed as a massive insurance business for almost 60 years. LIC has expanded its operations outside of India to 14 countries to meet the insurance needs of Non-Resident Indians.

With an asset value of INR 2,529,390 crores, LIC is now India's largest life insurance business, controlled by the government. LIC's headquarters are in Mumbai .

It currently operates eight zonal offices and 113 divisional offices around the nation. It has 2,048 branches across India in various towns and cities.

In addition, LIC maintains a network of over 15 million agents that sell life insurance to the general population. The LIC had a total life fund of $28.3 trillion as of 2019. In the 2018–19 fiscal year, the total value of sold insurance was $21.4 million. In 2018–19, LIC resolved 26 million claims. With 290 million policyholders, it is the largest insurance company in the world.

The Life Insurance Corporation of India (LIC of India) is one of India's largest financial organisations, providing comprehensive financial solutions for all aspects of life. It has a customer base of around 23 crores, making it the largest insurance company globally. After Indian Railways, it is the second-largest real estate owner in the country. The LIC advertises through newspapers , radio, television, billboards, and other media.

LIC's Products and Services

The Life Insurance Corporation of India (LIC) offers a variety of life insurance plans. As a government-owned Life Insurance Firm, LIC's policies are in high demand and appeal to a broad spectrum of consumers.

LIC For endowment, LIC offers the Jeevan Pragati, LIC Jeevan Labh, LIC Single Premium Endowment Plan, LIC's New Endowment Plan, New Jeevan Anand, LIC's Jeevan Rakshak, LIC's Limited Premium Endowment Plan, LIC's Jeevan Lakshya, LIC's Aadhaar Shila, and LIC's Aadhar Stambh.

LIC Jeevan Umang specialises in life insurance.

LIC's Bima Shree, LIC's Jeevan Shiromani, LIC's New Money Back Plan- 20 years, LIC's New Money Back Plan-25 years, LIC New Bima Bachat, LIC's Jeevan Tarun are some of the money-back plans available. Money-back plans include LIC's Anmol Jeevan II and LIC's e-term Plan.

Their pension schemes include the Pradhan Mantri Vaya Vandana Yojana, LIC New Jeevan Nidhi, and LIC's Jeevan Akshay.

LIC Services for its Employees

Agents are being offered home loans.

The LIC of India's Agents Housing Scheme provides house loans to the company's agents. It has a separate subsidiary, LIC-HFL, from which many housing plans are moved for fairer distribution.

Employees are given meal coupons.

In September 2010, the Life Insurance Corporation of India (LIC of India) introduced a one-of-a-kind benefit for all workers. The number of meal vouchers is determined by each team member's position in the hierarchy.

Team member participation in sports is encouraged.

Employees of the LIC of India are encouraged to participate in various sporting activities to improve their physical fitness and overall personality. Employees have also spoken on behalf of the company at different national and international levels. It has recruited numerous workers from its Sports Recruitment Quota to maintain competitive excellence in sports and to compete on an equal footing with other businesses.

Training its employees

LIC has begun to provide training to its staff at all levels of the organisation. It has established a distinct Human Resources Development / Organizational Development (HRD/OD) Department to develop and enhance capabilities, commitment and foster a learning and performance-focused culture.

LIC's Marketing Strategy

LIC Life Insurance's marketing approach is pretty basic. Its primary goal is to educate consumers about the company's different policies and brands. Personal selling, exhibits, demonstrations at events, advertising, and innovative schemes have all been used by LIC to achieve this goal.

As presents and incentives, policyholders are given bags, diaries, and calendars. As promotional activities, advertisements are displayed on TVs, newspapers, and billboards .

A mobile advertising van circulates across rural regions, raising awareness of the firm. LIC-Life Insurance has a website and a webpage where it provides thorough information on each potential inquiry to satisfy customers.

LIC is continually working to strengthen "Brand LIC" and strengthen the brand's link with growing market segments. It has done so by maintaining a regular media presence in national and regional outlets.

It has also sponsored several national and international programmes and a variety of activities such as newspaper campaigns and continuous coverage of goods in several publications.

Who is the founder of LIC?

LIC has been founded by Government of India in 1956.

What are the Subsidiary companies of LIC?

LIC subsidiary companies are:

• LIC Pension Fund Limited
• LIC Cards Service Limited
• IDBI Bank Limited

What is the number of employees in LIC?

There are 1,14,000 employees (2020) working for LIC, and over 10 Lakh LIC agents.

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Technology trends transforming the motor insurance sector in India

Recent case studies.

The Indian insurance industry is undergoing technological transformation. Technology in the insurance sector is changing the ecosystem not only in scaling up, but also in its fundamental aspects. The digital-first business model is the end result of firms leveraging data and technology more than ever before, assessing new business models and profits streams powered by new technology. For both the insurer and the insured, advanced insurance technology is now an integral part of the industry.

Today's consumers have extra complicated needs and are more informed and conscious of their preferences than ever before. They want customized offers and personalised communications that lead insurers to digitise their services and optimize all communications.

The business ecosystem is evolving with the rapid emergence of new technologies such as artificial intelligence (AI) and the Internet of Things (IoT), and established companies in the market are adopting strategies to enable their businesses to digitally transform. The application of any emerging technology is a top-down exercise for the industry, beginning from assessing new age technology adoption to changes in customer behaviour, the insurance company must lead the show and usher in the digital transformation.

The insurance industry in India has adapted to many developments, changes and trends such as:

Artificial Intelligence (AI): Consumers are continuously seeing for customised experiences, especially when buying for something as important as insurance. AI enables insurers to build these unique experiences in reaction to the high-speed demands of new consumers.

With AI, insurers can enhance claims processing cycles and essentially change the underwriting process. AI also assists insurers to access data faster and remove the human element, resulting in more accurate reporting over shorter time frames.

Machine learning: Machine learning can not only improve complaint handling; however, can automate it. When files are digital and accessible via the cloud, they can be examined using pre-programmed algorithms, enhancing processing speed and accuracy. This automatic review can be applied for policy administration and risk assessment.

In addition, advances in ML (Machine Learning) allow current operators to systematise progressively more complex tasks, in particular by responding to all forms of customer requests. Machine learning is gradually being used to identify fraud, process automatic small claims payments and provide self-service digital damage assessments.

Telematics: The capabilities of Telematics will remain to influence auto insurance. It is wearable technology for cars as utilized in insurance technology. Cars are outfitted with monitoring equipment that assist in measuring various parameters such as speed, location and accident data. This data is then verified and processed using analysis software which in turn establishes the policy premium. The use of telematics has numerous benefits for both insurers and policyholders. It promotes better driving behaviour that is directly proportional to lower damage costs for insurers. Additionally, it facilitates companies to better assist their customers and add proactivity in relationships.

Chatbots: According to some reports, by 2025, 95% of all consumer interactions will be powered by chatbots. Effective use of AI and machine learning permits chatbots to communicate transparently with customers, saving everyone’s time in an organisation. This further helps in decreasing costs for insurance companies. A bot can assist a customer go through a policy request or complaint process, while keeping human intervention only in complex cases.

IoT: Most consumers are eager to share supplementary personal information if it means saving money on their insurance policies and the Internet of Things (IoT) can computerize much of this data exchange. Insurers can use data from IoT devices, such as the numerous elements of smart homes and wearable technologies, to better regulate prices, alleviate risk and even avert losses. The IoT will offer other insurance technologies with first-hand data, improving risk assessment accuracy and giving policyholders more power to directly influence the pricing of their policies.

Technology as a constructive influence in the auto insurance industry:  The future of insurance lies in the digitisation of processes, making them seamless for insurance agents and customers. The introduction of the IRDAI (Regulatory Sandbox) by the Insurance Regulatory and Development Authority specifies the government's vision to look into digitisation as the future of the sector. The regulation will generate an environment conducive to experimentation with insuretech solutions. Digitisation will also improve insurance penetrate in the semi-urban and rural segments of India over the next five years.

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