Industry
Insurance
Reinventing the Insurance Industry with a Digital-first Strategy
Solution
Artificial Intelligence
Shape the future with human-first AI
Highlights
- Customer experience in the insurance claims function leaves a lot to be desired, with complex, time-consuming, manual processes.
- The claims process offers multiple avenues for automation using composite AI in combination with geospatial data, wearables, and digital twins.
- Insurers must deploy AI agents across customer experience, back office operations, smart engineering, and IT operations to introduce innovations in the claims lifecycle.
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Introduction
For an industry that is more than 300 years old, the insurance sector has come a long way.
It has successfully navigated many technology shifts from mainframes to the client-server paradigm, mobile computing to cloud and machine learning (ML, modernizing the insurance value chain across sales and marketing, distribution, underwriting, claims management, and customer experience.
Artificial intelligence (AI) technologies will unleash the next wave of innovation in the industry, and insurers have been quick to recognize its potential. This is corroborated by the banking, financial services, and insurance (BFSI) industry report of the TCS AI for Business Study which reveals that 64% of firms are optimistic about AI’s impact on business. Furthermore, 76% of insurance respondents are focused on using AI to spur innovation and grow revenue.
Insurers have made slow but steady investments in AI and ML initiatives—a whopping 94% of insurance executives said they have AI implementation planned, in-process, or already completed—reexamining existing processes to determine how best to use AI for positive impact. Given the accelerated pace of innovation, leveraging the right AI foundations is key. With the advent of GenAI, insurance firms must reassess how some of their earlier AI investments can be leveraged for continued innovation. In our view, insurers can now unlock exponential value by embedding composite AI in conjunction with other emerging technologies such as geospatial data, wearables, and digital twins within insurance offerings.
The claims lifecycle
Our study reveals that a higher degree of personalized engagement in post-sales support is a top AI customer focus area in insurance.
Claims processing is a key component of post-sales support given its ability to enhance customer experience as well as overall profitability through cost and efficiency optimization.
After buying an insurance policy, raising a claim is one of the few touchpoints that policy holders have with an insurer. An effective and frictionless claims processing engine therefore assumes significance. But the reality is different—claims processing is often complicated and time-consuming. This can be attributed to a variety of reasons: each case can be unique, the evidence needed to validate each claim may vary widely, inclusions and exclusions can be different, and the potential for fraud is high. In addition, decisions on approving or rejecting claims demand human judgment. Thus, even today, despite substantial digitization, there are multiple manual steps involved, depending on the nature and value of the claim being processed.
The claims lifecycle involves several key personas, with each persona having to make an effective judgement and shorten processing time. Each persona needs to perform a list of tasks for every claim (see Figure 1). Simple claims may not need action from each persona. However, some claims may be complicated and need to be handled differently, for instance, by using a consultant from a special investigation unit (SIU) for high-value, suspicious claims.
Figure 1: AI empowerment for each persona to simplify the claims lifecycle
As can be seen in Figure 1, there is tremendous scope for insurers to implement composite AI initiatives across the claims lifecycle, to support all personas in the conduct of their activities. This will have a direct impact on the key performance indicators (KPIs) of insurers including net profit, customer satisfaction, and operational efficiencies.
The transformational power of AI
AI has tremendous potential to transform value streams across banking, financial services, and insurance firms.
While most insurers have leveraged predictive and GenAI to bring rapid efficiencies in several areas of the insurance value chain, claims processing has lagged behind.
In our view, insurers must embrace an ‘enterprise-wise’ AI approach to convert AI potential to performance, in a continuum that spans across the three stages of assist, augment, transform. This framework can be applied to claims processing to uncover new possibilities across various dimensions. The aim of automation through AI models and systems is to help human personas across the claims lifecycle perform their tasks easily and efficiently. Consequently, keeping individual personas and their priorities at the center of the discussion on change initiatives is of paramount importance. In a future-proof AI organization, all these personas will interact through an enterprise knowledge fabric that will leverage a combination of AI and large language models (LLMs), sitting on top of contextual artifacts, with role-based access provided to each purposive AI agent.
Purpose driven AI agents can enhance the claims function across the four dimensions of customer experience, back-office operations, smart engineering, and IT operations (see Figure 2) through the assist, augment, and transform continuum.
- In general, AI agents in the assist stage are more hands-on, mostly helping users in tasks like knowledge discovery, summarization, understanding the terms of insurance policies or contact center interactions, where GenAI can play a predominant role.
- In the augment stage, humans and machines collaborate, using predictive analytics to optimize operations and improve efficiency, for instance, estimating the nature and value of upcoming claims to better manage the workload. AI agents can help extract relevant information from images, audio, and video evidence to evaluate loss and document additional information from outside sources, such as witnesses or camera feeds.
- Finally, AI agents in the transform category could be a little more futuristic and would need enterprise wide AI platforms and capabilities. For instance, a smart chip which includes information on travel, destination, insurance terms and so on can be embedded in high value baggage. If the airline misplaces the baggage, the chip could automatically trigger a claim and settle the payment provided all the policy criteria are met.
Figure 1: AI empowerment for each persona to simplify the claims lifecycle
Insurers can deploy many more AI agents spanning basic rules engines, predictive, or generative capabilities. As insurance firms tread further in the AI journey and attain maturity, they will need to build orchestration platforms that can quickly switch agents as appropriate or direct tasks to the right agent to deliver the expected output.
The composite AI journey
Many insurance carriers started on their descriptive and predictive AI journeys a couple of years ago.
Several other organizations are focusing on foundational steps, such as data preparation and pilot projects, before moving on to more advanced and transformative AI applications. Firms that have adopted GenAI must evaluate how to build upon their investments, given the recent innovations in AI. For instance, backend knowledge bases can be strengthened using GenAI to enhance existing bot frameworks while frontend interfaces can be retained as-is.
We believe that the combinatorial power of multiple AI technologies and macro-trends will unlock business value for insurers rather than a single, specific technology. In the claims function, specific use cases will necessitate exploration with different AI technologies to identify the right ones to achieve the intended impact. Established AI tools such as predictive analytics and forecasting, personalization and recommendation engines, robotics, intelligent automation and simulations are in use in the claims function. Insurers must embrace prompt-engineering, fine-tuning, variational autoencoders and retrieval-augmented-generation (RAG) in combination with natural language processing (NLP) for agentic and GenAI use cases for added advantage and competitive differentiation. While GenAI experimentation is underway, adoption will become mainstream when exponential value can be generated for all the stakeholders—insurers, individual personas in the claims process, and the end consumer. Zeroing in on the right metrics to evaluate pilots and identify successful ones for full scale adoption will therefore become critical.
In addition, foundational elements such as data quality, enterprise AI architecture, ethical considerations, responsible and fair AI use, privacy, and security will need attention. In our view, composite AI is an unceasing and dynamic journey as firms will need to continually ideate and innovate across the assist-augment-transform paradigm to identify new AI value streams that can improve claims operations and experience.
While AI adoption is widespread in the insurance industry, most insurers are still in the early or early to middle stages of their journey. That AI can reimagine the entire insurance value chain is unquestionable; however, respondents in our study said that realizing transformational outcomes is still a distant reality with only 4% believing that AI is a differentiating factor for business transformation at this time.
In summary
AI and GenAI are poised to play a key role across the insurance value chain, given insurers’ desire to evolve into a ‘friend in need’ for their customers.
And claims automation is an area that offers significant opportunities for insurance firms to improve the lives of policy holders and support them through adversity.
However, a word of caution: insurance is a tightly regulated industry, and any technology adoption must be well thought through, without resorting to sudden, large changes which may have a potential to disrupt operations. For trouble-free implementation, insurance firms must consider partnering with an established service provider with the requisite technical and domain expertise after a well-rounded market analysis.
