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AI in Insurance: A Deep-Dive

Blog Aug 24, 2022

Insurance: A Background

If insurance were a person in today’s world, it would be a baby boomer (1946-1964), hardworking and disciplined. Not only for the way it functions but also because of how the insurance industry is modeled to cater to the boomer insurance market. Given that insurance took off after the Second World War in 1945, this is hardly surprising.

But with the changing customer demographics, radical competitive dynamics, disruptive technology and innovations, digital transformation has become inevitable for the traditional insurers to stay relevant. Particularly with Insurtechs and Bigtechs entering the race armed with innovative technologies and personalized customer experiences.

Technologies like Artificial Intelligence (AI) and its components like OCR (Optical Character Recognition), Machine Learning (ML), Natural Language Processing (NLP), etc., are the biggest enablers and allies of these new players – their trump card. From sentiment analysis to predictive analytics, the applications of AI are vast and diverse. AI is often deployed in complex processes and trained on large data sets to discover new ways to tackle operational challenges and improve customer experience.

According to IBM, “Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.”

In any case, the primary objective of AI is to augment human capability, facilitate informed decision-making and reduce manual efforts.

The Need for AI in Insurance

Historically, the insurance industry has always been one that’s resistant to change. And its adoption of technology innovations has been relatively slow compared to other players in the banking and financial services sector. 

The fact that the insurance industry is heavily regulated and paperwork-driven with drawn-out, bureaucratic processes certainly doesn’t make things easier. But ironically, this is also why it is perfect for introducing AI. 

The insurance industry contains a wealth of business and customer data just waiting to be tapped into. Effectively harnessing the data collected using AI and related technologies can give insurance companies actionable insights into customer behavior and help them identify process gaps in their core operations. Locating these gaps also enables insurers to automate repetitive processes, besides modernizing legacy systems, reducing manual intervention, and increasing overall process efficiency.

And this can be a serious competitive advantage in terms of:

  • Learning the modern customer’s expectations,
  • Tailoring insurer offerings based on data analysis and visualization,
  • Introducing dynamic pricing,
  • Expediting core processes, and 
  • Advancing risk mitigation and fraud detection. 

Top Use Cases for AI in Insurance

According to Deloitte, almost 40% of all practitioners who have not yet invested in AI don’t know where to use AI in their business. So, here are some of the core insurance processes where AI can be of high value:

Document processing

Being a paper-heavy industry, most insurance companies rely on questionnaires, insurance claim forms, supporting documents, contracts, etc., flowing in from multiple input channels, offline and online. Manual handling of this complex, unstructured data is cumbersome, time-consuming, and prone to errors.

This is where AI and its components come in to digitize and structure the process. First, OCR digitizes data from scanned documents. NLP extracts meaningful data and adds structure, while neural networks identify patterns in the data collected for predictive analytics. Predictive analytics is a subset of advanced analytics that predicts future outcomes using historical data, statistical analysis, data mining techniques, and machine learning. That said, the system learns throughout the digitization process with machine learning to detect anomalies and deliver actionable insights.

Hence, AI reduces turnaround time (TAT), ensures faster processing, and cuts back on manual effort, allowing employees to focus more on acquiring customers and enhancing customer experience.


Underwriters deal with massive volumes of information and data from fragmented sources to assess risks effectively. Collecting, validating, and sorting through all this information is labor-intensive and takes the majority of an underwriter’s time, aside from increasing risk due to poor judgment and lack of consistency.

However, deploying an automated underwriting engine together with a predictive model (automated underwriting + predictive analytics) for risk assessment simplifies this process. Once document processing is complete, relevant business or customer data is fed into the underwriting engine to decide whether the policy application can go for straight-through-processing (no manual underwriting) or if it needs manual underwriting. Traditionally, the engine makes this decision based on codified rules. But a predictive model uses historical data to predict future outcomes, improving decision making. This enables insurance companies to unlock ongoing risk management to adapt to the evolving risk conditions. It also reduces the process cycle time with more straight-through processing (STP), adds value to the underwriter’s role, and mitigates risk. Similarly, it refines customer experience by lowering the number of application questions and required disclosures.

Claims processing

Claims are integral to a customer’s relationship with the insurer. Except, like every other insurance process, claims are manually processed, driven by time-consuming paperwork, and liable to errors, resulting in negative customer experiences. A poor claims experience can even affect the profitability of insurers.

AI streamlines the claims process through the end-to-end cycle, from First Notice of Loss (FNOL) to settlement. First, the customer files the FNOL via an NLP-based chatbot (conversational AI), which assists the customer in filing a damage description report and records the chat history to protect against subrogation using predictive analysis. Next, AI processes the required documents according to the claim complexity, value, urgency, etc. And the claim data is either triaged to a human or for STP, just as in underwriting. Computer vision i.e., a component of AI used to infer meaning from images and videos is then used to analyze the extent of damage, eliminating the need for manual inspections. And the claim is adjudicated with ML. Following this, AI, ML, predictive analytics, etc., help spot a fraudulent claim and aid in claim settlements, as well as audits. As a result, the claims process becomes streamlined with accurate fraud detection, quicker TAT, and minimized inefficiencies. At the same time, the claim journey becomes customer-centric, providing insurance companies the opportunity to boost revenue.

Pricing and Product Development

Pricing and product development have emerged as pain points for insurance companies due to increasing competition, rising regulations, and well-informed customers. The one size fits all approach and the cost-plus model are no longer sufficient. Likewise, if an insurance product doesn’t address customer demands, insurance companies lose their competitive edge.

Only taking a data-driven approach to quantify risks through AI’s machine learning and predictive analytics can empower insurers to kill the two birds – pricing and product development, with one stone. From application scoring to risk segmentation, the AI algorithm assists actuaries throughout the risk management process to control risks better. A dynamic pricing model tailors insurance premiums to each customer by assessing market conditions and calculating risk with real-time customer data. Once pricing and risk are managed effectively, insurers can perform a demand analysis to provide more and new insurance products, become flexible in their offerings, and quickly react to the market changes. They can further personalize their services with products like on-demand and usage-based insurance. Pricing and product development are crucial to earning customer satisfaction and loyalty. But more importantly, they are key differentiators when it comes to securing an edge over the competition.

The AI r-evolution in insurance

The role of AI in insurance is clearly versatile and diverse, as evidenced by the above use cases. It has exposed the inefficiencies, gaps, and shortcomings of the traditional insurance business. It has also already convinced several insurers to overcome their laggard ways. Except, AI is still a maturing technology, and the extent of its influence only grows as the technology evolves.

…the (Artificial Intelligence) revolution is more of an evolution, especially in traditional sectors like manufacturing, maintenance, and insurance, where physical objects play a key role.

What’s more, AI differs from other digital transformation technologies because it requires an organizational approach to harness its full potential.

One of the biggest mistakes leaders make is to view AI as a plug-and-play technology with immediate returns.

This implies that a coordinated effort is necessary from a technical, operational, and business standpoint to leverage AI for organizational goals, rather than solving isolated business challenges. Eliminating organizational siloes and data fragmentation is the first step, since AI needs cross-functional collaboration and extensive data resources for optimal functioning. Thereby, introducing AI to tackle challenges facing the insurance industry is merely step one. It is not a magic bullet.

The Future of AI in Insurance

As AI rebrands itself from the evil, job-stealing mastermind to that of an enabler, it is changing the business landscape for the better. And the insurance industry is no exception. By augmenting the capabilities of humans and eliminating menial tasks, the value that AI brings to the insurance sector is unparalleled, which is why AI is here to stay. So, the sooner insurance companies adopt AI into their ecosystems, the more value they gain from their investments.