17 Disruptive AI and Machine Learning Use Cases in Insurance World

Sep 08, 2021

Insurance | 11 min READ

AI and ML in Insurance Industry

The growing expanse of automation has been fast transforming every industrial landscape with proven benefits. The insurance sector is also quickly catching up to the AI bandwagon by its poised role in enhancing productivity and reducing costs. Many insurance service providers have been quick enough to automate routine tasks or assist human decision-making along the entire insurance value chain.

Shyam Somani

Former Head

Insurance Solutions

Birlasoft

The use of AI in insurance has been touted as one of the most pathbreaking developments, which result in substantial economic and societal benefits that eventually improve risk pooling and enhance risk reduction, mitigation, and prevention. Insurance companies can respond on time to requirements and ensure they can deliver high-quality service to the customer they promise through automation.

Conventional insurance players have predominantly been slow to react to technological changes. A Deloitte study stated that while almost all industries have achieved success with AI or have started investing in AI, the insurance industry seems to be lagging substantially, with only 1.33% of insurance companies have invested in AI compared to 32% in software and internet technologies. With the advent of InsureTech startups and technology incumbents, the scenario is fast changing now. They can deliver speedier claim payments, greater price transparency, and on-demand policies while simultaneously reducing the cost and resources required. The changing dynamics open up winds of opportunities for the AI-enabled insurance landscape globally.

AI and Machine Learning Use Cases in Insurance

The role of AI in insurance has been growing by leaps & bounds, from claims processing to compliance to risk mitigation and damage analysis. For instance, Robotic Process Automation (RPA) is being used to carry out repeated tasks so that operational teams can focus on more complex actions. AI is fundamentally changing the way insurers have been operating over the years.

There are immense opportunities to move from the traditional coding of complex processes to an iterative use of trained AI models against large (enterprise) datasets. AI has incredible potential across the entire insurance value chain, from marketing to underwriting and claims management. The industry is growing at a rapid clip, expected to cross $2.5 billion by 2025. This milestone indicates a compound annual growth rate of 30.3% between 2019 and 2025.

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Claims Processing
Claims Fraud Detection
Claims Adjudication
Automated Underwriting
Submission Intake
Pricing and Risk Management
Policy Servicing
Insurance Distribution
Product Recommendation
Property Damage Analysis
Automated Inspections
Customer Lifetime Value Prediction
Speech Analytics
Customer Segmentation
Workstream Balancing for Agents
Self-Servicing For Policy Management
Claim Volume Forecasting

#1 Claims Processing

Driven by policy and legal requirements, insurers need to ensure that the claims meet requisite criteria throughout the process cycle. Understandably, it is an ardent task to deal with thousands of claims and customer queries, making it time-consuming. Machine Learning makes the entire process efficient and effective. It dramatically improves claims processes value chain from moving claims through the initial report, analysis, and ultimately establishing contact with the customers. The process saves time and frees employees to focus on more complex claims and direct customer contact.

#2 Claims Fraud Detection

Federal Bureau of Investigation study on US insurance companies revealed that the total cost of insurance fraud (non-health insurance) is close to more than $40 billion per year. That means Insurance Fraud costs the average US family between $400 and $700 per year in the form of increased premiums. These startling statistics reflect the dire need for highly accurate automated theft detection tools to empower insurance companies to enhance their due diligence process.

#3 Claims Adjudication

Council for Affordable Quality (CAQH) Index report reveals that automating eligibility and claim verification can lead to an annual saving of $ 5.2 billion in healthcare insurance alone. The claim initiation automation process saves time for insurers with the help of a chatbot that interacts with customers and collects the required information. Through chatbots, information can be captured in a structured format, and a first-level validation can be carried out during the claim initiation process. World Economic Forum (WEF) study revealed that by 2022, 62% of an organization's data storage and data processes would be executed via computers. With the rising automation expanse, investing in auto-adjudication systems will help organizations stay relevant shortly.

#4 Automated Underwriting

Conventionally, insurance underwriting was heavily employee-dependent to analyze historical data and make informed decisions. Additionally, they had to alleviate risks and deliver customer value by working with haphazard systems, processes, and workflows. Intelligent process automation simplifies the underwriting experience by providing Machine Learning algorithms that collect and make sense of massive amounts of data. It also improves rules performance, manages straight-through-acceptance (STA) rates, and prevents application errors. By automating most of the process, underwriters can focus only on complex cases that may require manual attention.

#5 Submission Intake

Automation complemented by technologies like AI and NLP can extract data from structured and unstructured sources like ACORD forms, spreadsheets, loss runs, and brokers' emails to help underwriters collaborate effectively and make faster and more accurate risk decisions. Automation also facilitates better managing separate submission queues for new business, renewals, and endorsements wherein machine learning models quickly sort through hundreds of submissions and prioritize optimal entries based on risk appetite and underwriting triage guidelines.

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#6 Pricing and Risk Management

Price optimization deploys data analysis techniques to understand customers' reactions to different pricing strategies for products & services and find the best prices for a given company, considering its goals. Insurance companies mostly use GLMs (Generalised Linear Models) for price optimization for sectors like car and life assurance. This technique allows insurance companies to better understand their customers and balance capacity with demand and drive better conversion rates.

Similarly, Risk assessment automation enhances operating efficiency. By combining RPA with machine learning & cognitive technologies to create intelligent operations, risk assessment automation boosts productivity. As the automated process significantly reduces time, insurers can deliver a better customer experience and reduce churn.

#7 Policy Servicing

Automated intake of policy details enables integration with the policy administration system to retrieve details related to each policy. This reduces the manual effort for finding and locating relevant fields required for policy endorsements. It also enables parallel processing to manage complex scenarios where multiple requests get initiated from individual customers, which results in reduced turnaround time for policy processing and servicing. RPA in insurance aids in accomplishing a plethora of operations efficiently without involving vast navigation across systems. It automates transactional and administrative activities such as accounting, settlements, risk capture, credit control, tax, and regulatory compliance.

AI and Machine Learning Use Cases in Insurance

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#8 Insurance Distribution

Insurance customers would visit a local carrier or contact a financial planner to explore policy options in the pre-digital world. More often than not, there would be a leading carrier for a specific product in a localized market. Based on the information the customer provided, the carrier would perform underwriting activities and share a quote. Digitalized insurance distribution systems upended this picture.

Today, nearly every carrier has an online portal that allows customers to peruse their product and service catalog before deciding. This shift in consumer behavior prompted significant disruption in the insurance sector. The potential of AI goes beyond underwriting or claims approval; it could transform the sales and distribution phase of the insurance value chain, gaining from sophisticated AI algorithms available in the market today. Digital technologies such as optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) can help insurers gain from a customer's digital behavior.

This payoff points to a massive opportunity – with so many prospects researching digital channels, there is a vast repository of customer data that the AI engine can leverage, empowering the distributors to make smarter decisions.

#9 Product Recommendation

The insurance companies generate a lot of transaction data each day. In such a scenario, automation can assist companies in recommending insurance products for customers accurately and efficiently, eventually improving the competitiveness of the insurance company. Connected devices and wearables offer deep insights into the customer's physical condition, like blood pressure, temperature, pulse.

Nowadays, the insurer has the opportunity to explore the client's lifestyle patterns and preferences. Social media data from Facebook, Twitter, or other networks also is a great aid. By capturing and analyzing a customer's requirements, the insurance companies are better equipped to offer them a customized offering, which saves time and money and, most importantly, enhances customer confidence.

#10 Property Damage Analysis

Inspection is the first step in a damage insurance claims process, be it any asset - a mobile phone, automotive, or property. Assessing the damages to calculate repair costs is a daunting task for insurance providers with manual intervention. AI-powered object detection analyzes data, compares the level of damage before and after the event. Machine learning models are equipped to recognize damaged vehicle parts and help estimate the cost of repairs.

A PwC report highlighted the application of drone and AI technology to the insurance industry can result in savings of up to US$6.8 billion per year. Using the power of drones combined with automated object detection, the amount of time-solving a claim can be reduced between 25% and 50%.

#11 Automated Inspections

For a long, motor insurance claim estimation has been managed manually by claim adjusters and surveyors. Manual inspection requires the adjuster/surveyor to travel and interact with the policyholder, approximately costing $50 to $200 per inspection, making it a costly proposition. Claims settlement would also be slower as it takes about 1 to 7 days for report creation and estimation.

Through AI-based image processing, insurance companies can analyze the damage incurred to the vehicle. The system then generates an in-depth assessment report outlining the repairable and replaceable vehicle parts and their estimated costs. Insurers can cut down claim estimations costs and make the process highly efficient. Moreover, it also populates robust data to arrive at the final settlement amount.

#12 Customer Lifetime Value Prediction

Customer lifetime value (LTV) is one of the most critical tools that enable companies to trust customers and predict customer lifetime value through machine learning. A study conducted by Bain & Co. found that a 5% increase in retention rate can lead to a rise in a company's profit between 25%–95%. Machine Learning algorithms use a customer's purchase history and match it to a large inventory of products to discover hidden patterns and similar group products together.

These products are then made accessible to customers, which eventually encourages the purchase of the product. Understanding the customer's lifetime value enables insurers to find the perfect balance between customer retention and acquisition.

#13 Speech Analytics

In early times, insurance companies possessed a limited amount of data to assess a customer's profile. Additionally, Call Center executives used to have limited ability with a small amount of manually audited and transcribed phone calls. In such a scenario, the advent of the Speech Recognition tool makes perfect business sense for companies.

Speech recognition is a powerful tool to analyze customer speech based on lead calls to improve personalization. It can identify customer pain points with products through speech analytics of feedback to improve future products and detect fraud based on voice analysis of customer calls to improve security measures.

Speech analytics software often combines the power of Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Artificial intelligence (AI) technology. With the augment of such technology, companies are utilizing speech analytics tools to understand every aspect of their customer interactions and their contact center's performance and are applying those learnings in optimizing costs and generating higher revenue.

#14 Customer Segmentation

Customer segmentation is the first step towards enhancing personalization. It optimizes budgeting, product design, promotion, marketing, and customer satisfaction. Machine learning tools analyze customer data and find insights and patterns. AI-assisted tools precisely identify customer segments, a complex exercise to perform manually or with conventional analytical methods.

#15 Workstream Balancing for Agents

A study called AI at Work was conducted by Oracle and Future Workplace, which stated that 50% of workers are already using some form of AI at work compared to only 32% in 2018. Today more and more insurance agents are finding it convenient to use AI-assisted models that help them gain access to customers and empower them to expand their markets. With simplicity as the defining factor, AI is sure to be the mainstay for building and enhancing customer satisfaction and, in turn, growing insurance agents' expanse.

#16 Self-Servicing For Policy Management

Self-service business intelligence (BI) is a data analytics tool that helps users who do not have a background in BI, data mining, or statistical analysis to access, analyze and explore data sets. Self-service BI tools filter, sort, analyze and visualize data without involving an organization's BI and IT teams. Such tools make it easier for employees to get valuable business insights from the data collected in BI systems. This approach ultimately drives more informed decision-making, resulting in higher profits, improved efficiency, and enhanced customer satisfaction.

#17 Claim Volume Forecasting

The essential gamut of an insurance practice is to set the premium at the beginning of the insurance contract. To arrive at a precise premium for next year in an insurance company, a precise and reliable estimate of the number of claims occurrences and the total claim amounts is extremely important. Machine learning significantly improves the speed and accuracy of the forecast for individual claims. This has a positive impact on the efficiency of an insurer's pricing.

For instance, claims that are more likely to be large and with more uncertainty in outcome can be given more attention, and claims that are more likely to be smaller and more certain in outcome can be settled faster. While improving business performance, such tools also enhance customers' experience.

The combined power of machine learning, advanced analytics, and IoT in insurance enables insurers to reach prospective clients, study their real-time needs, develop insights from their profile on risk magnitude and create custom bespoke solutions. Once applied in its entirety, AI will truly transform the insurance landscape, which will be faster, convenient, and future-proof for the companies and the customers.

Going ahead, AI tools and intelligent assistants will become commonplace across the insurance company's technology stack, enabling professionals to make more informed decisions in managing risk across the business.

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