HARNESSING THE POWER OF ADVANCED ANALYTICS: UNLOCKING CUSTOMER NEEDS AND PREFERENCES FOR INSURANCE COMPANIES

The funding in the Insurtech sector increased from $4.9 billion USD in 2018 to a whopping $16.7 billion USD in 2021 worldwide. The stagnant insurance industry is now changing; increased customer demands, disruptive technology, fast adaption rates, the advent of artificial intelligence, the significance of customer experience, availability of data, machine learning, and optimized data analytics techniques have collectively transformed the insurance business environment.
This era of disruptive technological advancement has increased customer power significantly. Customer satisfaction is the goal of any service provider. The insurance industry has also realized the immense potential of customer power and is working relentlessly to improve user journeys. The insurance industry is driving from reactive techniques to preventive measures. The sophisticated advanced analytics (AA) tools and techniques help minimize fraudulent activities, personalize plans and charge premiums, and provide realistic underwriting through machine learning. AA is a complete package tool that helps measure, predict, and optimize organizational performance. Ranjit Bose, the Regents’ Professor of Management Information Systems at the Anderson School of Management of the University of New Mexico, has put forward a three-fold foundational infrastructure proposal for implementing AA (Artificial Intelligence and Analytics) within any organization.
Foundational Infrastructure Proposal for AA Implementation in Insurance Industry
These AA tools should be integrated across the functional units of the insurance organization to deliver results. Insurance companies have access to various data points. However, the true value lies in using these data points to make effective decisions. Turning data into useful key insights is only possible by AA. Researchers have identified the following use cases of the implementation of big data in the insurance sector that improves customer satisfaction.

USE CASES OF ADVANCED ANALYTICS TO ENHANCE CUSTOMER SATISFACTION

CUSTOMER SEGMENTATION AND PERSONALIZATION

Customers of today have ephemeral brand loyalties. They are constantly on the lookout for new experiences and better products. Digitization has facilitated effortless accessibility and reduced switching costs for the customer. Previously, customers primarily compared insurance plans based solely on price, opting for the lowest option. However, the current trend indicates that customers now prioritize enhanced consumer journeys and better experiences. As a result, insurance companies have been compelled to transition from a product-centric mindset to a customer-centric approach. The art of understanding the needs of various customer segments and predicting their buying behavior lies in effective segmentation.
Advanced analytics unlocks limitless segmentation potential by handling 1000s of data sets simultaneously. Traditionally, limited data sets, time consumption, human constraints, and biases restrict segmentation. Machine learning algorithms and advanced visualization techniques process real-time data instantly and provide a picturesque representation of data which makes decision-making effective and efficient.
Segmentation identifies the right product for the right customer segment. Clustering techniques are widely used to classify customers. Some segmentation frameworks are coined below to understand the role of AA for customer satisfaction.

CUSTOMER SEGMENTATION FRAMEWORK USING RECENCY, FREQUENCY, AND MONETARY (RFM) AND CUSTOMER'S LIFETIME VALUE (LTV) MODELS FOR BANKING MARKETING STRATEGIES

A structured framework has recently been developed to apply Recency, Frequency, and Monetary (RFM) customer’s lifetime value (LTV) models. This framework utilizes customers’ demographic data to segment banking customers and create effective marketing strategies. The analysis study comprises two main phases: in the first phase, CRM data is employed to cluster the customers, and in the second phase, demographic data variables such as age, education, and occupation, selected through the SOM technique, are used to re-cluster the segments obtained from the first step. Both of these steps employ the K-Means clustering technique. To optimize the customer’s value, which is one of the study’s objectives, the comparison of customer value uses LTV instead of inter/intra cluster distances
Another researcher developed a comprehensive framework to segment customers, compute LTV for each segment, and project their future value. Employing K-means and two-step clustering algorithms, two levels of clustering were applied to a substantial dataset of customer transactions, encompassing deposit type, transaction date, balance before transaction, and transaction amount. The customer’s lifetime value was determined using the RFM model, renowned for its simplicity and effectiveness as a customer LTV approximation model. Additionally, the study utilized a time series method, the multiplicative seasonal ARIMA regression, to forecast future values for each segment
Another researcher developed a comprehensive framework to segment customers, compute LTV for each segment, and project their future value. Employing K-means and two-step clustering algorithms, two levels of clustering were applied to a substantial dataset of customer transactions, encompassing deposit type, transaction date, balance before transaction, and transaction amount. The customer’s lifetime value was determined using the RFM model, renowned for its simplicity and effectiveness as a customer LTV approximation model. Additionally, the study utilized a time series method, the multiplicative seasonal ARIMA regression, to forecast future values for each segment

CUSTOMER ATTRITION AND RETENTION

A lost customer is an opportunity lost. Customer acquisition costs (CACs) are much greater than customer retention costs. It is essential to look for factors that make customers lose trust in the brand. Advanced analytics predict trends and map pain points in customers’ journeys to determine reasons for leaving. It is estimated that 43.6% of customers leave due to poor service quality. An AI service bot can handle claims and assist immediately. Predictive analysis determines the customers’ lifetime value (CLV). This determines the profitability period of a customer. It also gives insights on when to promote or demote a certain offer. Lemonade Insurance uses AI technology to provide personalized plans to their customers and hence a lower churn rate than industry giants. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management held in Malaysia highlight an interesting use case of customer retention using advanced analytics.

UTILIZING CUSTOMER BEHAVIOR ANALYSIS FOR BANK CUSTOMER SEGMENTATION AND RETENTION STRATEGIES

A comprehensive analysis study was conducted to segment bank customers based on their behaviors, aiding the bank in devising retention strategies and attracting new customers. The dataset used for analysis integrates three tables: customers’ demographic data, transaction records, and bank card data. The study considers essential information and combines attributes such as transaction type, transaction frequency, service type, bank type, and channel type (ATM, Web, and Terminal) with other customer attributes. The author employed Artificial Neural Networks (ANN) to classify the factors based on their profitability.
T. L. Oshini Goonetilleke identifies the use of decision trees and neural networks to reduce churn rate in his works titled as Mining Life Insurance Data for Customer Attrition Analysis

ANALYZING CUSTOMER DATA IN LIFE INSURANCE COMPANY CRM TO PREVENT CHURN

This research focused on life insurance company CRM data aimed to analyze customer data and mitigate churn and attrition. The data is extracted from an operational database: the dataset covers life insurance policies with an average term of 18-20 years, necessitating the mining of a significant amount of historical data to build an efficient model. The study incorporated demographic data (gender, age, profession, etc.) as well as policy details like term, sum assured, premium, and agent information. Visualizations, Correlation-based Feature Selection (CFS), and Information Gain techniques were utilized to select relevant attributes or attribute combinations. The classifiers employed included the J48 decision tree and Artificial neural network model with a standard Multilayer Perceptron. Additionally, the study addressed challenges related to the dataset’s numerous attributes and the need for human intervention in various stages of analysis

CUSTOMER ONBOARDING AND ENGAGEMENT

In today’s digital age, customers expect simple language, easy procedures, hands-on applications, and immediate processing. No one wants to wait for days for a query to be answered or a lengthy mail document filled with industry jargon to sign for a policy change to implement. Insurance companies need to invest in easygoing yet state-of-the-art applications and faster processing. Digital Onboarding is the need for today. It is easy on the pocket and heavy on the heart. To drive engagement, insurance companies use channels such as video marketing, email marketing, and interactive marketing. For example, a personalized email message on a birthday can result in a lifetime customer a insurance policy.

CLAIMS MANAGEMENT

Claims Management is an integral part of insurance processes. An insurance company is responsible for managing thousands of claims every day. This is a hefty job that requires in-depth analysis and expertise. IoT and Data management capabilities have made this easy. Claims management software can process these claims and characterize them based on urgency, revenue, fraud, expertise, and legal assistance required. Advanced analytics and AI collectively can handle day-to-day claims efficiently. This saves money, resources, and time. Claims subrogation management can be optimized using the power of analytics. Complex mathematical models predict recovery trends, claims processing costs, and recovery potential based on historical patterns of liability trends, litigation data, and mitigation efforts. Simultaneously predictive and prescriptive analysis process claims information and assigns the best-suited adjuster for the job. This reduces inefficiencies in the system and increases customer satisfaction.
Framework for claims management using Advance Analytics

TELEMATICS AND USAGE BASED INSURANCE

Usage-based insurance has transformed the insurance industry’s outlook. Telematics is widely used in auto insurance where a black box is installed in the car which monitors driving habits. Additionally, data is gathered through police records, previous claims, vehicle maintenance, and challans issued. This helps insurance companies in determining the high-value vs high-risk customers. It also favors customers as they pay for what they are worth.
Soon telematics will begin to penetrate the home and life insurance. A customer that agrees to potential data tracking of health records via applications or smart trackers can save an amount on insurance. Similarly, a well-maintained house that has water sprinklers, an automated water shut-off device, and house security control installed will have a lower insurance fee and lower risk than a worn-out house.

RISK ASSESSMENT AND UNDERWRITING

Risk assessment is a lucrative task, if done wrong, it would cost millions. However, a human has limitations and can only process limited data. Advanced analytics and machine learning have made it easy to quantify and qualify hundreds of data points at once. Multiple risk parameters can be processed with claims data, to identify potential risk types and impacts. The predictive models help in identifying optimized risk scoring and expense ratio to write the correct underwriting policy. Analytics speeds up the entire underwriting process. Analytics influence all steps of underwriting. From initial data review to final policy issuance, the use of AA optimizes the process.
The insurance industry is growing at a CAGR of 12% and is expected to reach a market value of $9.8 trillion (about $30,000 per person in the US) by 2027 globally. The insurance market is submerged with new startups, out of which 53% are from the United States alone. This exponential growth in the insurance industry makes it challenging for companies to keep their competitive edge. Multiple options in the market and low switching costs have made customer retention and acquisition difficult. It is important to penetrate user minds and map their journeys to provide a better experience. The penetration of IoT & cloud computing technology has rewired consumers to look out for a one-stop solution. Nowadays customers don’t want a car insurance plan or a health insurance plan but a package deal. A car insurance plan that includes car maintenance and rewards for safe driving is much more appealing than a traditional car insurance plan. The insurance core products are losing their touch and non-insurance products such as financial planning or home maintenance plans are in demand. The future of the insurance industry is by preventive measures rather than reactive rewards. Customers require instant access to information and assistance digitally via mobile applications or websites. The use of an AI virtual bot to handle customer queries and claims will increase customer satisfaction and reduce costs
The future of the insurance industry belongs to the insurance companies that proactively integrate these technological advancements into their integrated systems, establish relevant partnerships, invest in employee skills, and implement effective change management across the entire company to leverage the full potential of these technologies. A leading insurance industry OP Financial installed a virtual chatbot that converses in the native language to its customers and reduces customer waiting time to zero. Together with Indian Farmers Fertiliser Cooperative Limited (IFFCO) and the Tokio Marine Group, IFFCO Tokio General Insurance Company Limited innovated claims handling by integrating an AI-based Claim Damage Assessment Tool that uses computer images and neural networks to identify vehicle damages.This reduces claim management costs by 30% and optimizes time from four hours to just fifteen minutes. Corebridge Financials have recently partnered with Blackstone and Blackrock (Innovative investment management solutions companies) to strengthen their business model.
These collaborations form the foundation of their innovative investment model, combining their expertise in asset allocation determination, asset and liability management profiling, and risk management with the expertise of world-class asset managers. Furthermore, by integrating BlackRock’s Aladdin platform, they are revolutionizing their investment platform with expanded analytics and accounting capabilities.The climate in the insurance industry is transforming as more companies look for innovative solutions. In this era of disruption, innovation leads to profitability. Big Data is a game changer for the Insurance industry. The future belongs to early adopters and innovators. It’s either innovate today or regret later.