July 21, 2021 | Written by: Debashish Hota
Categorized: IT infrastructure
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The Insurance sector is today growing, and technology plays an important role in their rapid growth. Today the Insurance providers are looking to enhance their reach to the customers, optimize processes, customer experience and above all get accurate risk assessments using the massive volumes of data that are gathered. This change in behavior has given rise to a new term – Insurtech.
Insurance businesses assess risk by using rule engines. This practice has been going on for decades. But with the shift in technology and Neural networks and Machine learning taking the forefront, Insurance companies have started investing in modernizing and evolving claims and processing platforms. The most crucial function in this process is Risk Underwriting.
The two major factors which are propelling the need for a sophisticated Underwriting system is the Rise of Data and Higher Customer Expectations. 80% of the data received today by Insurance Underwriters is unstructured, residing in the form of emails, PDFs, forms, and images. To extract useful information from this set of unstructured data and then apply it back to the underwriting rule engine is a huge task. When performed manually, this increases the processing time as well as increases the risk of human error. This legacy process does not even account for 3rd party data like environmental, social, digital, location, health devices etc.
Insurance customers are now looking to reduce their turnaround time while responding to customers. With the advent of omni-channel communication, the customers are unwilling to wait and are more likely to apply for an insurance somewhere else (multiple applications at multiple insurance companies is also common). To ensure that Insurance companies have the attention of their customers and to personalize their offerings for each customer segment, they have had to make technology the focus of their business strategy.
Machine Learning has now made it easier than ever to analyze all the data available. Solutions from our partners include a customizable platform to fast-track on-boarding, underwriting, risk analysis, prediction, health profiling, claims, fraud detection, customer profiling through KYC (know your customer) and complete data intelligent platform to reduce the time and costs of processing data while enhancing the overall customer experience. They analyze both historical and current data. Historical data includes individual and family health history, financial history, and the customer’s interactions with the web. Social Media accounts are used to analyze public data while fitness apps and financial transactions (current income trends, spends and investments) are captured for real time analysis.
The output expected out of a Risk Underwriting solution is to provide deep insights on the financial status of the individual to predict the insurance premium value by calculating an overall health index (in case of health insurance) and financial index (in case of others). Tracking assets and liabilities of the individual over a period and their interactions with the web and social media are used to customize and personalize the insurance products thus helping in cross sell and up sell. Also, as an output, the lifetime value of the individual for the insurance company is predicted.
Benchmarks of some sophisticated Underwriting solutions show benefits of over 50% in efficiency of manpower utilization, 90% reduction in turn around time, 80% better fraud identification, 70% less cost of policy issuance, 60% better customer engagement and 40% better risk delinquencies identification and intelligence in near real time.
The solution mentioned above is deployed on IBM Power 9 IC922/LC922 servers with RedHat Enterprise Linux (RHEL).
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