Predictive analytics model for the insurance underwriting process

Abstract

Loan is one of the main products offering by any Bank or financial institution. Generally, Vehicle loan is the important loan requested by customers. These institutes strategize in order to attract customers to this loan offerings and encourage them to take this loan. Once the customer has opted for this loan, we can’t predict his behavior post that. Huge losses can occur to these banks and financial institutions of these customers default. This led to tightening of process of loan approval i.e. underwriting process. Larger number of loans are being rejected. Therefore, this brings in the requirement for better credit risk management for these banks and financial institutions to not loose good customers. In underwriting process, we decide if the application can be accepted based on risk assessment done using customer related information available in application. In general, 90% of the loan approval time is spent on the processing of application. It also huge amount of data which is complex in nature and difficult to process manually. Real time data can’t be accessed most of the time while processing manually. Automation of this process can solve this issue of huge complex data. Risk can be managed in a better way and losses can be avoided in real-time. With this automation, bank and financial institutes can shift their focus from dealing with data to strategic decisions and portfolio analysis. Overall, this will lead to less processing time and error rate to manage the operations efficiently in handling this huge complex data. Underwriting process is accessing the risk of the loan using the information like customer details, loan requirement details and credit history details. By using the above-mentioned details, automation of the underwriting process is done