How SBI Life Insurance’s customer self-service portal increased by 124% since last year

Across industries, the Covid-19 pandemic led to an increased need to enhance the IT systems, in order to enhance customer experience and operational efficiency.

Similarly, SBI Life has taken various initiatives on the digital front. For instance, the KYC process was made seamless with minimal documentation by providing additional options like Central KYC and Video-based KYC. Optical Character Recognition (OCR) has also been introduced which helps in identifying KYC documents, reads the data from the documents and validates data with proposal form data. Finally, we have facilitated real time tracking of cases and status availability, which eliminates the need for person dependent processing.

Further, SBI Life Insurance’s existing customer application – ‘Easy Access’ application – has been enhanced with more features and ease of use. Customers can now place requests for Partial Withdrawal, Free Look Cancellation or Name Correction. These requests are authenticated using OTP and Geo Tagging of Location based verification. To make the process of buying a policy simpler, API integration with platforms like NSDL, UIDAI, Credit Bureaus, Insurance Information Bureau of India (IIBI) has been done.

“So if a customer has his data available on these platforms, then we do not ask him to provide details like KYC, Aadhaar, PAN and financial documents, among others. We thus have the capability to issue paperless, pen-less policies to certain segments of customers,” said Anand Pejawar, President-Operations, IT & International Business, SBI Life Insurance.

With the help of all these innovations, in FY21, the Digital Transactions on SBI Life Insurance’s customer self-service portal increased by a staggering 124% on a year-on-year basis, according to Pejawar. An in-house app called M Connect Life application has also been developed to aid the company’s sales staff and employees.

“The app assists the company’s sales force in the digital on-boarding process of customers. It has not only reduced the turnaround time (TAT) for policy issuance from 7 days to 2 days, but also aids in the reduction in mis-selling and an increase in Data Accuracy. It also helps the agents stay up-to-date with the latest communication and commitment provided to the customer, with all information available at their fingertips,” he added.

Using Big Data Analytics

Undergoing digital transformation involves a complete relook of all systems & processes and seeing how user experience can be improved. That’s the journey SBI Life has been on. The company uses big data analytics in all the milestones of a policy journey. It is used in onboarding, for claims, for renewals and for revivals.

“Analytics has helped us in enhancing several processes, including quicker underwriting decisions, getting better understanding of the customer, focused renewal calling and cross-selling of products,” said Pejawar. The insurance provider has an analytics based tool called ‘Profile Score Underwriting’ that provides relaxed financial underwriting including reduced documentation for proposals identified with a good profile score.

“Introduction of this score has improved the auto underwriting percentage. Today, almost 10% proposals are being accepted based on the available profile score information, without insisting on additional evidence,” he added. SBI Life Insurance has a huge repository of data pertaining to the 20 years of its operation. The company has issues of scale and also heterogeneity of data. One of the major challenges of having such a vast and varied data set is to find ways to analyze the data and put it to use in serving customers and partners in the best way possible. To ensure data consistency and integrity, certain checks and balances have been put within the processes.

For example, there used to be an internal process which used to uniquely identify customers and each policy to the correct customer. As the business grew and the volume of data also increased, the logic and the assumptions which were in place to identify a customer became weak. This resulted in multiple IDs for the same customer under different policy numbers.

“A couple of years back, we undertook a project to revise the customer identification logic. We scanned the millions of policies in our database using specialized tools to fix erroneous IDs and thus purified the customer database,” Pejawar maintained.