Forecasting Business Persistency at HDFC Life: Smart Insights Powered by Data Analytics

Abstract

The life insurance industry is inherently a data-driven industry with various applications for analytical decision-making. Data science has influenced all business functions in an insurance organization to provide a distinct competitive advantage and push the industry towards the vision of ‘Insure Tech’.

This case revolves around one such application of analytics in HDFC analytics dealing with championing the initiative for forecasting and analysis of Business Persistency. The persistency ratio is actually a fairly simple, yet very important metric that provides a snapshot of the health of the insurance industry. Considering the importance of this parameter, it became extremely important for HDFC Life to understand the factors behind persistency numbers and what lies ahead for the organization.

The existing forecasting techniques were biased by the nature of work and did not give a significantly accurate and realistic number. The top management found this to be challenging for decision-making and decided that this required the intervention of the Business Insights department. Mr. Francis Rodrigues, SVP—Data Labs, Business Insights and Innovation was given the task to take over the pilot project and increase usage of analytical tools for Persistency Analysis.

While Quarter 1 results have been significant, Mr. Francis Rodrigues still wonders whether he captured all the internal and external measures to obtain effective results. Has he done enough and how many more areas can analytics be applied for in the insurance domain?

Research Question/Purpose: The current methods of persistency calculation are biased by the nature of work and deflect by a huge margin from the numbers actually achieved. There are no realistic forecasts for the coming year making the management uncertain with the decisions they take for strategic purposes.

Theory: Time-indexed collection data help in predicting persistency numbers which are also influenced by some important variables behind collection follow-up and categories of customers.

Type of the Case: Applied problem solving.

Protagonist: Not needed.

Options: Due to the availability of time-indexed data, time series was considered to be the best approach to forecast values into the future. However, that would only solve one half of the problem. It was also important to understand the parameters affecting persistency numbers, important variables behind persistency collections and classify the customers based on such variables. With this knowledge, classification models were also taken into consideration.

Discussions and Case Questions: Besides time-indexed collection data which other variables amongst policy and demographic parameters influence persistency parameters? How can insights be drawn about the factors that are actually affecting the persistency numbers? Will calculating and sharing monthly persistency numbers for the coming year improve the percentage of customers remitting payment on time? By classifying customers into various categories with regards to the frequency of follow-up required for premium collection can a favourable segment be carved out?