DataRobot > Case Studies > Pricing Analysis with DataRobot at NTUC Income

Pricing Analysis with DataRobot at NTUC Income

Customer Company Size

Large Corporate

Region

Asia

Country

Singapore

Product

DataRobot

Tech Stack

Automated Machine Learning

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Productivity Improvements

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Functions

Business Operation

Use Cases

Demand Planning & Forecasting
Predictive Replenishment

Services

Data Science Services

About The Customer

NTUC Income is a top composite insurer in Singapore, serving over two million customers with 3.7 million policies. It is part of the National Trades Union Congress, the sole national trade union center in Singapore comprised of 58 trade unions and 10 social enterprises. These social enterprises were established by the government to help stabilize the price of commodities and services, strengthen the purchasing power of workers, and to promote better labor-management relations. NTUC Income is the only insurance cooperative in Singapore, providing life, health, and general insurance products to over two million customers across the country. It is both the top composite insurer in Singapore, as well as the largest automobile insurer.

The Challenge

NTUC Income, a top composite insurer in Singapore, was facing rising claims costs across the insurance industry. As the cost of doing business increased, the company needed to understand the factors driving up claims costs, who was affected, and what actions to take. Furthermore, with insurance increasingly becoming a commodity, accurate price setting became more critical than ever. However, pricing analysis in insurance can be complex, repetitive, and time-consuming. The traditional method of using Generalized Linear Models (GLMs) for pricing analysis was not ideal due to several limitations. These included assumptions of a straight-line relationship between a rating factor and claim costs, time-consuming processes, and inability to analyze text in claim descriptions. The company needed a solution that could address their pricing analysis challenges and scale with their team.

The Solution

NTUC Income turned to DataRobot, an automated machine learning platform, to undertake a massive pricing analysis project. The platform was used to identify changes in exposure, claim frequency, and the severity and nature of claims. DataRobot's Feature Impact and Word Cloud capabilities were used to identify patterns in claim frequency and visualize how claim descriptions were changing over time. The insights gained from the data allowed the actuaries at NTUC to set more accurate technical pricing. They could get a better estimate of the risk premium, identify mispriced exposures, and account for future inflation. The final step was to transition from technical pricing to commercial pricing. Using DataRobot, actuaries could analyze a sample of competitor quotes and generalize out to see what competitors would price for different types of policies. This allowed them to balance profit margin vs. volume and find a practical premium rate that customers would pay.

Operational Impact

DataRobot automated and expedited many of the manual, repetitive tasks that actuaries had to undertake.
The platform helped with data manipulation and simplifying complexity.
The insights uncovered by DataRobot could be easily communicated to other business owners, allowing for corresponding actions and positive change to be enacted quickly.