Skip to main content
HomeBlogData Science

Data Science in Insurance Today

The insurance industry is rife with data and data science use-cases that provide value. In a recent webinar, Allianz Benelux Regional Chief Data & Analytics Officer Sudaman T M explored the state of data science in the industry today.
Dec 2021  · 4 min read

Today, high-performing insurance organizations are leveraging data science to advance their businesses in a competitive environment. In a recent webinar, Sudaman Thoppan Mohanchandralal, Regional Chief Data and Analytics Officer at Allianz Benelux discussed the current state of data science in insurance and exciting use-cases of machine learning in the industry.

Why Data Science is Valuable in the Insurance Industry Today

Recently, the industry’s demand has shifted to more personalized services rather than those that appeal to mass markets. This McKinsey report describes this shift Sudaman refers to. It explains how deep one-to-one relationships can be built with consumers at scale through strategic personalized marketing and service offerings.

These changes in needs and relationships are made possible by the massive growth of available data for individual customers as well as advances in machine learning and resources. In this article, IBM discusses how insurers observe a 22-25% improvement in time to profitability when creating personalized services relative to mass-appealing products. These products can only be developed at scale using machine learning.

The state of data science in insurance

Sudaman explains in the webinar that machine learning provides value when it is leveraged for predictions rather than causal inference and when the problem is sufficiently self-contained. As of today, machine learning fails when organizations attempt to use these algorithms as a crystal ball or black box rather than a tool to inform humans on directions to explore. Here’s a glimpse of some of the use-cases covered in the webinar:

1. Fraud Detection in insurance Claims

Machine learning performs very well on prediction tasks with a lot of data. Therefore, fraud detection is a great use case to train and deploy a classification algorithm such as logistic regression or a decision tree. This system can be used to flag claims that look suspicious. This makes fraud prevention significantly more manageable for an analyst as it will augment their workflow by surfacing the claims most likely to be fraudulent.

2. Identifying Customers for Retention Activities

Another important use case for improving the scale of any organization is improving customer retention. Machine learning can help to identify which customers are at risk of leaving your organization. Identifying these customers at scale and providing incentives for them to stay will lead to better customer lifetime value in the long run—enabling deeper personalization.

3. Optimizing Pricing with Time Series Data (Risk Premium Modeling)

Understanding a customer’s risk to file a claim is critical in determining the correct price and structure of a plan to provide them. All insurers are doing this at some level. With the expansion of available data, model accuracy to determine a customer’s risk profile can be improved to offer more competitive pricing to individuals.

4. Future Disease Prediction

Similar to the previous use case, understanding future disease risks is critical in portfolio optimization and pricing. discusses how the insurance industry will have to pay out upwards of $100 billion in COVID-related claims. While predicting the pandemic in advance would have been challenging, understanding and predicting health outcomes will enable insurers to prioritize and personalize their services.

5. Portfolio Optimization

The final use case we will discuss is portfolio optimization. Portfolio management consists of a few steps: identifying meaningful groupings of risk to be analyzed, informing optimal decisions, and identifying opportunities. Each of these subtasks is solvable through data science optimization techniques.

If you want to learn more about the future of data science in insurance, make sure to tune in to the webinar here.


Top 10 Data Science Tools To Use in 2024

The essential data science tools for beginners and data practitioners to efficiently ingest, process, analyze, visualize, and model the data.
Abid Ali Awan's photo

Abid Ali Awan

9 min

Google Cloud for Data Scientists: Harnessing Cloud Resources for Data Analysis

How can using Google Cloud make data analysis easier? We explore examples of companies that have already experienced all the benefits.
Oleh Maksymovych's photo

Oleh Maksymovych

9 min

A Guide to Docker Certification: Exploring The Docker Certified Associate (DCA) Exam

Unlock your potential in Docker and data science with our comprehensive guide. Explore Docker certifications, learning paths, and practical tips.
Matt Crabtree's photo

Matt Crabtree

8 min

Bash & zsh Shell Terminal Basics Cheat Sheet

Improve your Bash & zsh Shell skills with the handy shortcuts featured in this convenient cheat sheet!
Richie Cotton's photo

Richie Cotton

6 min

Functional Programming vs Object-Oriented Programming in Data Analysis

Explore two of the most commonly used programming paradigms in data science: object-oriented programming and functional programming.
Amberle McKee's photo

Amberle McKee

15 min

A Comprehensive Introduction to Anomaly Detection

A tutorial on mastering the fundamentals of anomaly detection - the concepts, terminology, and code.
Bex Tuychiev's photo

Bex Tuychiev

14 min

See MoreSee More