Nowadays, data is the most valuable asset. In fact; data quality and how we process data often determine the game. While the ability to make data-driven decisions is of great value for all industries, it has and always will have a profound impact on the insurance industry that was built on a foundation of statistics. So can the insurance industry turn big data into insights and ultimately into smart decisions?

Looking for patterns in large volumes of data is nothing new to the insurance sector. However, for this data-heavy industry, the biggest challenge has been making sense of millions of unstructured and fluctuant data. This is where machine learning comes to the rescue! With qualified data and useful insights, it can enable insurance companies to evolve into better service providers that operate with greater efficiency.

Here are 3 ways how machine learning is transforming the insurance sector:

Automated processes will have a significant impact on the insurance industry as it would require fewer resources to offer personalized products to the customers. With machine learning, insurers are able to analyze huge chunk of data and gain more granular and precise understanding of customer behavior. Such insights enable them to segment their customers. A deeper understanding and clearer segmentation of customers allows insurers to drop standard product offerings with general features and pricings. Instead, they can offer personalized products and solutions based on the specific needs of narrower segments. Such advanced personalization cannot be easily replicated, therefore it gives a great competitive advantage when we see insurers are still lagging considerably behind in using insights from new data sources. Only 36% of executives in insurance sector reports that their companies can utilize insights from new data sources.

Moreover, insurers can also benefit from understanding the current patterns of customer behaviors and act on them even before they occur — without the need of a crystal ball! In addition to being aware of problems before they get more complicated and provide instant solutions; insurers can also easily recognize upselling and cross-selling opportunities.

Machine learning delivers more accurate predictions than us humans. Using machine learning, insurance companies can make confident predictions on coverage changes, potential losses for policies and and manage the risks more effectively. Coupled with broad spectrum of data sources such as social media channels, websites, third party sources, telematics and IoT devices integrating with customer view; advanced machine learning algorithms can improve risk management.

Insurers can also leverage deep learning algorithms to increase profitability. Especially with regard to risk management, the opportunities mostly lie in the insurance sector. For example deep learning algorithms can be applied for automated classification of drivers into various risk groups. With the help of telematics (in-vehicle telecommunication devices), automobiles are now able to transmit drivers’ behavior and vehicle usage data to the insurance companies. As a result, auto insurance companies can calculate drivers’ risks and offer usage-based insurance to align driving behavior with premium rates. This will also benefit low-risk drivers as well as the insurance companies since they will pay reduced premiums.

Insurers can also assess customer lifetime value (CLV) to calculate the customer’s profitability for their insurance company. Machine learning algorithms that process customer data can be applied to forecast the likelihood of customer churns and future claims.

Fraud is a growing problem in the insurance sector costing billions of dollars to the industry. In the United States, fraud steals $80 billion a year across all lines of insurance. The good news is that machine learning algorithms can easily eliminate human errors and detect unnoticed fraud patterns by identifying exceptions. And they can also help insurance companies to alert suspicious claims that require deeper investigation.

Usually, insurance companies rely on predictive models that use the previous cases of fraudulent activities. If the variables in claims match with the previous fraud cases, then those claims are pinned for further investigation. In addition, complex metrics such as subtle behavior patterns of the person who makes the claim and partner agencies involved can be included. It would further increase the capacity of statistical models to identify fraud schemes. It is also crucial to constantly feed the machine learning algorithms with new data in order to have much more accurate fraud detection.

Since emerging technologies are drastically transforming all businesses, the insurance sector doesn’t have the luxury to adopt a “wait and see” attitude. Coming to dethrone the traditional methods of doing business, machine learning offers the truly transformative potential to the insurance industry. It promises to enable insurance companies to optimize their products in an automated manner, constantly improve risk assessments, and become less prone to errors and fraud.