Predictive analytics is key to insurance sector competitiveness

The insurance sector is at a crossroads. Those organisations that embrace predictive analytics to forecast behaviour will enjoy a significant competitive advantage over those that move slowly

24 July 2018

Access to data is essential to the success of insurance companies—data and analytics play a critical role in sales and distribution, fraud detection and prevention, and underwriting and claims management. They also provide insurance companies with valuable customer insights.

One particular group of techniques—predictive analytics, which uses data and statistical algorithms to analyse historical data to forecast behaviour—continues to disrupt the insurance sector.

The industry is accustomed to handling and interpreting huge amounts of data, but it is also known to be risk-averse, as insurance companies face many uncertainties, such as the nature of forecasting risk, regulatory environments, technology changes, and economic volatility. These factors mean insurers require strong confidence before adopting new technologies for predictive analytics.

Predictive analytics should form part of a secure, scalable, and well-governed data solution that helps insurers connect to their complex and rapidly-changing data, integrate with leading statistical and programming languages, and help them transform these insights into real-world business value.

But first, what do we mean by ‘predictive analytics’?

What is predictive analytics?

Predictive analytics is a subcategory of data science which uses historical data to create and train machine learning models, such as regressions or clustering, and uses those models to forecast future outcomes with new data. Predictive analytics is critical in appraising and controlling risk in underwriting, pricing, rating, claims, marketing, and reserving in the insurance industry.

It also provides additional capability to existing data products to arm insurers with even more information to develop new opportunities while proactively reducing risks and losses.

How insurers can support their customers with predictive analytics

Predictive analytics helps insurance companies understand and model the future behaviour of their customers, enabling them to create tailored financial products that are relevant and appealing to them, while also being competitive in the marketplace.

They can also map trends to keep the organisation ahead of the market when predicting risk, as its analysts can better understand customer preferences, lifestyle, call centre interactions, and other important traits to better inform product offerings.

How the insurance sector uses predictive analytics to combat fraud

According to the Coalition Against Insurance Fraud, at least $80bn is lost yearly due to insurance fraud in the US alone, making identifying fraud a high priority for insurance companies when processing claims. Predictive analytics are used in fraud detection, so insurers can spot inconsistencies and avoid making large payouts for fraudulent claims. As fraud attempts become more complex, insurers need the ability to create increasingly intricate, predictive analytics models that can access federated and governed data sources and easily be deployed through an organisation from one analytics hub.

The analytical future for insurance

Given the role data plays in business performance and fraud prevention, insurance companies require powerful analytics platforms with easy-to-use visualisation tools and real-time reporting across multiple devices to enable their data experts to derive key insights and make informed business decisions.

Technology aside, to capitalise fully on the potential of predictive analytics, insurance companies need to foster a data-led culture throughout the organisation, not solely in the data team. From the boardroom to the call centre, insurance professionals assess the macro and micro data they need to effectively combat fraud and ultimately improve the company’s overall business performance.

Predictive analytics will continue to play a key role going forward, so failure to adopt the technology could prove a costly oversight.

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