How data and analytics are “ultimately helping insurers make better, faster decisions”

Newly appointed chief analytics officer reveals the link between digital transformation and improved risk prediction

How data and analytics are “ultimately helping insurers make better, faster decisions”


By Alicja Grzadkowska

The use of data and analytics in insurance is bringing big changes to the industry. While insurance has always been driven by data, the sophistication of today’s analytical and machine-learning models is allowing insurance companies to begin reaping the rewards of the big data evolution, according to Insurance Nexus. For one, this power couple is changing risk exposure assessment and claims operations, one expert told Insurance Business, though that’s not the only upside of data and analytics.

“This digital transformation has enabled the insurance industry to form a better understanding of customer expectations, and design more tailored products and services for customers. The industry can now use these insights to improve and better inform risk exposure management, friction-free claims experience, and accelerate underwriting operations,” said Govind Balu (pictured), AXIS Capital’s newly appointed chief analytics officer, who was most recently chief data and analytics officer for Allstate Roadside Services.

The specific uses of digital and data-focused solutions that have proven to be especially useful to insurance firms have a common theme, in that they are putting better tools and insights into the hands of industry professionals and, in turn, helping them make better decisions, according to Balu.

“For example, machine learning (ML) and artificial intelligence (AI) techniques are increasing automation, and accelerating underwriting and claims operations. These techniques help better understand risk exposures, can quickly convert various unstructured data inputs, such as emails, policy documents, and claims records, into structured and digital formats, so that underwriters and claims teams can make decisions in real-time, looking at submission materials in a sensible order,” he explained. “This allows more time for professionals to do the jobs in which they are highly skilled, rather than using their time to wade through information.”

Analytics also come in handy when building pricing algorithms, which means quotes can be provided in real-time, thereby making the insurance-buying process that much more efficient for brokers and their clients. Risk mitigation is top of mind as well for insurance companies, and can likewise be addressed by analytics tools.

“Insurance is about providing better risk management solutions to our customer while effectively managing the company exposure management, comprising a mixture of appropriate products/services and risk mitigation strategies,” said Balu. “From identifying the root of a problem (diagnostic analytics) to anticipating the problem (predictive analytics) to identifying solutions (prescriptive analytics), analytics tools are ultimately helping insurers make better, faster decisions. For example, if you can predict a risk on a product being offered to a customer, then you can prescribe the solutions to mitigate that risk. This allows for risk prediction and price optimisation.”

In his new role at AXIS, Balu is initially focusing on using analytics to better understand clients’ needs, which will help the company provide more tailored products and services.

“We view data and analytics as helping us put better tools and information in the hands of our people – which ultimately bring our clients more value from quote to policy to claims,” he said.

Looking ahead, the future of data and analytics in insurance is bright, with Balu predicting that the industry will continue to embrace digital innovations and new technologies in underwriting as well as in business interactions with customers.

“I see the industry continuing to build out algorithms to enhance underwriting operations and risk assessments. I also see the industry increasing the use of natural language processing (NLP) when quoting clients. Finally, I see data becoming more democratised in that our people will have better data at their fingertips, which will help them make better decisions and ultimately bring more value to their clients,” said Balu.

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