Data and analytics are very quickly becoming a fundamental business function within insurance. As policyholder demands increase, forward thinking insurance companies have begun to rely more heavily on predictive analytics to accurately forecast evolving needs and mitigate risk in advance. They’re also using big data service offerings to bring about operational efficiencies and reduce their expenses in key areas like underwriting and claims.
Despite the clear benefits of engagement with data and analytics, insurance companies have been laggards compared to their financial services counterparts, according to Amir Raskin, data and analytics product strategist at Sapiens International. But this is slowly starting to change, especially as more insurers see concrete results from investments in innovation and technology.
“Claims management is the first area where I expect data and analytics solutions to become standard,” Raskin told Insurance Business. “There’s an understanding in claims management today that the response to the customer is far away from optimum […] but with technology, positive changes can be made. On average, the general insurance company spends about a third of their total expenses on claims, and approximately half of that goes into managing these claims. It’s a huge manual effort [that can be optimized with] automation and machine learning.”
The real buzz term – the one that’s piqued the interest of insurance company boards worldwide – is ‘autonomous technology’. But the conversion from manual to autonomous doesn’t just happen overnight.
Raskin gave the example of autonomous cars. At the beginning of the 20th Century, car manufacturers developed the first automatic cars, removing the need for manual gear transmission. From automatic, the expectation 100-years-later was to jump to fully autonomous vehicles, but that hasn’t yet happened (at least not on a mass scale). Rather, today’s vehicles are supported by semi-autonomous tools, like assisted driving technology.
“In claims management, the next step is augmented decision making or augmented processes, and insurers are receptive to that,” said Raskin. “In claims, there’s a psychological issue that helps the [digital] transformation, which is that you can see the results almost immediately. By using artificial intelligence (AI) and machine learning [to assist with and speed up remediation], you can increase your claims capacity by X amount per day, so you become a better and more efficient team and can provide a superior customer service. It’s very hard for a claims manager to say ‘no’ to that.”
The good thing about insurance organizations, according to Raskin, is that they’re run by businesspeople. By that, he means that the industry understands money as an asset, and therefore can treat tools like AI and data and analytics as an asset. If big data service offerings bring about operational efficiencies and reduce their expenses by a significant percentage, most insurance organizations are willing to make the initial investment.
He commented: “Insurance companies understand money, which is a benefit. They’re not investing in innovation for the sake of innovation. We’re dealing with serious businesspeople. They’re not saying: ‘This is an interesting piece of technology – why don’t we try it?’ Rather, they’re saying: ‘Show me the money. Show me the major change and the impact on the bottom line.’ This concentration around [operational efficiency and increasing their margins] is starting to materialize in a data and analytics wave in insurance, which will grow very strong as soon as there are more samples and proven use cases.”