Big Data and Analytics Help Insurance Companies In Their Day-to-Day Operations
Wed, April 21, 2021

Big Data and Analytics Help Insurance Companies In Their Day-to-Day Operations

Keith Stonell of Information Age, a website dedicated to CTOs and technology leaders, explained that several years of rapid investment in data and data analytics have helped transform the insurance industry. Data analysis has been one of the industry’s historical pillars / Photo by: everythingpossible via 123RF

 

Keith Stonell of Information Age, a website dedicated to CTOs and technology leaders, explained that several years of rapid investment in data and data analytics have helped transform the insurance industry. Data analysis has been one of the industry’s historical pillars. In fact, actuaries have utilized mathematical models to predict property loss and damage for centuries. Insurers gather large data sets about their customers, which are updated when they make a claim whenever insurers sell policies.

But insurers have wanted to become more relevant to their customers and be more efficient in recent years. They have started to realize the strategic value of their data investments. Currently, insurers want to leverage data analytics to enhance customer experience while reducing claims handling time and costs, preventing fraud, and more.

 

Statistics on the Insurance Industry

Insurance company Swiss Re found that world insurance premiums rose 1.5% in 2018, and adjusted for inflation, it amounted to $5.2 trillion, per the company’s 2018 world insurance study, which was based on direct premium data from 147 countries with detailed information on the largest 88 market, cited business association Insurance Information Institute (IIS). The said percentage was more than the 1.2% growth recorded during the period 2008 to 2017.

Moreover, non-life premiums grew 3% in 2018—taking inflation into account —and it was faster than the 2.2% from 2008 to 2017. Meanwhile, life insurance premiums grew 0.2% in 2018, a 0.6% reduction in 2008 to 2017, adjusted for inflation.

Insurance company Swiss Re found that world insurance premiums rose 1.5% in 2018, and adjusted for inflation, it amounted to $5.2 trillion, per the company’s 2018 world insurance study / Photo by: Phonlawat Chaicheevinlikit via 123RF

 

In the United States, the property and casualty (P&C) sector, the world’s largest insurance market, saw an exponential increase in net income (66%), amounting to $60 billion, reported Sam Friedman and colleagues of Deloitte, a multinational professional services network. This is due to a 10.8% boost in net premiums written and “nearly breaking even on underwriting.” Apparently, underwriting lost $23.3 billion a year prior.

US insurer results deteriorated in the first six months of 2019, with the insurance industry having an underwriting gain of $5.4 billion, a sharp decrease from $6.1 billion for the same period in 2018. It also had a profitable combined ratio of 97.3, up from 96.2.

How Big Data and Data Analytics Reshape the Insurance Industry

Streamlining Processes

Manually identifying troublesome claims can be challenging for insurers. And it is even more challenging for them to formulate strategies to mitigate the risk as soon as it is identified. But nowadays, information needs to be transmitted in a timely fashion or in real-time to the adjuster’s natural workflow, possibly along with an alert to the supervisor or large loss unit.

The information should explain the attributes supporting the risk level and suggest a work plan or solution for the adjuster. This process should be done repeatedly in real-time as underlying data changes to the claim file, especially for long-tail lines like bodily injury.

There are insurers using smarter predictive analytics to fast-track claims and processes with little to no human intervention. For example, We Predict leverages predictive analytics to help vehicle manufacturers and suppliers manage the “frequency and cost of malfunctions for vehicles under warranty.”

On the other hand, big data can also be used in pricing and underwriting processes, said Kelly Preston, data analytics manager at SilverBridge, via iWeb, a business technology media company. She added, “More modern systems ensure that vast amounts of data on claims are processed faster than ever to accelerate the process and potentially reduce premiums, thanks to more effective pricing models.”

Understanding New Risks

For insurers, actuarial science has limits with predicting new categories of 21st-century risks such as cyber, food safety, or complex supply chain disruption. Aiding businesses and individuals in managing these risks offer “huge potential” rewards for the insurance industry. As of this writing, global cyber premiums are increasing at 30% each year with less than 15% penetration in the US and less than 1% penetration globally.

Insurers are not equipped with the necessary tools to understand or price the abovementioned risks accurately. These new insurable risks have varying patterns and connections ranging from vehicles to property. For example, cyber risks are interconnected with not just the same infrastructure but also by patterns of human error, software usage, and network connectivity.

To try and mitigate these risks, insurers must embrace new technologies such as internet-scale “data listening” that analyzes, cleanses, and updates volumes of data to develop risk models with regard to cyber risks, for example. The data might also include public and propriety sources about connected devices, network presence, control systems, and semantic content (Ex: data breaches and job boards).

Fortunately, the insurance industry is starting to employ machine learning, natural language processing, and other modeling techniques to their core and third-party data to support operation and risk analytics. This includes underwriting tools for evaluating and pricing risks through the scores used and significantly improving operational decisions in service and claims after those risks are insured.  

For insurers, actuarial science has limits with predicting new categories of 21st-century risks such as cyber, food safety, or complex supply chain disruption. Aiding businesses and individuals in managing these risks offer “huge potential” rewards for the insurance industry / Photo by: kzenon via 123RF

 

Being More Data-Driven

Insurers are using external data sources and adding more information about a claimant or injured party. Some examples include identity verification or social media data. However, there are barriers to adding external data points. Insurers can incorporate machine learning into how data is aggregated to help insurers become more “data-led and driven businesses.”  

Being Customer-Oriented

IBM found that customer analytics drives big data initiatives at insurers, Preston mentioned. This is consistent with the pressure insurers face when shifting from being product-centric to becoming customer-centric organizations. This way, data insights, operations, technology, and systems revolve around the customer.

Most executives understand that big data results in “real business advantages” to enable the collection and analysis of external data sources. If combined with existing internal data—including human insights with a more effective and real-time data analysis—the insurer can become more innovative when developing customized and segmented service offerings, Preston explained.

“This means claims are not only processed faster but more accurately too, as well as with fewer false positives,” said Preston. Not only does this lead to the reduction of fraudulent cases, but customers will also be happier because of the smoother process, helping the business grow due to increased loyalty.”

Big data and analytics help streamline processes in the insurance industry, making customers more satisfied with the insurance firm’s services. Big data also aid in assessing risks, allowing insurers to develop risk models to mitigate threats. Not only will analytics make insurance companies become more data-driven but, more importantly, also customer-oriented.