Back in 2014, McKinsey & Company, a global management consulting firm, published an article about what insurers need to do to unlock the value of analytics in their businesses. The article outlined that insurers traditionally employed actuaries who used advanced math and financial theory to analyze and understand risks. At the time, McKinsey and Co explained that developments in computing technology, and the explosion of new digital data sources, had expanded the scope of what insurers could do; allowing them to transcend the boundaries of traditional actuarial science.
Fast forward to 2021 and the game has transformed even more. Given the sheer magnitude of data that insurers have to deal with today, it is impossible to even imagine that an actuary could analyze and make sense of all of this information. Luckily, computing capabilities and analytics platforms have advanced to such an extent that insurers can use the data they have collected and stored over the years to deliver real business value.
Modern insurers are seeing major productivity gains thanks to integrated analytics. These solutions enable them to bring data from various different sources together, and put that data in context so that it can be used to boost sales, improve underwriting, aid claims and streamline customer service. And because insurers have access to data from a wider variety of sources, they can leverage predictive analytics to accurately forecast evolving market needs, mitigate risks in advance, better understand their customers and even predict customer behavior.
Below, we outline five ways that data analytics can help insurers reimagine and improve their core business processes.
Today, unstructured data from the web – like social media data – is a lead generation gold mine for insurers. By analyzing this information, insurers can unlock new insights about customer behavior and tap into opportunities to up-sell and cross-sell. In addition to this, insurance businesses can leverage predictive analytics to find out more about a specific demographic and then target their marketing efforts accordingly.
Before predictive analytics, insurers could only estimate what their customers wanted or guess what they liked and disliked. But with the right data, and the right strategy, insurers can analyze every interaction they have with their customers in an effort to identify any sticking points and create frictionless experiences tailored to meet their customers’ evolving wants and needs. This boosts customer loyalty.
The Coalition of Insurance Fraud estimates that as much as $80 billion is lost annually from fraudulent claims in the US alone. While insurers traditionally relied on statistical models to detect fraud, they can now use predictive analytics to spot any dodgy activities and prevent fraud before it happens.
Underwriting is a complicated task that can be simplified using data analytics. Algorithms automate and enable more accurate risk modeling. When your underwriting process is faster, you can issue policies faster. For example, analytics tools improve car insurance underwriting because these systems combine a range of data to assess an individual’s driving behavior and then offer a personalized rate and coverage based on how well or poorly the person drives.
According to Business Insider, 55% of tech-savvy and 43% of non tech-savvy insurance customers would be motivated to stay with an insurer because they were offered personalized products and services. Similarly, a Salesforce survey found that customers don’t want to feel like a number, they want to feel like they are being offered something that is customized to their specific requirements. Today, insurers can build personalized plans for prospective and existing customers, making it possible to create a unique profile for, and offer personalized premiums to, each buyer.
One of the biggest challenges related to data analytics is collecting and storing data. This requires tools that can gather data from multiple sources, store structured and unstructured data and deliver it in a usable format to drive analytics. Without the knowledge and tools necessary to manipulate data, insurers will not be able to make sense of their data and optimize analytics.
Synatic’s Hybrid Integration Platform is a Nimble, Simple, and Powerful data integration tool that enables businesses to collect, organize, and operationalize data to help them capitalize on the benefits of data analytics and reach their business goals. And beyond Analytics, Synatic can integrate key data sets between solutions whilst achieving your ETL and Warehousing needs. All within one platform!
If you want to learn more about how Synatic can help you turn your data into actionable insights you can use to identify new markets, improve internal insurance processes, and better serve your clients? Contact Synatic today!