You may or may not be surprised but basic analytics is not new to the P&C insurance business. It has been an integral part of the insurance business since the 17th century when Edward Lloyd opened his coffee house in London. The coffee house became extremely popular as a place where shipowners and bankers gathered to hear the latest news on global shipping. Bankers used basic analytics to determine a fair price or premium to protect a shipowner from the loss of a ship and cargo on a global sea voyage. Basic analytics such as analyzing the risk based on data obtained from the shipowners was used to price the coverage, leading to the beginnings of the insurance business and the formation of one of the world’s leading specialist insurers, Lloyd’s of London.
At its core, predictive analytics is a decision-making tool that complements the actuarial approach used by the insurance business for centuries. Predictive analytics hit mainstream status when computer power and data availability exploded and the recent technologies became available to P&C insurers: artificial intelligence (AI), machine learning (ML), and predictive analytics.
Instead of manually calculating the risks associated with a sea voyage and setting a price to insure it based on shipowners’ experiences, predictive analytics evolved to the state where it now can predict outcomes – and more importantly – P&C insurers can proactively warn/prevent bad outcomes for insureds. It is a critical decision-making tool enabling insurers to look at risks in a new way, focusing on not only what happened and why, but more importantly, what might happen next.
Here's how predictive analytics works
Predictive analytics is amazing. It extracts insights from multiple data sources: structured, unstructured, internal, and external. It allows insurers to imagine or think about different decisions or approaches, evaluate the approaches, look for weaknesses and strengths, and then move on to make more and better predictions and decisions.
Computers using predictive analytical models comb through data, identify trends, help underwriters assess risks, and assist underwriters in risk assessment and pricing. Predictive analytical models also help insurers identify and generate new revenue opportunities by converting insights gained from customer interactions, telematics, and even social media into credible foresights, leading to new products and services.
Aside from the speed and efficiency with which predictive analytics can process and analyze data, it is helping insurers:
Streamline insurance processes – It helps automates basic functions such as underwriting, renewal/non-renewals operations, claims processing, and risk management services by enabling insurers to gain more accurate underwriting data and implement more efficient underwriting processes. It also helps insurers obtain better insights into customers’ preferences, thus enabling better informed business decisions, according to McKinsey and Company.
Detect fraud – the FBI reports that the cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. Using predictive analytical software, insurers can collect customer data and extract behavioral information that allows the computer to automatically flag fraudulent or high-risk activities. Once identified, the investigators are alerted, in real time, about questionable cases which can then be investigated.
Increase customer satisfaction – According to The State of Connected Customer report, 84% of customers say the experience a company provides customers is as important as its products or services. Today, it is all about the customer experience. It is moving from a transactional business to creating a customer journey that meets the customer wherever they are, at a desktop or smart phone. Now, with predictive analytics, insurers can implement customer experience initiatives throughout the customer journey from initial application to quote and bind and through the critical claims settlement processes, turning the customer experience into a digital one that customers say they want.
Identify trends and opportunities quickly - The power of predictive analytics empowers insurers to identify new revenue sources automatically to assess customer needs and develop the products and services customers want, with little human interference required. The Geneva Association reports that “these technologies allow for the development of powerful new business models which in turn enable the role of insurance to evolve from ‘understand and protect’ towards ‘predict and prevent. ”
Having evolved from basic analytics to today’s powerful predictive analytics, P&C insurers now have access to robust data platforms with the capabilities and applications needed to look into the future and predict outcomes. Options exist for insurers to use pre-built P&C insurance predictive analytics models or to customize their own models based on proprietary datasets.