Big data and advanced analytics have begun to yield big payoffs for a few insurance companies. And insurers globally plan to spend more on it over the next three to five years, having seen the early successes of companies such as Progressive in personal property/casualty (P&C) lines, and AIG in commercial lines.
Annual spending growth on big data analytics will reach 24% in life insurance and 27% in P&C insurance on average, according to the 70 insurers surveyed for Bain & Company's benchmarking database. Opportunities expand every day with the proliferation of new sources, including sensors, Web chat logs and videos, as well as the 195 000 datasets that US government agencies have made publicly available.
Yet most insurers have barely scratched the surface. Roughly one in three life insurers and one in five P&C insurers do not apply big data advanced analytics for any function, according to Bain's benchmarking survey. On average, insurers apply big data to approximately two functions, and many companies do not have a solid plan to wring value out of the data.
For the firms that dig deeper, substantial benefits accrue. For example, big data can improve decisions in the following three areas:
For the firms that dig deeper, substantial benefits accrue.
Innovation: Some insurance companies are using analytics to create innovative new products or expand underinsured markets. Automakers, for instance, have a parts warranty exposure of over $60 billion per year, but they lack the capability to aggregate and analyse their claims data. We Predict, a UK-based company, built an analytics engine that accurately predicts parts failure, which it now licenses to the industry. This engine helps to improve supply chain efficiency (getting the right parts to the right dealer at the right time), reduce dealer fraud (detecting when repair rates are higher than normal and actively intervening), and raise consumer advocacy (by anticipating problems before they occur and notifying loyal customers in advance).
Underwriting and claims: At a commercial lines carrier, underwriting due diligence took up to nine months, with on-site inspections of scores of properties owned by any large business applying for coverage. The carrier decided to review its own database of clients to uncover best safety practices and then check US federal data on safety violations as a way to screen prospective clients. While the analytics have helped the carrier reduce expensive initial site inspections by engineers, the real value lies in avoiding signing on a client with a high probability of a $100 million accident down the road.
Turning to claims, Santam in SA wanted to reduce the fraud rate of its medical claims. Working with IBM, the company decided to analyse three years of customer data to hypothesise where fraud was most likely to occur. It then established a set of rules on claim type, amount and other red flags to segment incoming claims and take appropriate action.
Claims data, combined with pathology results and customer questionnaires, has helped Santam forecast and prevent further health risks. The initiative saved $2.4 million in the first four months, and it has given Santam the ability to accelerate half of its processed claims by putting them to straight-through processing. As a side benefit, the initiative identified a motor insurance fraud syndicate in the first month.
To get more familiar with big data analytics, insurers should start with small steps. They should choose a case, hypothesise what data will correlate with the behaviour in question, obtain easily accessed data sets and begin modelling to see what develops. They can then build their capabilities so as to use analytics to their full potential. By applying big data in the right places and the right ways, insurers can develop insights that are difficult to replicate and eventually build a competitive advantage.
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