Insurance sector discovers Business Intelligence
By Corey Springett
The insurance industry has emerged from an intense period of flux. Now that the flurry of mergers and acquisitions is over, organic growth is the key area for expansion.
Insurers must grow the business from their existing client base and to do so, they need sophisticated analytical solutions that transform data into information, and then turn information into knowledge to give sustainable competitive advantage.
The industry is no longer growing through mergers and acquisitions. The industry, however, already offers an enormous product range and is unlikely to develop startling new products, or find brand new markets to tap. Insurance companies therefore need to grow from their existing client base. At the same time, the insurance industry faces a number of problems including high client attrition rates, fraud and pricing challenges.
The client lapse rate averages between 25 and 35% each year, and is considered the norm.
Fraud also seems to be on the increase. Many policyholders seem to feel that they must compensate for the premiums they have paid by over-claiming. Syndicated fraud is an even bigger issue.
Finally, pricing is a problem for the industry. If companies could better understand the risks associated with particular individuals, or geographic areas, for example, they could price more effectively.
Business intelligence solutions can address all these ‘pains’. They can significantly reduce client lapse rates, with immediate impact on the bottom line. Metropolitan Life, for example, can predict – within around 90% accuracy – the likelihood that a new policy-holder will default in the first year. This means that the company can implement interventions and campaigns to retain these customers.
Business intelligence solutions can also examine reams of data, picking up patterns that indicate fraud. And they can be used for scientific rate making, or pricing, that takes numerous factors into account and helps to keep profitable clients while letting unprofitable clients leave.
In addition to all these ‘pains’, the industry also faces data challenges. Insurance companies generally have many disparate data sources, and pulling all the data together has long been a challenge. For many, it is almost impossible to get a single view of the client across different product lines. In many companies there is also a perception that the data they have is of poor quality, resulting is insurance companies being data rich but information poor. Many insurers have little idea which of their clients are really profitable. For example, an individual may be unprofitable on his motor insurance, but highly profitable on his household insurance.
To achieve organic growth, however, insurers need to understand the client base even better than this. They also need to be able to anticipate the risks associated with each client. Using customer lifetime value management tools, companies can predict who is likely to lapse, and when. They can also predict who will buy which product, and when – leading to cross-selling opportunities.
Profitability analysis also takes account of claims, and enables companies to predict what a particular individual is likely to claim, based on previous patterns.
Business intelligence solutions are of enormous value for marketing. Certain individuals react to different types of marketing differently. Analytic solutions can help understand which clients react in what ways, when and how, enabling marketing campaigns to be targeted to the right individuals for the best returns. Historically, the industry has upped its premiums by a certain percentage, determined by the loss ratio, each year. There are more scientific ways to set pricing,. Companies need to understand who their profitable clients are, and thus who they want to keep. They can then understand the impact of a particular price increase on this group, and make sure that premiums are increased accordingly.
For example, if the most profitable customers are pensioners, drastic price increases may lead to the loss of this most profitable segment of the customer base. Business intelligence tools can assist in new product development by intelligently segmenting existing clients, and identifying groups that do not fit the profile of any existing product offering. It can also turn traditional thinking about risk on its head. For example, within the age categories traditionally used by the industry, there are several sub-categories that have now been found not to fit the pattern.
One local insurer used data mining to identify, within the 18 to 25-year old group, a sub-category with an associated risk much lower than the norm for this group. In addition, there is a sub-category of high-risk individuals – executives with drinking problems – in the 45 to 55-year group.
Once companies re-classify their customers, they can better understand the real risks associated with them.
Corey Springett is a business development manager at SAS Institute, a market leader in providing business intelligence software and services. Contact SAS Institute on (011) 713-3400 or visit their South African web site at http://www.sas.com/sa