Business Intelligence: The Secret to Successful Leadership
Insurance is a data-rich industry. Many insurance organisations are overloaded with data, but the trick is to turn that data into insightful information for better decision-making.
The uncertainty of the current economic climate underscores the need to collect, transform and analyse data to help better manage growth, costs and risks throughout the organisation.
Business intelligence is the secret to success for a growing number of insurance executives. Consider the wide range of areas of focus for business intelligence programs:
- Financial risk. By automating data-gathering processes and increasing the frequency and accuracy of financial risk modelling, insurers are refining their reserves and driving significant performance improvements in their portfolios.
- Operational costs. With insurance back offices being asked to do more with less, operational managers are using real-time dashboards to manage backlogs and monitor performance at group or individual levels.
- Pricing models. Proprietary company data and third-party information are helping actuarial departments better segment customers based on their individual risk factors to develop more focused and accurate pricing.
- Fraud detection. Claims managers are using predictive analytics to help identify potentially fraudulent claims as early as the first notice of loss, and they are analysing claims costs to get a better handle on negative trends.
- Producer and agent relationships. Companies are using business intelligence to get a better understanding of the value that each agent or broker brings to the organisation, allowing them to provide tiered services based on producer performance.
- Vendor management. A wide range of areas within an insurance company are analysing data related to costs and performance. This is to make more informed decisions when negotiating contracts with outside vendors including law firms, body shops, medical professionals and laboratories.
Getting Past Data Quality Problems
Is your company doing everything it can to turn your data into business intelligence? It is not a matter of whether you have a data quality problem, it is a matter of how large your data quality problem is.
It is not uncommon for insurance companies to have multiple sources storing identical information such as customer account data. It is also not uncommon for individual users of systems to use available fields for other than their intended purpose, causing the data extracted from these systems to be suspect. To ensure a successful implementation of business intelligence programs, insurers must thoroughly analyse and cleanse the data.
One of the biggest challenges is the siloed nature of systems using a wide range of technologies and, in many cases, acquired through acquisitions. This makes it difficult to obtain all the data required to have a good understanding of products, customers, vendors, etc.
Companies need to implement a master data management (MDM) strategy that outlines how data will be integrated throughout the organisation, along with data governance policies and processes that help to ensure data quality and consistency. Both MDM and data governance are crucial to any business intelligence strategy.
Looking into the Future with Predictive Analytics
Within many insurance company systems, key data are rarely leveraged for business intelligence initiatives. A prime example is the data that sits in the field of adjuster notes in a claims system.
Within the notes field, you will find commentary about any interaction with the claimant that could provide additional insight about claim. It may be noted that the claimant seems nervous or appears to be trying to rush the process, both indicators of potential fraud. The implementation of text mining technologies as part of a business intelligence strategy helps insurers access valuable information hidden in these text fields.
Successful companies are now using predictive analytics technologies to mine historical data combined with third-party information and predict the company’s future rather than study its past.
Analytics is a continuous process. To achieve true value from data analysis it must be done continuously to make sure it takes into account marketplace changes. Conducting a pricing study once a year to set rates, for example, will almost guarantee lost opportunities for revenue.
Today’s market is simply too dynamic to wait that for the numbers to come in. Every successful insurance leader knows that.
Written by CSC’s Scott Kemmerer, this article appeared in the October 2009 issue of Insurance Networking News magazine. Scott Kemmerer is a Chicago-based financial services consultant for CSC.