Insurance is a data-rich industry. Many insurers 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 analyze data to help better manage growth, costs and risks throughout the organization.
Business intelligence is the secret to success for a growing number of insurance executives. Consider the wide-ranging of areas of focus for business intelligence programs:

  • Financial risk. By automating data-gathering processes and increasing the frequency and accuracy of financial risk modeling, 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 are analyzing claims costs to get a better handle on negative trends.
  • Producer relationships. Companies are using business intelligence to get a better understanding of the value that each agent or broker brings to the organization, allowing them to provide tiered services based on producer performance.
  • Vendor management. A wide range of areas within an insurance company are analyzing data to make more informed decisions when negotiating contracts with outside vendors, such as law firms, body shops, medical professionals and laboratories.

Getting Past Data Quality Problems


Turning data into business intelligence is not a matter of whether you have a data quality problem, but more the size of your data quality problem.
It is not uncommon for insurers to have multiple sources storing identical information, such as customer account data. It also is 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 analyze and cleanse the data.
One of the biggest challenges is the siloed nature of systems using a wide range of technologies that were, in many cases, obtained through acquisitions. This makes it difficult to garner all the data required to have a good understanding of products, customers, vendors, etc. Insurers need to implement a master data management (MDM) strategy that outlines how data will be integrated throughout the organization, along with data governance policies and processes that help ensure data quality and consistency.
Both MDM and data governance are crucial to any business intelligence strategy.

Looking into the Future of Predictive Analytics


Within many insurance company systems, key data elements 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 into the 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.
While it is natural for an insurer to look at historical data to better understand its business, successful companies are now using predictive analytics technologies to mine historical data, combining it with third-party information to 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.
This article can also be found at InsuranceNetworking.com.