Can we have tarot reader for Insurance companies

Whether you are a property casualty and health insurers or other financial services provider, handling claims is tougher than wandering on bare rocky hills. Analysts estimate the annual cost of property casualty insurance fraud in the range of US$ 44 billion, and healthcare claims fraud numbers vary from US$ 90 to US$ 180 billion annually. One technology option now gaining popularity within the industry is Predictive Analytics - an emerging market that is expected to grow as much as US$ 3 billion by 2008.

The biggest test for insurance companies is to evaluate how to identify fraudulent claims and categorize risk profiles. To start with, many insurers are looking at technology options to support investigative  and adjuster personnel,  to automate claims handling processes and to create an integrated, real-time claims operation that ultimately reduces both claims payout and legal expenses. One technology option now gaining popularity within the industry is Predictive Analytics - an emerging market that is expected to grow as much as US$ 3 billion by 2008.

What is Predictive Analytics?
Predictive Analytics, in simple terms is similar to consulting a tarot reader which is used to gain insight into the current and possible future situations based on the  card you choose. In the  same manner, Predictive  Analytics is a future-focused branch of business intelligence/data mining software  solutions, where analytics are used to determine the probable future outcome of an event, or the  likelihood of a current state where it is unknown.

The Predictive Analytics model blends and combines claim behavior variables to arrive at a score for individual claims. It is applied to every transaction associated with the claim by comparing it with not only the claim history but also the patterns captured from industry claims. The major difference between Predictive Analytics models and a standard analytics solution is in the number of variables and exceptions that Predictive Analytics  solutions can process simultaneously. The  greater the number of variables fed into the system, the higher the chance of accuracy. Hence, Predictive Analytics solutions work best in industries that process huge amounts of data, such as the data needed to detect potential claims fraud.

Benefits of Predictive Analytics
Predictive Analytics software has  the ability to  learn from patterns  of customer behavior and update itself to improve accuracy. For example, when an insurer issues a life insuran ce policy, they need to consider a number of factors, such as age, gender, health condition, occupation, income, location etc and on the basis  of these inputs, analyze  the data from different internal and external  sources.  The dynamic ability of  Predictive Analytics solutions to predict outcomes by analyzing the interrelationships between thousands of claims variables can present a tremendous competitive advantage to insurers who discover patterns that go beyond traditional assumptions and use them to manage claims, develop new products, and redirect sales campaigns.

In summary, some of the benefits of Predictive Analytic tools to claims management are:
The answer is easily found in the broader solutions that Predictive Analytics can put forward. To conclude this with a smiling face we can say - Predictive Analytics enables insurance companies to make smarter decisions and ensure valuable claims operation.

• Insurers can identify which claims qualify for immediate approval and which are potentially fraudulent
• Depending on the score allocated by the software, insurers can decide whether to send the claim to the fraud department for detailed investigation
• Companies can enhance customer service by reducing claim cycle times
• By reducing fraudulent claims through active detection, insurers can offer more competitive premiums to their customers
• Insurers can more effectively utilize their most skilled and scarce claims resources
• Insurers can apply the principles of predictive analytics to other claims areas such as subrogation or adjuster workload management.

A couple of questions before we end –

• Which customers are important to our business?
• How do we regard customer behavior and pattern?
• How to identify current claims that fit the risk profile?
• How to identify relationships that can serve as risk indicators for future claims?
• How to apply archive data relationship rules that indicate abnormalities and apply those rules against all new claim submissions?