Down for the count: Fight fraud in the insurance industry with analytics
Insurance fraud is costing insurance companies billions of dollars annually, which is translating into higher premiums for policyholders. However, fraud no longer has to be just a cost of doing business.
How serious is fraud?
Fraudsters are walking away with billions of dollars, which costs regular people like you and me more of our hard-earned money. When insurance companies take a hit from fraudulent activities, we’re right beside them in the ring. Can you believe that there were almost 130,000 cases of detected fraudulent claims last year in the UK, totaling over 2 billion USD? US insurance companies are also rolling with the punches of insurance fraud. US property and casualty insurers are losing roughly $30 billion a year to fraud. Additionally, the Insurance Journal reports that US insurers are paying between $5.6 billion and $7.7 billion in excess payments for auto insurance injury claims. The numbers are steep indeed, but insurance fraud no longer has to be just a cost of doing business. Insurance companies can fight back with fraud detection and fraud prevention solutions.
What can you do to fight back?
It’s not yet time to throw in the towel. There are measures insurers can take to detect and prevent fraud, as many other insurers are realizing. In fact, “IBM has assisted nine of the top 10 largest insurance companies in significantly reducing fraud losses. Using the IBM Counter- Fraud Management solution, forward-thinking organizations can proactively combat fraud, improve business results, and reduce loss while maintaining a positive customer experience.” Organized crime rings and opportunistic individuals have thrown their hats into the ring with insider fraud, identity theft, staged automobile accidents, medical billing and more. If you want to avoid a knockout, you should do what other top insurance companies are doing, and fight back with data and fraud analytics.
Analytics solutions are in your corner
IBM's Property and Casualty Claims Fraud for Insurance solution helps insurers detect, predict, prevent and investigate fraud. It is a fully integrated, scalable fraud analytics solution that takes you through the entire fraud process. You can follow the case from “first notice of loss, detection, investigation and the discovery of new patterns, through alert and case management and reporting, and finally to retrospective analysis.” You can monitor cases, track their investigation progress and detect fraudulent claims with a dynamic dashboard. What exactly can you look forward to with advanced analytics powered by IBM? You will have an aggregated view of all data regarding a person, place, event, business or transaction. Eliminate data silos that threaten the efficiency and accuracy of detecting insurance fraud. Instead, tap into large disparate data sets for the complete view that you need to take action.
What are the main benefits of the solution that I can present to my program director, CRO, CIO or other business decision-maker?
IBM's Property and Casualty Claims Fraud for Insurance provides a comprehensive solution to address your fraud challenges. You will be able to detect, prevent and address fraud more efficiently, saving valuable time and money. With the fraud prevention solution you will also be able to:
- Swiftly distinguish fraudsters from your valuable customers
- Reduce the number of false positive fraudulent claims
- Detect fraud and take appropriate investigative action before unnecessary payments occur
- Focus your investigations on high-risk cases
- Operate the claims investigation process all the way from prevention through litigation
- Conform with regulatory compliance obligations
- Leverage enterprise intelligence in order to continuously adjust operations and adapt to stay ahead of trends
Request a complimentary, customized workshop for your insurance company that will teach you how top insurers are preventing fraud, how you can utilize insights from fraud analytics and how to create a roadmap for addressing your chief pain points with analytics.