Research shows that the medium financial loss from fraud is $145,000. However, over 20 percent of corporate fraud cases involve losses of over one million dollars. Cyber-crime, identity theft, and credit card abuse are the most common problems in small businesses, but corporations must deal with asset misappropriation and financial statement fraud. Fortunately, the power of Big Data is improving the accuracy and efficiency of risk and fraud management activities.
Health care fraud makes up anywhere from three to 10 percent of all health care expenditures in the country. Property fraud costs the economy about $34 billion every year. Traditional fraud prevention relies on rules that are easily shared, manipulated and loopholed. Predictive analysis solutions that are powered by Big Data use a combination of rules, searches, modeling, data mining and exception reporting to effectively identify fraud throughout the claims cycle. Loss reserving and claims forecasting can accurately calculate outcomes based on massive amounts of claims data. Data mining technology can group claim loss characteristics together to rank, prioritize and assign claims to the most experienced adjuster.
The bulk of an insurance company’s loss adjustment expenses is often wasted on defending disputed claims. Insurance companies use Big Data and predictive analytics to accurately calculate litigation propensity scores in order to identify which claims are statistically more likely to result in litigation. These claims can then be assigned to senior adjusters who will be better prepared to settle sooner and lower losses. Some insurance companies use fast-track processes to instantly settle claims, but this results in over payments and unnecessary claims. Analyzing claim histories allows management to reduce labor costs, optimize payout limitations and shorten claim cycle times.
Financial organizations are using Big Data, sentiment analysis and machine learning techniques to extract valuable insights from large data sets. For example, Big Data improves risk management from both an operational perspective and a customer relationship angle. Fraud prevention and credit scoring accuracy can be improved through scenario simulations that identify risk factors, demographics, and concentrations. Big Data enhances the power and performance of traditional tools, such as the value-at-risk (VaR) model that statistically assesses the market, portfolio and time frame risks.
Big Data is becoming the preferred analytics tool to mitigate risks, uncover hidden patterns, control settlement amounts and drastically reduce costs.