The makers of cars, trucks, and other vehicles account for between 40% and 45% of the warranty expenses of all US-based manufacturers. Fraudulent warranty claims therefore represent significant exposure for Automotive OEMs. In order to estimate the potential savings, we need to understand these expenditures and also implement an effective system for detecting warranty fraud.
Finding warranty claims fraud is as much about us wanting to do something about it as it is about finding the proverbial needle. In a world where there are always some people and organizations trying to take advantage of a potential fraud opportunity, what can we do to identify that needle in a haystack? We can use the power of predictive analytics.
We sat down with Porsche Westlake’s Service Director, Sam Abregel, to address 8 categories of warranty fraud to watch out for. Given the vast amount of transaction data and the presence of anomalous patterns indicating fraudulent activity in these categories, there is a huge opportunity to harness predictive analytics and machine learning models.
Fraud has been causing rising challenges for businesses. Over 72% of businesses cite fraud as a growing concern, and about 63% of businesses report the same or higher levels of fraudulent losses over the past 12 months according to a report by Experian Global. The challenge is not just about preventing fraud, but figuring out how to predict it before it happens, so it can be prevented from happening at all. But before we make strategies to combat fraud, it’s important to understand the barriers associated with fraud detection strategies.
We’re living in an era of extreme automation, high transaction volumes, and a highly connected world where it’s so virtually easy for fraudulent transactions to hit any business. To protect your business from fraud schemes and bad actors before they can cause significant damage, detecting fraud patterns is essential and a huge payoff when done in real-time.