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.
Continuous monitoring (CM) and continuous auditing (CA) systems provide real-time monitoring and reporting of business processes, activities, and transactions. This could largely benefit CEOs, COOs, and board members who are concerned with the risking risk, regulation, and costs in their organization.
With the rapid growth of the internet and the IoT and the resultant digital transformation of the world we live in, there is an explosion of data that is being generated, collected, and stored. There is data available for “normal” transactions, as well as for the fraudulent of interest to a company. If one could successfully analyze this data and gain meaningful insights and draw conclusions from it, it would be possible to use that insight for reducing the threats and risks against organizations.
As the volume of online transactions continues to grow exponentially, there is a new quest for enterprises to start new channels of business, gain more online presence, retain existing revenue streams, and stay relevant to current technology. Many organizations are unable to detect patterns related to Fintech fraud because they are stuck doing traditional rule-based approaches. Emerging modern technologies, like NEMESIS, use the power of AI and machine learning to fight against financial fraud.