AML and Fraud detection
Fraud/AML investigation is costly due to its high human resources demands and a constant need to adapt to new methods used by fraudsters and money launderers. However, with the help of machine learning, these costs can be reduced significantly.
Current fraud/AML systems may be ineffective due to high false positive rates and are not adapted to conditions of increased efficiency. As a result, fraudsters and money launderers adjust to the existing detection rules and develop new unrecognized methods. Typically, this requires a combination of the following approaches:
- Extreme prejudice in fraud/AML rules (and therefore revenue loss from client rejections)
- High staffing demands for fraud/AML specialists checking each flagged case individually and leads to additional wage costs
- Increased AML and fraud risk if the other two approaches are relaxed
We provide a custom open platform to decrease false positives/alerts and increase the number of true positives or actual frauds. Reducing staffing demands of fraud/AML investigation and speeding up the process by:
- Providing investigators with an interpretation of the reasons/triggers for suspicion
- Prioritizing the level of distrust and thus enabling investigators to focus on a smaller number of high-risk cases
Reducing risk of fraud and money laundering by finding new cases and patterns with the help of machine learning methods.
- A custom platform where you own the underlying code
- Identify new fraud/money laundering patterns
- Reduce workload by better prioritization of suspicious cases