Enriching fraud/AML detection by new data
The need for adequate cybersecurity and fraud detection measures is critical in the financial services industry. Unfortunately, fraud and anti-money laundering investigations are costly due to their high demand for human resources 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. AI/ML solutions in fraud detection transform the limited, traditional rule-based approach with machine learning algorithms, giving it a more reliable and accurate engine for detecting suspicious cases.
Current fraud and anti-money laundering systems are too straightforward and not adapted to conditions of increased efficiency and might be ineffective due to high false-positive rates. They are based on expert rules, require manual adding and adjusting of scenarios and can not always detect implicit correlations.
As a result, fraudsters and money launderers adjust to the existing detection rules and develop new and unrecognised methods. Fighting them efficiently requires either extreme prejudice in fraud/AML rules (and therefore revenue loss from client rejections) or high staffing demands for specialists checking each flagged case individually (which leads to additional wage costs). Relaxing either of the previously mentioned approaches increases AML and fraud risk.
AI solutions can help boost the security of digital transactions and online activities by analysing thousands of data points in real-time and flagging suspicious transactions or fraudulent claims before it leads to any loss for the company.
We provide a custom open platform to decrease false positives/alerts and increase the number of true positives or actual frauds. This platform improves the process of detecting fraud activity by exploring behavioural and unstructured digital data, such as website, app, forms or graph connections between clients, as well as visual data, using computer vision to score scanned documents. In addition, advanced methods like graph-based machine learning, clustering, or computer vision-based solutions combined with traditional rules help create more robust solutions.
Our platform provides investigators with an interpretation of the reasons or triggers for suspicion and prioritises the level of distrust, and thus enables investigators to focus on a smaller number of high-risk cases. It also reduces the risk of fraud and money laundering by finding new cases and patterns with the help of machine learning methods.
- Machine learning can spot new fraud and money laundering patterns and create algorithms that process large datasets, making it easier to find hidden correlations between user behaviour and fraudulent actions.
- The platform, customised to your needs, explains the clusters, classifies the transactions, and prioritises suspicious cases, allowing the investigators to use their time much more efficiently.