Fraud detection and Prevention
[wtm_use_case_cats]
Financial organizations use it to monitor a considerable amount of transaction parameters at once for every account in real time. The algorithm examines historical payment data and analyses every account holder’s action. Fraud detection involves monitoring and analysis of the user activity to find any usual or malicious pattern. Such models can be highly prominent and prevent some suspicious behavior with a rather big precision. Apart from spotting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time instead of detecting them after the crime has already been committed.
Benefits for the company
Fraud has never been a new thing. It is especially obvious now, when the world is moving away from cash to the increase in number of cash-free transaction, and as digitalization increases.
As the trend for Credit Card fraud rises , fraud scenarios and malware become more subtle and harder to detect. Banking fraud protection has never been so important.
Feasability
High
Type of expertise/ AI domain
SVM, Clustering, Classification Algorithms, Deep Neural Networks
Internal data required
Historical Payment and User Activity Data
Stock market analysis, early detection of insider trading, financial anomaly detection is high risk, so this use case is challenging because it must be done truly real time so that it can be stopped as soon as it happens. Also, it’s more important perhaps than other use cases to be careful with false positives that may disrupt user experience.