Fraud Detection


Banking 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 cardholder’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 in-store purchases and the number of cash-free transaction increases to digital.

As the trend forCredit Card fraud rises , fraud scenarios and malware become more subtle and harder to detect. Banking fraud protection has never been so important.



Type of expertise/ AI domain

SVM, Clustering, Classification Algorithms, Anomaly Detection

Internal data required

Historical Payment and User Activity Data

One Response

  1. As far as the number of transactions, real clients, and integrations grows, security threats come along. This is when machine learning algorithms come in handy when banks and other institutions require a special fraud detection.

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