Fraud detection with AI/ML


Fraud detection with AI/ML solution enriches the limited traditional rule-based approach by combining it with machine learning algorithms, giving a more reliable and accurate engine for detecting suspicious cases.

Business case  

Traditional fraud and AML solutions are based on expert rules and require adding/adjusting scenarios manually and can hardly detect implicit correlations. They are too straightforward and prone to inefficiencies due to extremely high false-positive rates. In addition, they do not scale well in case of increased complexity. 


We can help you to improve the process of detecting the fraud activity by exploring behavioral and unstructured digital data (website, app, forms, graph connections between clients, etc.), visual data (using computer vision to score scanned documents, etc.) with the help of more advanced methods like graph-based machine learning, clustering, computer vision-based solutions in combination with traditional rules to create more robust solutions. 


  • Build a custom open platform for fraud management. 
  • Find the new fraud/money laundering patterns with the help of machine learning methods. 
  • Create algorithms that process large datasets and find hidden correlations between user behavior and fraudulent actions. 
  • Transform the transaction processes and contracts of the clients into connections in (directed acyclic) graphs. 
  • Find anomalies in these graphs with clustering techniques. 
  • Automatically explain the clusters, classify the transactions, and detect the fraud. 

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Key contacts

Bob Hroch - DataSentics

Bob Hroch

Business Development

Čeněk Kras - DataSentics

Čeněk Kras

Business Development