FraudIA has been originally developed for Transactis the joint venture between two equal partners Société Générale and La Banque Postale specializing in the management of real-time, high-volume payment systems. fraudIA is a software solution to detect credit card fraud in real-time. fraudIA complements the existing fraud detection systems such as expert rules engines.

Plus points


Up to 5 %

recall on frauded transactions undetected

by other tools*

0 million

credit card transactions

 per day

0 ms

Response in less

 than 50 milliseconds

Reduce financial losses

Avoided fraud up to several million euros per year

250 transactions/second

Tested on volume metrics in real time

Speed, security, and simplicity of processing

Simple API

*The values are estimates based on simulations performed on historical data from 2017-2019. The values of recall differ between 5 and 35% depending on the type of transaction (VAD, withdrawal, payment) and the bank. The percentage corresponds to the fraction of reported fraud detected by the FraudIA algorithm with an accuracy of 30%. Reported fraud is defined as fraud reported by the bank’s customers, i.e. fraud that has not been discovered by any other tool used by the bank.

Performance in production may differ and is highly dependent on changes in the configuration of other fraud detection tools and changes in the behavior patterns of fraudsters and customers between the preparation of the model and its use in production.

AI in the service of fraud detection for online payments, face-to-face payments, and ATM withdrawals.

fraudIA helps to detect and block fraudulent transactions in online payments, face-to-face payments, and ATM withdrawals. It reduces the amount of fraud impacting cardholders.

High processing speed is one of the essential features of any credit card transaction processing system. fraudIA must produce an AI score within 50 milliseconds!

In this short time interval, fraudIA performs complex data processing, optimized to handle a load of several hundred transactions per second.

  • Analysis of the card transaction history
  • Calculation of model features
  • Model Scoring


fraudIA uses real-time machine learning to calculate the probability that a transaction is fraudulent. The calculation is based on :

fraudIA regularly retrains the machine learning models to include new fraud scenarios manifested by fraudulent transactions and objected cards.

fraudIA has been developed by a team of Data Scientists, developers, and architects. It contains a custom model adapted to the specifics of the bank’s data.

The advantages of fraudIA


at Transactis

Transactis is the joint venture between Société Générale and La Banque Postale.  It manages the information systems for e-banking activities, developing new SEPA & international direct debit and wire transfer management platforms.


Transactis is a major payment processor in France. Every day, up to 12 million transactions for more than 3 million cards pass through its servers. Each year, more than 1TB of data is produced by the 15 million credit cards in circulation managed by Transactis.

Implementation / Expertise

To answer these technical challenges, Probayes employs Big Data and NoSQL technologies. The data flow between cluster nodes is managed by Apache Kafka, while in-memory databases such as Couchbase and RocksDB ensure extremely fast response times for transaction data storage and retrieval. To ensure process isolation, high availability, and scalability, services are managed by containers and an orchestrator.


The solution has been deployed and performance data will soon be available.

« A real-time, AI-based solution to detect fraud built by Probayes, is directed towards the banking and insurance industry.

fraudIA manages more than 200 transactions per second with response times shorter than 50 ms per transaction.

The solution complements existing tools and enables the detection of fraud patterns in credit card transactions (withdrawals, face-to-face payments, and online payments) that remained undetected by traditional expert systems. The features contribute to a net reduction in fraud. »

Vincent Maigron

La Banque Postale – Head of Anti-Fraud – Operational Management

Société Générale – Head of the Fight Against Fraud – Carriers & Merchants

Probayes solutions for the financial services industry


Detect credit card fraud

Deux personnes se serrant la main


Detecting customers that are likely to leave


Giving customers and employees a conversation agent