Fraud Detection based on Machine Learning

Fayrix Machine Learning solution identifies and helps to prevent fraudulent or suspicious actions inside enterprise IT infrastructures
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Key challenges of developing a fraud detection plan
  • Good guys need data to analyse, therefore the initiative comes from bad guys.
  • Good guys need time for developing and testing models.
  • Bad guys can adjust their fraud methods fast.
  • Bad guys are smart and more motivated.
Machine Learning is the solution!
  • Detects low-level patterns and abstractions.
  • Restores complex quickly-changing patterns, unavailable in the initial training data set.
  • Works well processing complex signals - video and audio.
  • Completes all tasks automatically.

Fayrix Solution
COMPOSITION

Fraud detection technology solution is based on Artificial Intelligence and Deep Learning. The solution monitors the IT infrastructure of a company and ensures high security from any unauthorized internal and external actions with continuous self-learning.


1
Infrastructure monitoring and dynamic data analysis module
2
Machine learning module
3
Analytical reports and alerts module (Administration Panel)

fraudulent activity
tracking (Finance case)

Fayrix Fraud Detection Solution conducts multi-level monitoring of the IT infrastructure of a company, provides real-time analysis of all actions and notifies system users of all detected suspicious actions.

Transaction level
  • The solutions utilises the latest Machine Learning and statistical models for fraud detection.
  • Algorithms get more complicated with every new transaction
Client profile level
  • Activity is being monitored on a user account level.
  • Suspicious actions include frequent money transfers and frequent user profile updates.
Client network level
  • User interactions and affiliations are tracked.
  • Frequent money transfers from single independent accounts to the main one look fraudulent .

SOURCE DATA
( for finance case)

Do you want a smart solution to protect your business from fraud?
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