Fraud detection for a multi-dimensional view of identity risk

Tractus Identity Fraud machine learning models are designed for particular industries and trained with feedback data from a group of clients to tackle targeted fraud trends and generate real-time actionable risk scores and reason codes.

Fraud prevention technology has made enormous strides from advances in computing speeds (high-performance analytics), machine learning and other forms of artificial intelligence (AI). Fraud touches every area of our lives; it raises the price we pay for goods and services, squanders tax money, pulls resources from innovation and even costs human lives.

The machine learning (ML) approach to fraud detection has received a lot of publicity in recent years and shifted industry interest from rule-based fraud detection systems to ML-based solutions.

The advantages of ML-based Fraud Detection

One of the advantages of machine learning systems over rule-based systems is that they process data faster and need less manual labor. Smart algorithms, for example, work well with behavior analytics to help minimize the number of verification steps.


Automatic detection of possible fraud scenarios


Real time processing


The reduced number of verification measures


Finding hidden and implicit correlation in data