Accurate Fraud Detection

Accurate Fraud Detection

Accurate Fraud Detection

The Challenge

Today, we live and breathe in a digital world. Digital payments and e-commerce have grown significantly. Evolution of digital payments in retail opens avenues for fraud and financial crime. As the stakes are getting higher, it is not enough to detect fraud after the event, and traditional tools are insufficient. Fraudsters are constantly finding new ways and means to use some of your confidential information to execute fraudulent transactions. Organizations need tools and techniques to detect frauds in real-time to take appropriate actions.

About the Company

The Solution

CyborgIntell enables you to detect fraud in real time with extremely high accuracy. It gives you the power of highly accurate data driven predictions, which means that you can not only reduce fraud but also the time spent on manual analysis. iTuring can utilize the hundreds of parameters that are involved in a financial transaction such as spend amount, past transaction trends, location of the transaction, transaction time, merchant name etc. to detect an anomaly and fraud in real-time.

 iTuring develops highly accurate machine learning models to help detect and prevent both known and unknown frauds and recognizes patterns in data to distinguish fraudsters from legitimate customers, based on hidden behaviour and pattern, that sometimes may seem completely unrelated to a human being.

For the Delivery Services Company, CyborgIntell used iTuring to build both “Supervised” and “Unsupervised” models to detect both known and unknown fraud. iTuring’s model detected known fraud with 97% accuracy and only 2% false positives even with an exceptionally low incidence of fraud (0.00011%) and correctly identified 1 unknown fraud in every 1.28 transactions reviewed.



Predictive Accuracy

0 %

Saving in Fraud

$ 0 K+↑

New Frauds Detected

0 +

Why CyborgIntell

Not only does iTuring provide highly accurate predictions of which transactions are likely to be fraudulent, it also uncovers anomalies in the data and discovers fraud that were not known previously. Additionally, it detects changes in data, patterns and behaviours and automatically retrains models, thereby ensuring no failure in decision making. This ensures that businesses have a real saving from fraud prevention and can adapt quickly to ever changing fraud behaviour.

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