Fraud Detection

Accurately predict fraud and save millions of dollars

Fraud is a massive problem in a range of industries. On average, the banking industry reports more than $150 billion in losses a year. The insurance industry reports around $40 billion in fraud per annum, plus up to 10% of all healthcare expenditure.

Accurately identifying fraud is a challenge due to the variety of ways fraud can occur, huge volumes of data to assess and the rate of innovation from fraudsters. Even if prediction is possible, there is the challenge of maintaining the fne balance between customer experience and risk appetite. If you reject a legitimate purchase, you anger a loyal customer, lose the fee income & risk an account churn. But if you approve a fraudulent purchase, your customer becomes a crime victim, and your bank has a loss.

Stop fraud in its track with AI

Using AutoAI and Auto Machine learning platforms, you can analyse large volumes of data and identify fraud patterns.

iTuring AutoAI takes this one step further by analysing data in a matter of hours and producing predictions in real time which allows for immediate action. This means that it’s not only relying on past experiences but takes into account emerging behaviours and trending transaction anomalies. The iTuring AutoAI platform also constantly re-evaluates if the predictive model is the most accurate. When fraudsters change their techniques, iTuring senses the degrading of the model and triggers an alert for retraining.

These real-time results create a better customer experience, reducing false positives and quickly approving purchases. Keeping fraudsters at bay means lower fraud losses and a more profitable business.

How AI fraud identification happens in the real world

A leading retail bank had experienced a 400% increase in fraud cases, particularly around application fraud. This resulted in a huge loss of money, as well as an increase in costs for fraud detection and investigation. They had built a predictive model using traditional analytical tools which helped them identify these frauds, however they were looking at opportunities to increase accuracy due to the large number of false positives.

iTuring AutoAI’s predictive model for fraud prediction, which was built on the same set of data, identified almost 95% of fraudulent activity, which meant a 12% uplift on existing model. These results translate to millions of dollars of savings each year when implemented as a real time event-based system that disallows fraudulent transactions as they happen.

CyborgIntell can reduce fraud in your business

iTuring AutoAI’s end to end single click AutoAI solution understands the data and applies the correct techniques to improve the quality of the predictions. When deployed in real time, it can instantly provide decisions to approve legitimate transactions, decline fraudulent ones, or trigger further investigations as required.

Contact us now to find out how CyborgIntell can reduce instances of fraud and increase profitability of your business.