Efficient Fraud Management in Leasing and Financing

Obtaining credit by false pretenses leads to losses running into millions of euros or dollars every year for financial service providers – and fraud rates are on the increase. Credit fraud usually arises when applicants deliberately give false information, for example, about their identity or their income. In such cases, a loan may still be approved because conventional risk management systems only map the classic default risks of potential business partners, but not their fraudulent intentions.

Increasing the Rate of Fraud Detection with Minimal Effort

The high number of contracts makes it impossible to manually check all loan requests for suspected fraud. It would simply be too time-consuming and expensive. To achieve a high rate of fraud detection with only a little manual effort, it makes sense to preselect contracts intelligently and with the help of IT. But simple reporting solutions or rule-based systems are not suitable for this: They are too static and recognize only known or defined patterns. As a result, new types of fraud often remain undetected for a long time. One solution is the automated use of classic data mining procedures. However, these also have their disadvantages, such as high costs, complexity, and long project times.

Achieving Goals Fast and Efficiently with Self-Acting Data Mining

With our analysis approach known as Self-Acting Data Mining, we can implement for you a fraud prevention system in a matter of weeks that:

• Automatically and continuously detects and documents new types of fraud and
• Forecasts individual fraud probabilities – for example, for leasing and financing contracts – just a short time after an application has been made

This is how it works:

If you want:

• To be able to predict a project’s success at any time during that project
• Short project times and low cost-risk
• High-quality forecasts, even if the data quality is moderate
• Results displayed in a way that is intuitively understandable
• Ease of use for fraud detection officers

Then e-mail us at This e-mail address is being protected from spambots. You need JavaScript enabled to view it .