MACHINE LEARNING DEFENSE AGAINST FRAUDfsda

Fraud and misconduct are a risk for every business. Expert or rule-based solutions provide the first line of defense, detecting fraud cases based on predefined criteria. While being simple and straightforward, this approach often suffers from high false-positive rates and, more importantly, is not resistant to intricate fraud patterns.

We developed a modern automated solution to help you detect fraudulent events in various data and businesses, be it application fraud, insurance fraud, accounting fraud, or others. Our machine learning module provides a second line of defense, identifying new fraud types from data directly. Thanks to this data-first approach, this system learns continuously and adapts itself to evolving fraud sophistication.

LET YOUR DATA SPEAK FOR ITSELF

Empowered by our technology, your fraud management is taken one step further. Our framework works in synergy with your rule-based system as an additional security layer on top, making it easy and cost-effective. Machine learning looks at data from multiple angles, spots complicated fraud patterns, and prevents them in the future. As a result, your fraud prevention is robust, precise, resistant, and adaptive.

EFFICIENT FRAUD SOLVING

Aberrations identified by ML FDS are presented in customizable, intuitive dashboards, facilitating quick data-driven decision making and efficient fraud solving. Assessors can then focus on more complex frauds, with better efficiency and improved bottom line as a result.

FAST RETURN ON INVESTMENT

Thanks to the immediate effect on the efficiency, return on investment into the ML FDS system typically occurs quickly, regardless of the business area.

Key numbers and benefits

RAPID FRAUD DETECTION

After deployment of ML FDS, fraud detection and investigation time reduces from days to minutes or seconds.

ABILITY TO LEARN

The ability to learn enables ML FDS to adapt without the need for manual management.

MORE ACCURATE FRAUD DETECTION

With ML FDS integrated into your business, up to 60% of false positives are decreased, and the fraud detection accuracy increases up to 76%.

Addressable use cases

1.

ERRORS AND MISMATCHED RECORDS IN HISTORICAL TRANSACTIONS

2.

HIDDEN RISK MANAGEMENT

3.

ACCOUNTING REPORTS PROCESSING

4.

ATTENDANCE REPORTS AND PERFORMANCE PROCESSING

5.

FRAUDS AND MISCONDUCT BEHAVIOR DETECTION

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