ML FDS: Discover frauds in your data, using machine learning, so you can act immediately.
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Key Benefits
Improved fraud detection accuracy, therefore, less time needed for fraud management
A steep decrease in false positives, resulting in a decrease in costs per fraud
Ability to organically learn and adapt to new data, without the need for manual rules
Assessors can focus on more complex frauds, with better efficiency and improved bottom line
The second line of defense against frauds
Your rule-based system for fraud detection usually suffers from high false-positive rates, taking a toll on your employees. It is also vulnerable to complex fraud patterns. A machine-learning fraud detection system (ML FDS) acts as an additional layer of security on top of a rule-based system, making it easy and cost-effective.
Letting the data speak for itself
ML FDS helps you to spot complex fraud patterns and prevent them in the future, in a robust, precise, and resistant fraud prevention. No need for manual management of rules and blacklists. ML FDS evaluates customer data and creates microsegments, such as the microsegment of proven insurance fraud, to evaluate if a new insurance event falls into this group. The solution adapts to evolving fraud sophistication, resulting in high accuracy in fraud detection over time.
Decide on new data, fast
Aberrations identified by ML FDS are presented in customizable, intuitive dashboards, facilitating quick, data-driven decision making and efficient fraud resolution. Assessors can thus focus on more complex frauds, with better efficiency and improved bottom line. Thanks to the immediate impact on efficiency, return on investment into ML FDS occurs typically in the short to medium-term horizon.
Client Case-Study
Mobile provider reduced application fraud by 30%
Despite an implemented rule-based system and background check for new customers, one of the top Czech mobile providers suffered from subscription fraud (customers not paying for services and hardware) and was struggling to reduce it. Thanks to using ML FDS to enhance risk management and empower the fraud prevention process with AI, they prevented roughly 30% of frauds, which secured ROI within the 1st year already.
%
ML FDS instantly prevented around 30% of subscription frauds and adopts itself automatically as new data arrives.
Work smarter, as others do
"We had been thinking about applying artificial intelligence to innovate our fraud detection process. ML FDS supported us in this initiative and demonstrated how machine learning can uplift our fraud detection efficiency."
Jan Řezníček, Head of Claims, ČSOB Pojištovna, member of KBC Group
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