Innovative Solution For Dynamic Fraud Detection
ML FDS system contains a machine learning module that provides the second line of defense, identifying new fraud types directly from available data. Thanks to this data-first approach, a system learns continuously and adapts itself to evolving fraud sophistication.
Next generation solution complete three lines of defense for your fraud prevention activities leveraging power of Machine Learning technology.
Multi line fraud detection pipeline ensures high fraud resistance thanks to combining both backward looking approach (blacklists, rules) as well as machine learning as forward looking approach utilizing your data and ML technology.
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Despite implemented rule-based engine and background check for new customers, our client suffered from application fraud (customers not paying for hardware and postpaid services) and was struggling to reduce it.
We have applied our own machine learning fraud detection framework to enhance risk management and empower fraud prevention process with AI.
Our framework was implemented in production, preventing roughly 30% frauds, which secured ROI within 1st year. Measured by a randomized control trial.
Even though insurers have fraud detection process, tools and dedicated employees they felt need to innovate and increase fraud detection efficiency, decrease false positives and protect themselves from evolving fraud activity.
Blindspot leveraged historical claim data applying our machine learning fraud detection framework. As the result, AI models picked up complex patterns from claims, policy and other data enabling reliable fraud detection and whitelisting at the same time.
Blindspot anti-fraud solution applied to historical claim data automated fraud detection process, allowing analysts to concentrate on relevant cases, reducing false positives by 60%.
Results of Machine Learning:
Top 40% sorted by Machine Learning score contains 90% true positives and 48% of all frauds.
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