Stop Fraud Before It Happens.
A real-time fraud detection platform that evaluates hundreds of transaction signals in milliseconds to reduce losses without creating unnecessary friction for legitimate customers.
The business problem and the software solution.
THE BUSINESS CHALLENGE
An online marketplace was losing roughly $2M a year to fraudulent transactions, account abuse, and manual review inefficiencies. Existing rules-based tools were catching too little fraud while still flagging a large number of legitimate customers. The business needed faster, more accurate fraud detection that could adapt to new attack patterns without overwhelming the operations team.
THE SOLUTION WE DELIVERED
We built an AI-driven fraud prevention system that scores transactions in real time using behavioral, transactional, and contextual signals. The platform combines machine learning models with explainable rules so risk teams can understand why activity is flagged and tune thresholds with confidence. It was deployed in shadow mode first, then moved into active blocking once false positives were reduced to an acceptable level.
How the project was planned, built, and launched.
Fraud Pattern Study
Mapped more than 340 fraud patterns and reviewed historic chargebacks, disputes, and manual review outcomes.
Detection Engine
Built a scoring engine that evaluates 200+ transaction and behavior signals in under 50ms.
False Positive Reduction
Calibrated thresholds with the risk team to balance fraud prevention against customer experience.
Shadow Mode First
Ran the model in parallel before switching on active blocking and workflow automation.
Core capabilities delivered for the client.
FEATURES
Real-Time Fraud Detection
Risk is evaluated before a transaction completes, not hours later in a manual queue.
Adaptive Intelligence
The models evolve as new fraud behaviors and marketplace abuse patterns emerge.
Lower False Positives
Legitimate customers are less likely to be blocked or delayed unnecessarily.
Clear Audit Trails
Risk teams can review the factors that influenced every automated decision.
Fraud losses dropped by 95%, and our team finally trusted the system enough to move faster.
What the client achieved after launch.
Annual fraud losses dropped from $2M to under $100K while the business also reduced false positives and manual review load.
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