Active Learning Fraud Detection for Adaptive Authentication
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Solution Overview
Problem
Existing adaptive authentication techniques rely solely on risk assessment for challenging transactions, which limits the improvement of classifier results and can lead to false positives, and do not effectively address transactions with low confidence scores based on prior knowledge rather than actual data.
Innovation Solution
Implementing an active learning-based fraud detection method that selects transactions for further challenge based on predefined non-risk criteria, such as low riskiness scores and events in buckets with few tagged events, to improve classifier accuracy and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If challenges are applied only to high-risk transactions based on existing classifier results, then the immediate security of risky transactions is improved, but the classifier results for non-challenged events are not necessarily improved and may contain false positives
Solution Approach 1:
The system implements feedback by using active learning to identify transactions that would benefit from challenges based on expected classifier improvements. The challenge results are then fed back to improve the classifier model, creating a continuous learning loop that enhances both immediate security and long-term classification accuracy
Solution Approach 2:
The system performs preliminary action by proactively identifying transactions that should be challenged based on active learning criteria before processing them. This allows the system to pre-determine which transactions will most benefit classifier improvement, rather than reactively challenging only high-risk transactions
2Measurement precision
If the system challenges more transactions to improve classifier accuracy, then the detection accuracy of fraudulent activities is improved, but the system complexity and processing overhead increase
Solution Approach 1:
The system changes parameters by introducing active learning criteria that dynamically determine challenge selection based on multiple factors including expected classifier improvement, transaction characteristics, and model confidence levels. This allows flexible adjustment of challenge frequency to balance accuracy improvement with system complexity
3Productivity
If the system uses only risk-based criteria for challenging transactions, then the processing is simple and fast, but transactions with low confidence scores based on prior knowledge rather than actual data are not effectively addressed
Solution Approach 1:
The system applies dynamics by making the challenge selection criteria adaptive and dynamic rather than static. The active learning mechanism continuously adjusts which transactions are challenged based on changing model confidence levels, data availability, and expected improvement potential, allowing the system to balance speed and precision dynamically
Data Source
AI summary
Active learning-based fraud detection techniques are provided in adaptive authentication systems. An authentication request from an authentication requestor is processed by receiving the authentication request from the authentication requester; comparing current data for the user associated with the user identifier with historical data for the user; generating an adaptive authentication result based on the comparison indicating a likelihood current user data is associated with a fraudulent user; and performing one or more additional authentication operations to improve learning if the request satisfies one or more predefined non-risk based criteria. The predefined non-risk based criteria comprises, for example, (i) the request receiving a riskiness score below a threshold based on current data and wherein the request was expected to have a risk score above a threshold, or (ii) the request being in a bucket having a number of tagged events below a threshold.