ML Decision Tree Ensemble for Fraud Detection
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Solution Overview
Problem
Current analytics and rules-based monitoring solutions for detecting unauthorized digital activities in digital transactions are reactive, prone to false positives, and limited in scope, struggling with scalability and maintenance overhead, and fail to detect sophisticated or emerging digital transaction schemes.
Innovation Solution
A system utilizing a machine learning model trained on historical transaction data to generate an ensemble of decision trees, which extracts rules for a proactive risk management system to detect fraud in real-time, with a trees model that reduces and refines rules for effective implementation, and incorporates user feedback for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based monitoring solutions are used to detect unauthorized digital activities, then detection capability is provided, but the system suffers from high false positives and requires significant maintenance overhead
Solution Approach 1:
The machine learning model automatically learns and updates detection patterns from historical transaction data, enabling the system to self-improve detection accuracy without requiring manual rule updates. The model autonomously adapts to new fraud patterns, reducing maintenance overhead while maintaining high detection reliability.
Solution Approach 2:
The system transitions from static rules-based parameters to dynamic machine learning models that continuously adjust detection parameters based on learned patterns from historical data. This allows the system to adapt detection thresholds and patterns automatically, reducing false positives and maintenance requirements.
2Productivity
If the volume of transactions increases, then more transactions can be processed, but performance bottlenecks occur due to evaluating each transaction against expanding rules
Solution Approach 1:
The machine learning model is pre-trained on historical transaction data before deployment, performing the complex learning task in advance. During real-time transaction evaluation, the pre-trained model provides rapid predictions without requiring on-the-fly rule generation, thus handling high transaction volumes efficiently without time bottlenecks.
3Ease of manufacture
If static rules are used for fraud detection, then implementation is straightforward, but the system cannot detect sophisticated or emerging fraud schemes
Solution Approach 1:
The system replaces static rules with dynamic machine learning models that continuously adapt to new fraud patterns. The model learns from historical data and evolves its detection capabilities over time, enabling it to detect sophisticated and emerging fraud schemes while maintaining ease of implementation through automated training pipelines.
4Adaptability or versatility
If predefined rules are expanded to cover more fraud types, then detection scope increases, but false positives increase and operational efficiency decreases
Solution Approach 1:
The machine learning model incorporates feedback from historical transaction outcomes and model predictions to continuously refine its detection patterns. This feedback mechanism allows the system to learn from false positives and adjust its detection logic, maintaining broad detection scope while minimizing false alarms through iterative improvement.
Data Source
AI summary
A model engine is provided for managing transactions for one or more computing devices over a network. There is provided a machine learning model trained and tested on historical transaction data including labelled fraud data, to provide a target signal indicative of a likelihood of fraud within a given transaction, generating an ensemble of decision trees. The engine further includes extracting a set of rules by traversing each tree in the ensemble of decision trees from a root node to each leaf node of the tree, each path from the root node to a particular leaf node including splitting criterion providing a rule to form a set of rules. A proactive risk management system is also provided for applying the set of rules to a new transaction and when at least one rule is met, triggering predetermined actions on at least one computing device on the network.