Adaptive Fraud Analytics Variable Creation
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Solution Overview
Problem
Existing fraud detection models degrade over time due to changing data distributions, and rely on manually created feature variables that limit their ability to adapt to new fraud patterns, leading to performance degradation and increased false positives.
Innovation Solution
An adaptive analytics system that automatically creates variables in real-time using decision tree algorithms to generate binary indicator variables from raw transaction inputs, allowing the model to continuously update and improve fraud detection without relying on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual variable creation is used, then domain expertise is leveraged, but the ability to adapt to new fraud patterns is limited
Solution Approach 1:
The system enables automatic variable creation where the model itself generates variables from raw transaction data without requiring manual intervention from domain experts. The automated variable creator uses algorithms to discover meaningful variables and features directly from the data, allowing the system to self-adapt to new fraud patterns while reducing the complexity burden on human operators.
Solution Approach 2:
The system dynamically changes variables based on detected fraud patterns and data distributions. When new fraud tactics are identified, the automated variable creator generates new variables or modifies existing ones to capture these evolving patterns, allowing the model to adapt its parameter structure in response to changing conditions without manual reconfiguration.
2Reliability
If static model variables are used, then model stability is maintained, but performance degrades over time due to changing data distributions
Solution Approach 1:
The system transitions from static model variables to dynamic variables that can evolve over time. The automated variable creator continuously monitors data distributions and generates updated variables when changes are detected, allowing the model to maintain high performance by adapting its variable structure to current data characteristics while preserving stability through controlled, data-driven updates.
Solution Approach 2:
The system implements feedback mechanisms where model performance and data distribution changes are continuously monitored and fed back into the automated variable creation process. This feedback loop enables the system to detect when existing variables become less effective and automatically generate new variables to restore optimal performance, thereby maintaining reliability over extended periods.
3Productivity
If automated variable creation is implemented, then real-time adaptation is enabled, but computational complexity increases
Solution Approach 1:
The system performs preliminary variable creation and feature engineering activities during model training or offline processing periods. The automated variable creator pre-processes raw data and generates candidate variables before they are needed for fraud detection, allowing real-time adaptation without the computational burden of creating variables on-demand during critical detection operations.
Solution Approach 2:
The variable creation process is segmented into distinct stages: data processing, variable generation, validation, and model integration. This segmentation allows computational tasks to be distributed across different time periods and processing layers, reducing the computational complexity burden on any single operation while maintaining real-time adaptability through staged updates.
4Measurement precision
If traditional offline model building is used, then thorough validation is possible, but the system cannot respond to evolving fraud tactics in real-time
Solution Approach 1:
The system maintains continuous variable creation and model updating operations that run alongside traditional validation processes. The automated variable creator continuously generates and validates new variables based on current data, ensuring that the system is always ready to respond to new fraud patterns without requiring complete model revalidation, thus eliminating the trade-off between validation thoroughness and response speed.
Data Source
AI summary
A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.