Dynamic Fraud Detection Strategy Calibration
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Solution Overview
Problem
Presently available tools for fraud detection fall short in providing effective assistance to investigators, particularly in calibrating strategies to search vast records and detecting fraudulent activity in real-time, as they lack the ability to dynamically adjust to changing data and business processes.
Innovation Solution
A method that allows investigators to define, execute, and dynamically calibrate fraud detection strategies using multiple inputs such as thresholds, weighting factors, and parameters, enabling the system to refine its results and apply the calibrated strategy to new records, potentially halting fraudulent activities in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If investigators manually review vast amounts of records to detect fraud, then detection thoroughness is improved, but time consumption and productivity deteriorate
Solution Approach 1:
The patent replaces manual investigator review with an automated fraud detection system that uses machine learning models and algorithms to analyze records, thereby maintaining detection thoroughness while dramatically improving investigation speed and productivity
Solution Approach 2:
The patent introduces a fraud detection system as an intermediary between raw records and investigator analysis, using trained models to pre-process and flag suspicious records, which improves both detection accuracy and reduces the time investigators need to spend on manual review
2Device complexity
If fixed fraud detection strategies are used, then system simplicity is maintained, but adaptability to changing fraud patterns deteriorates
Solution Approach 1:
The patent implements dynamic fraud detection strategies where models are continuously retrained and adjusted based on new fraud patterns and historical data, allowing the system to adapt to changing conditions while maintaining manageable complexity through automated processes
Solution Approach 2:
The patent changes detection parameters and model configurations based on evolving fraud patterns, using historical data to automatically adjust thresholds, weighting factors, and detection rules, thereby maintaining adaptability without requiring complete system redesign
3Measurement precision
If detection sensitivity is increased to catch more fraud, then fraud detection capability is improved, but false positive rate increases
Solution Approach 1:
The patent adjusts detection parameters such as thresholds and weighting factors based on historical data and performance metrics, optimizing the balance between detecting fraud and minimizing false positives through data-driven parameter tuning
Solution Approach 2:
The patent implements feedback mechanisms where detection results and investigator confirmations are used to continuously refine detection models and adjust sensitivity parameters, thereby improving fraud detection while reducing false positives over time through iterative optimization
Data Source
AI summary
A method of determining potentially fraudulent records in a database comprises defining a detection strategy. The detection strategy is targeted to detect existing records from the database and comprises multiple inputs. The detection strategy is executed on existing records and results are displayed for review by a user. The detection strategy is then dynamically calibrated as desired based on input received from the user, and any modified results are displayed. A calibrated detection strategy is set, and then it is executed on new records to detect potentially fraudulent records warranting investigation. A computer system having a processor and memory storing instructions for performing such methods is also described. A computer readable storage medium having computer-executable instructions for performing such methods is also described.