Dynamic Alert Prioritization for Financial Crime Investigation
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Solution Overview
Problem
Financial crimes investigation units face challenges in timely review of transactions due to high volumes, leading to potential fraudulent transactions being allowed and legitimate ones being delayed, causing financial and reputational losses.
Innovation Solution
A system dynamically prioritizes alerts based on risk levels by classifying users into tiers based on attributes and assigning priority levels, allowing for real-time adjustment of staffing to accommodate peak volumes while maintaining risk mitigation, by using an analysis manager, status manager, and interface component to output interaction and priority levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If investigators review all transactions manually, then fraud detection accuracy is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent segments transactions into different risk tiers (low, medium, high risk) based on analysis of transaction attributes and historical data. This segmentation allows investigators to focus manual review efforts on high-risk transactions while automated processing handles low-risk transactions, thereby maintaining fraud detection accuracy for critical cases while significantly improving overall processing throughput.
Solution Approach 2:
The system dynamically changes the review threshold parameter based on volume conditions. During peak volumes, the threshold is adjusted to automatically approve more low-risk transactions, reducing investigator workload. During lower volumes, the threshold tightens to ensure more thorough review, maintaining high detection accuracy when capacity allows.
2Productivity
If more investigators are hired to handle peak volumes, then productivity is improved, but operational cost increases
Solution Approach 1:
The patent implements dynamic alert volume adjustment that automatically scales the number of alerts presented to investigators based on real-time capacity and risk conditions. During peak periods, the system dynamically reduces alert volume by applying more aggressive risk-based filtering and tiering, allowing existing staff to maintain productivity without additional hiring. This dynamic adjustment eliminates the need for static over-staffing to handle peak loads.
Solution Approach 2:
The system employs automated risk assessment and tiering mechanisms that self-adjust transaction routing without human intervention. Low-risk transactions are automatically approved or routed to automated monitoring, while high-risk transactions are prioritized for investigator review. This self-service capability reduces reliance on additional human resources during peak volumes.
3Ease of operation
If all transactions are reviewed with equal priority, then fairness is improved, but critical high-risk transactions may be delayed
Solution Approach 1:
The patent assigns different priority levels and review characteristics to different transaction tiers based on their risk profiles. High-risk transactions receive immediate attention with high priority flags and are presented first to investigators. Low-risk transactions are processed with standard or reduced priority. This local differentiation of quality ensures that critical transactions receive the attention they need while maintaining efficient overall processing.
Solution Approach 2:
The system performs preliminary risk assessment and tiering before transactions reach investigators. By pre-classifying transactions into risk tiers and pre-prioritizing high-risk cases, the system ensures that when investigators begin their review, high-risk transactions are already positioned at the top of their work queues, eliminating delays in addressing critical cases.
4Productivity
If risk-based prioritization is implemented, then fraud detection efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional risk scoring system that evaluates multiple transaction attributes (amount, frequency, location, counterparty, historical behavior) through a single integrated scoring mechanism. This universal approach consolidates what would otherwise require multiple separate analysis systems into one cohesive tiering engine, managing complexity while maintaining comprehensive fraud detection capabilities across diverse transaction types.
Data Source
AI summary
Intraday alert volume adjustment based on risk parameters is disclosed. A set of alerts associated with user transactions can be displayed to one or more human reviewers for processing. The set of alerts can be analyzed to determine whether or not the volume of alerts exceeds a threshold capacity level, which is based on a number of human reviewers available to process alerts. If the volume of alerts exceeds the threshold capacity level, alerts in the set of alerts can be prioritized based on risk in descending order and displayed to the human reviewers. In one instance, alerts below a threshold can be hidden from view of the human reviewers and optionally automatically approved. Further, prioritization can be performed dynamically as new alerts are added to the set.