Compliance Score Model for Illicit Activity Detection
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Solution Overview
Problem
Current systems lack effective methods to determine and enforce compliance in transaction processing, particularly in identifying and addressing illicit activities such as money laundering, where investigators face challenges in determining the legitimacy of transactions involving illegal goods or services.
Innovation Solution
A compliance determination and enforcement platform that utilizes a processor, computer-readable medium, and associated code to evaluate user accounts through a compliance score model based on various factors like age, due diligence, transaction volume, geographical location, and identity verification, flagging non-compliant accounts and enabling corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If investigators manually research parties of transactions to determine illicit activities, then detection accuracy may improve for obvious cases, but productivity decreases and it becomes impractical for complex or offline illegal activities
Solution Approach 1:
The system enables self-service by implementing automated compliance scoring that independently evaluates user accounts without requiring manual investigator research. The compliance score model automatically processes transaction data, user information, and risk factors to generate compliance assessments, allowing the system to serve itself in detecting illicit activities while maintaining both accuracy and high productivity across all accounts including those involving offline illegal goods or services
Solution Approach 2:
The patent replaces the mechanical manual research process with an automated computational system. Instead of investigators manually researching parties of transactions, the system uses computer processors to execute compliance score models that automatically analyze transaction patterns, user data, and risk factors, substituting human mechanical research with automated digital processing that handles both obvious and complex illicit activities efficiently
2Measurement precision
If a compliance score model evaluates multiple factors for each account, then compliance determination accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the compliance evaluation into distinct modular components: a compliance score model that processes multiple factors (transaction patterns, user information, risk indicators) separately and systematically. Each factor is evaluated independently within the model structure, allowing accurate multi-factor analysis while maintaining manageable system complexity through organized segmentation of evaluation criteria and data processing steps
Solution Approach 2:
The compliance score model serves as a universal multi-functional system that handles diverse compliance evaluation requirements through a single integrated platform. It processes multiple account types, various risk factors, and different transaction patterns using the same core model structure, reducing overall system complexity by consolidating what would otherwise require separate specialized systems for each compliance scenario
Data Source
AI summary
A compliance determination and enforcement platform is described. A plurality of factors are stored in association with each of a plurality of accounts. A factor entering module enters factors from each user account into a compliance score model. The compliance score model determines a compliance score for each one of the accounts based on the respective factors associated with the respective account. A comparator compares the compliance score for each account with a compliance reference score to determine a subset of the accounts that fail compliance and a subset of the accounts that meet compliance. A flagging unit flags the user accounts that fail compliance to indicate non-compliant accounts. A corrective action system allows for determining, for each one of the accounts that is flagged as non-compliant, whether the account is bad or good, entering the determination into a feedback system and closing the account.