Compliance Score Model for Transaction Processing
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Solution Overview
Problem
Current systems lack effective methods to determine and enforce compliance in transaction processors, particularly in identifying and addressing illicit activities such as money laundering, where parties involved in transactions may not be easily identifiable.
Innovation Solution
A compliance determination and enforcement platform that utilizes a processor, computer-readable medium, and associated code to assess 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 manual investigation methods are used to determine illicit activities, then investigator expertise can identify obvious illegal transactions, but it becomes impractical or impossible to detect hidden illicit activities in large volumes of transactions
Solution Approach 1:
The patent replaces manual investigator analysis with an automated compliance scoring system that uses computational algorithms to evaluate transaction data. The system automatically calculates compliance scores based on multiple factors including transaction patterns, user behavior, and risk indicators, enabling high-volume processing while maintaining consistent detection accuracy without human intervention limitations
Solution Approach 2:
The patent creates a virtual model of compliance assessment by generating compliance scores that replicate the decision-making process investigators would use. The scoring system copies and formalizes investigative criteria into computable metrics, allowing the system to evaluate transactions using standardized rules that mirror manual investigation methodologies while scaling to large datasets
2Reliability
If comprehensive compliance checking is performed on all user accounts, then detection of non-compliant accounts improves, but system complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the compliance assessment into a quantitative scoring system where multiple compliance factors are converted into numerical values that can be aggregated and compared. By changing the parameter representation from qualitative judgments to quantitative scores, the system simplifies the complexity of comprehensive compliance checking while maintaining reliability through mathematical aggregation of risk indicators
Solution Approach 2:
The patent divides the compliance assessment into discrete factor components, each evaluating a specific aspect of account behavior or characteristics. The system segments the overall compliance determination into multiple independent scoring factors that can be calculated and weighted separately, then combined to produce an overall compliance score, making the complex assessment process manageable and systematic
3Reliability
If corrective actions are taken for all potentially non-compliant accounts, then compliance enforcement improves, but false positives may lead to unnecessary account restrictions
Solution Approach 1:
The patent implements a threshold-based approach where corrective actions are triggered only when compliance scores fall below a predetermined threshold, rather than acting on all potentially non-compliant accounts. This partial action approach filters out false positives by requiring scores to meet specific criteria before enforcement actions are taken, balancing compliance effectiveness with avoidance of unnecessary restrictions
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.