Machine Learning Model for Regulatory Violation Detection
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Solution Overview
Problem
Banks face challenges in efficiently managing and consolidating vast amounts of data across different divisions, leading to potential regulatory violations and inefficiencies in data usage.
Innovation Solution
A method utilizing a machine learning model to process data from multiple databases stored in a common repository, identifying potential regulatory violations, and transmitting alerts for remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If banks manually manage and consolidate data across different divisions, then data accuracy for regulatory compliance can be maintained, but the time and resources required increase significantly
Solution Approach 1:
The system enables automated self-monitoring of regulatory compliance through machine learning models that continuously analyze data across divisions without human intervention. The ML model automatically detects potential violations and generates reports, allowing the system to serve its own compliance monitoring function without requiring manual data consolidation efforts.
Solution Approach 2:
The patent replaces manual mechanical data consolidation processes with automated machine learning-based analysis. Instead of human personnel manually collecting, consolidating, and analyzing data from multiple divisions, the system uses ML algorithms to automatically process and interpret data across the enterprise data lake, significantly reducing time while maintaining compliance accuracy.
2Reliability
If banks store data in separate divisional databases, then data security and control can be maintained, but redundant data storage increases costs
Solution Approach 1:
The system creates a universal enterprise data lake that serves multiple divisions simultaneously while maintaining their individual security requirements. The ML model is designed to analyze data from diverse sources (retail banking, commercial lending, investment banking) using the same platform, eliminating redundant storage while preserving divisional data security through controlled access mechanisms.
Solution Approach 2:
Instead of storing complete copies of data in each divisional database, the system creates selective copies or references to data in the centralized enterprise data lake. The ML model can access and analyze data where needed without requiring full duplication, reducing redundant storage while maintaining data availability and security through controlled copying mechanisms.
3Device complexity
If banks use traditional data analysis methods, then implementation complexity can be kept low, but the ability to detect regulatory violations decreases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the raw data in the enterprise data lake and the compliance analysis process. These ML models serve as mediators that automatically interpret complex data patterns and identify potential regulatory violations, enhancing detection capability while managing complexity through specialized intermediary algorithms rather than requiring complex manual analysis systems.
4Measurement precision
If banks increase manual review of data for compliance, then violation detection accuracy improves, but processing efficiency decreases
Solution Approach 1:
The machine learning model performs automated self-analysis of compliance data with high accuracy, eliminating the need for manual review while maintaining or improving detection precision. The system independently identifies potential violations through pattern recognition and anomaly detection algorithms, achieving both high accuracy and efficient processing without human intervention.
Solution Approach 2:
The patent replaces manual compliance review processes with automated machine learning-based analysis. The ML model processes large volumes of data from the enterprise data lake much faster than manual reviewers while maintaining or improving detection accuracy through advanced pattern recognition, thereby substituting mechanical human review with automated computational analysis that achieves both precision and productivity.
Data Source
AI summary
A method for determining whether an entity is violating regulatory requirements using a machine learning model. The method includes providing a repository having a plurality of databases each storing data and information where the data and information is available to authorized users, updating and revising the plurality of databases as the data and information stored therein changes and new data and information becomes available by the users, processing the databases and the data and information contained therein using the machine learning model to determine whether the entity is potentially violating regulatory requirements, and transmitting a communication identifying that the entity is violating regulatory requirements, where remedial steps could then be taken to correct the violation of the regulatory requirements.