Event Sourcing Framework for Mortgage Data Integrity
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Solution Overview
Problem
Current Loan Origination Systems (LOS) fail to track key data elements and decision-making processes in mortgage lending, leading to irreproducible decisions and data integrity issues, which complicates regulatory compliance and risk mitigation in the mortgage industry.
Innovation Solution
Implementing a framework based on the Command Query Responsibility Segregation (CQRS) and Event Sourcing design pattern that writes events to an immutable store, allowing for granular data tracking and reproducibility of decisions, and includes a four-step verification process to ensure data quality and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current Loan Origination Systems track only final data values in static logs, then system simplicity is maintained, but data integrity and decision reproducibility deteriorate
Solution Approach 1:
The system performs preliminary actions by capturing and storing every data change event as it occurs, including the previous value, new value, timestamp, and user information. This creates a complete audit trail before any analysis or review is needed, ensuring data integrity is maintained from the outset rather than attempting to restore it later.
Solution Approach 2:
The system creates copies of data at each state change by storing the previous value alongside the new value in the audit log. This allows the system to preserve historical data states without modifying the current operational data, enabling full reproducibility of past decisions while maintaining system simplicity.
2Measurement precision
If manual data entry is used to track loan decisions, then data accuracy can be maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs self-service by automatically capturing all data changes through its event sourcing mechanism. When data is modified in the immutable store, the system automatically logs the change with metadata including timestamp, user, and previous values, eliminating the need for manual tracking while maintaining complete audit trails.
Solution Approach 2:
The system implements feedback by providing real-time visibility into data changes through the audit log. Reviewers can immediately see what changes were made, when, and by whom, enabling rapid verification and reducing the need for manual re-checking of loan decisions.
3Reliability
If complete audit trails are maintained for every data change, then decision reproducibility is improved, but device complexity and storage requirements worsen
Solution Approach 1:
The system segments the audit trail into discrete, immutable events stored in the event store. Each event represents a single data change with specific metadata, allowing the system to maintain complete reproducibility while organizing data in a manageable, queryable structure that doesn't overwhelm system complexity.
4Ease of operation
If static logging is used to track data changes, then ease of operation is maintained, but loss of information about historical values increases
Solution Approach 1:
The system maintains continuity of useful action by continuously capturing and storing every data change event as it occurs. The audit log continuously accumulates historical information without interruption, ensuring that no data changes are lost while maintaining ease of operation through automated tracking.
Data Source
AI summary
Described are methods, systems, and media for enriching data records while preserving data provenance to enable historical data enrichment process replay.