Financial Record Trace Verification Using External Data
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Solution Overview
Problem
Existing auditing methods rely on random sampling, which leaves problematic entries undetected, undermining the trustworthiness of financial records.
Innovation Solution
A trace-based data verification process that analyzes associations across multiple financial records and compares them to independently-sourced external data to validate entries, using machine learning techniques to identify traces and determine confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random sampling is used to verify financial records, then the auditing process is efficient and quick, but problematic entries may remain undetected reducing trustworthiness
Solution Approach 1:
The system segments the auditing process into multiple independent verification paths by distributing financial record entries across multiple blockchain nodes. Each node independently verifies entries using the same cryptographic proofs, creating parallel verification channels that increase both efficiency and reliability without requiring centralized review of every entry.
Solution Approach 2:
The system implements continuous feedback mechanisms where verification results from blockchain nodes are constantly monitored and fed back to the auditing system. When discrepancies or problematic entries are detected by any node, the feedback loop triggers immediate re-verification and alert mechanisms, ensuring high reliability while maintaining efficient processing of normal entries.
2Reliability
If all entries in financial records are verified, then trustworthiness is maximized, but the auditing process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary verification actions by having blockchain nodes continuously monitor and validate transactions as they are recorded on the blockchain. This preliminary action ensures that most entries are already verified before the auditing process begins, allowing the audit to focus only on exceptional cases or high-risk areas, thereby maximizing reliability without proportionally increasing auditing time.
Solution Approach 2:
The system applies partial verification to the majority of entries through blockchain's inherent consensus mechanisms, while applying excessive (comprehensive) verification only to specific high-risk or anomalous entries. This differentiated approach ensures overall trustworthiness while minimizing the time loss associated with verifying every single entry in detail.
3Reliability
If cryptographic proofs are distributed across multiple blockchain nodes, then verification reliability increases, but system complexity increases
Solution Approach 1:
The system uses universal cryptographic proof mechanisms that can be implemented identically across all blockchain nodes. Each node performs the same verification functions using standardized protocols, which increases reliability through distributed verification while avoiding the complexity that would arise from node-specific custom verification logic. The multi-functionality of blockchain nodes (simultaneously serving as transaction processors, validators, and audit participants) further reduces overall system complexity.
Data Source
AI summary
The present disclosure is directed to transforming a data set of discrete records, tracing unrelated entries across the records, and verifying the traces using independently-sourced external data. In one aspect, a system includes memory and one or more processors configured to execute the computer-readable instructions to receive a first set of data, the first data set including multiple discrete financial records of an entity; apply a set of logics to the first data set to identify a plurality of traces, each of the plurality of traces associating discrete entries across one or more of the multiple financial records; performing a verification process to verify the plurality of traces against a second data set for the entity, the second data set being independently sourced from a third party entity to yield a verification result; and prepare an output of the verification result to be presented on a display.