AI Microservices for Roll Forward Amount Determination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining a roll forward amount in accounting are rigid and lack adaptability, particularly when dealing with uncertain record linkage and 'fuzzy' reconciliation scenarios, failing to provide robust information when a large portion of data values are not fully reconciled.
Innovation Solution
The implementation of AI-augmented composable and configurable microservices that identify reconciled and non-reconciled data values, assign confidence scores, and compute a roll forward amount within a composable framework, enabling processing of varied datasets from multiple sources and triggering human review or data retrieval as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic roll forward is used with strict reconciliation requirements, then data integrity is improved, but adaptability to fuzzy reconciliation scenarios deteriorates
Solution Approach 1:
The system dynamically adjusts the reconciliation threshold based on the situation. When data quality is high, it uses strict deterministic reconciliation; when data quality is lower or uncertainty exists, it transitions to probabilistic reconciliation with confidence scores. This dynamic adjustment allows the system to maintain data integrity when possible while adapting to fuzzy scenarios when necessary.
Solution Approach 2:
The patent changes the parameter of reconciliation strictness from fixed to variable. By introducing confidence scores and probabilistic matching parameters, the system can adjust the level of strictness based on data characteristics, enabling it to handle both highly structured and fuzzy data scenarios effectively.
2Measurement precision
If only fully reconciled data values are used for roll forward determination, then accuracy is improved, but productivity deteriorates due to inability to process uncertain data
Solution Approach 1:
Instead of requiring complete reconciliation of all data values, the system performs partial reconciliation by processing data values to varying degrees of confidence. It accepts partially reconciled data with appropriate confidence scores, allowing productivity to improve while maintaining acceptable accuracy through the confidence metric.
Solution Approach 2:
The system provides feedback through confidence scores that indicate the quality of each roll forward determination. This feedback mechanism allows users to understand the reliability of results and take appropriate actions, enabling the system to process more data efficiently while maintaining accountability for accuracy.
3Reliability
If rigid hardwired processes are used for specific ledgers, then reliability of specific processes is improved, but adaptability to varied data sources deteriorates
Solution Approach 1:
The patent creates a universal roll forward determination system that can handle multiple data sources and ledger types through a common probabilistic reconciliation framework. The confidence score mechanism and flexible matching algorithms allow the same process to reliably handle diverse data scenarios without requiring separate hardwired processes for each ledger.
Solution Approach 2:
The system dynamically adapts its reconciliation approach based on the characteristics of the data being processed. Rather than using fixed rigid processes, it adjusts its matching criteria and confidence thresholds according to the specific data source and scenario, maintaining reliability across varied applications.
4Measurement precision
If comprehensive reconciliation of all data values is performed, then measurement precision is improved, but loss of time deteriorates
Solution Approach 1:
The system performs reconciliation to the extent necessary rather than attempting complete reconciliation of all data values. By accepting partial matches with confidence scores, it achieves sufficient precision for decision-making without the time cost of exhaustive reconciliation of every possible data value.
Data Source
AI summary
Provided herein are methods and systems for determining a roll forward amount. The method can include identifying a starting balance, extracting reconciled data values from a database, each reconciled data value having a confidence score that indicates a confidence level that a reconciled data value of a first dataset is the same as a corresponding reconciled data value of a second dataset. The method can include identifying a first subset of the reconciled data values that are a first type and a second subset of the reconciled data values that are a second type, and determining a roll forward amount based on the starting balance, the first subset, and the second subset.