AI Transaction Reconciliation Using Dynamic Risk Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing transaction reconciliation systems rely on static rules, which do not consider transaction-level and account-level risks, leading to inefficiencies and resource wastage due to manual review of high-risk transactions and accounts.
Innovation Solution
An AI-based transaction data processing and reconciliation system that employs anomaly detection, R-tag prediction, and aging prediction models to dynamically assess transaction and account risks, generating combined risk scores for efficient prioritization and automatic reconciliation or certification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static rules are used for transaction reconciliation, then system simplicity is maintained, but reconciliation efficiency and accuracy deteriorate due to inability to identify high-risk transactions
Solution Approach 1:
The patent applies dynamics by transitioning from static reconciliation rules to dynamic risk-based rules. The system continuously updates risk scores for transactions and accounts based on real-time data analysis, allowing the reconciliation process to adapt dynamically to changing risk patterns rather than relying on fixed, predetermined rules.
Solution Approach 2:
The patent implements preliminary action through pre-reconciliation risk assessment. Before the actual reconciliation process begins, the system pre-identifies high-risk transactions and accounts using machine learning models, allowing resources to be prioritized and allocated in advance rather than reacting to issues during reconciliation.
2Reliability
If manual review of all transactions is performed, then reconciliation accuracy is improved, but time consumption and resource wastage increase significantly
Solution Approach 1:
The patent applies local quality by differentiating the level of review applied to different transactions based on their risk profiles. High-risk transactions receive intensive manual review and scrutiny, while low-risk transactions undergo automated processing with minimal or no manual intervention, optimizing the allocation of human resources across the transaction portfolio.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated machine learning-based risk assessment systems. The ML models automatically score and prioritize transactions, substituting human judgment for routine risk evaluation while preserving human review for complex, high-risk cases that require nuanced decision-making.
3Adaptability or versatility
If static reconciliation rules are used, then system implementation is straightforward, but the ability to adapt to changing transaction patterns and risks deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the reconciliation system continuously learns from actual transaction outcomes and risk events. The machine learning models are retrained periodically using feedback from resolved discrepancies and identified risks, allowing the system to adapt to emerging fraud patterns and transaction anomalies while maintaining manageable complexity through iterative improvement.
Data Source
AI summary
An Artificial Intelligence (AI) based transaction data processing and reconciliation system analyzes transaction data of different accounts to determine anomalous transactions, tagged transactions with Required Adjustments tag (R-tag), or aging transactions. Different Artificial intelligence (AI) based models are trained to produce corresponding risk scores that enable the determinations. Those transactions having low-risk scores are automatically reconciled whereas transactions having higher risk scores can be flagged for further review. Furthermore, the accounts corresponding to the transactions are also analyzed via different AI-based account-level models to identify accounts that can be R-tagged and/or accounts that are at the risk of being de-certified. Those accounts with higher risk scores can be flagged for further review while accounts with lower risk scores can be automatically certified.