SAR Narrative Generation From Tabular AML Transaction Data
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Solution Overview
Problem
Current systems in financial institutions struggle with transforming tabular data from AML alerts into understandable narratives for SARs, leading to inconsistent and incomplete reports, which can result in costly fines and hinder effective law enforcement efforts.
Innovation Solution
A computerized method using a convolutional Seq2Seq model with a hierarchical attention mechanism and NLGSimple model to automatically generate readable SARs from high-dimensional tabular data, handling various transaction types and long sequences while preserving accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tabular data is used to present AML alerts, then data structure and completeness are maintained, but readability and understandability deteriorate
Solution Approach 1:
The patent introduces an intermediate processing layer (NLG system with Seq2Seq model and attention mechanism) that transforms structured tabular data into natural language narratives. This intermediary component bridges the gap between machine-readable tabular format and human-readable narrative format, preserving data completeness while improving readability.
2Adaptability or versatility
If manual writing of SARs is performed, then flexibility and context understanding are maintained, but consistency and productivity deteriorate
Solution Approach 1:
The system enables automated self-service generation of SAR narratives by training the NLG model on historical SAR data and transaction patterns. The model learns to automatically understand context and generate consistent narratives without human intervention, improving both productivity and consistency while maintaining adaptability through continuous learning.
Solution Approach 2:
The system incorporates feedback mechanisms where the NLG model is trained on historical SAR narratives and continuously improved based on performance metrics and user feedback. This feedback loop ensures the model maintains context understanding while improving consistency and productivity over time.
3Adaptability or versatility
If manual SAR writing is performed, then narrative customization is possible, but time consumption and resource expenditure increase
Solution Approach 1:
The automated NLG system performs narrative customization automatically by learning from historical data and adapting to different transaction types and suspicious activity patterns. This eliminates manual time expenditure while maintaining narrative customization capabilities through intelligent pattern recognition and generation.
4Measurement precision
If complex tabular data is presented to AML personnel, then data accuracy is maintained, but interpretation difficulty increases
Solution Approach 1:
The NLG system acts as an intermediary that preserves data accuracy from tabular sources while transforming it into naturally readable narratives. The attention mechanism ensures that important features and relationships in the tabular data are accurately captured and expressed in natural language, reducing interpretation difficulty without sacrificing accuracy.
Data Source
AI summary
A computerized-method for automatically generating a two-part readable Suspicious Activity Report (SAR) from high-dimensional data in tabular form is provided herein. The computerized-method may include receiving high-dimensional data in tabular form of evidence financial transactions to be reported under Anti Money Laundering (AML) regulations. Then, displaying the received data to a Subject Matter Expert (SME) for ordering each displayed transaction in a predefined construction; Then, training one or more Natural Language Generation (NLG) translation models, for each transaction type, according to a deep learning model. Then, operating the one or more NLG translation models on each transaction type to generate for each transaction type a narrative of SAR; Then, operating a prebuilt summary model on the generated narrative of SAR of each transaction type to generate a summary of the plurality of narratives of SAR; and combining the plurality of narratives of SAR and the summary to one SAR.