LLM Fraud Risk Analysis for Faster AML Investigations
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Solution Overview
Problem
Current anti-money-laundering (AML) investigations are hindered by the complexity and inefficiency of handling large volumes of data from disparate sources, leading to high costs and slow response times, particularly in the context of suspicious activity reporting.
Innovation Solution
A fraud investigation digital assistant system utilizing a conversational large language model (LLM) that integrates structured and unstructured data from various sources, including proprietary and public databases, to provide natural-language responses on the trustworthiness of entities, automating data merging, chunking, embedding, and query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data gathering and analysis methods are used, then investigators can thoroughly evaluate suspicious activities, but investigation time and costs increase significantly
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between investigators and vast data sources. The AI assistant automatically gathers, processes, and analyzes data from multiple sources (transaction records, news articles, social media, etc.), presenting synthesized findings to investigators. This mediator handles the time-consuming data processing while preserving investigative accuracy through intelligent analysis algorithms.
Solution Approach 2:
The system enables self-service through automated data collection and initial analysis capabilities. The AI assistant independently queries multiple databases, retrieves relevant information, performs preliminary analysis, and generates reports without requiring manual intervention for each data point, thereby reducing investigation time while maintaining thoroughness.
2Reliability
If data from multiple disparate sources is integrated, then comprehensive analysis is achieved, but system complexity increases
Solution Approach 1:
The patent implements a universal data integration platform that handles multiple data sources (structured databases, unstructured text, web sources) through a single standardized interface. The AI assistant performs multiple functions including data collection, cleaning, analysis, and presentation within one system, reducing the need for separate tools and simplifying the overall system architecture while maintaining comprehensive analysis capabilities.
3Productivity
If automated AI systems are implemented, then investigation efficiency improves, but implementation costs increase
Solution Approach 1:
The patent applies partial automation where the AI assistant handles specific high-volume, repetitive tasks such as data gathering, initial filtering, and routine analysis, while human investigators focus on complex decision-making and nuanced judgment. This selective automation approach improves efficiency for routine operations without requiring full system automation, thereby reducing implementation and operational costs.
Data Source
AI summary
A system is adapted to automatically report the trustworthiness of an entity. The system includes a processor and a computer readable medium carrying instructions. The instructions include receiving unstructured data pertaining to an entity from public sources, and receiving structured data pertaining to the entity from at least two databases. The instructions also include merging the structured data and the unstructured data into a single document; splitting the single document into chunks; creating embeddings corresponding to the chunks; and storing the embeddings in a vector store. The instructions also include receiving a natural language user query regarding trustworthiness of the entity; converting the query to a query embedding; based on the query embedding and a similarity calculation, fetching a relevant embedding from the vector store; with a large language model (LLM), generating a query response regarding the trustworthiness of the entity; and communicating the query response to the user.