Fraud Assistant LLM With RAG for Rapid Suspicious Activity Detection
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Solution Overview
Problem
Traditional fraud detection systems face challenges in addressing the growing volume and complexity of customer interactions due to manual processes and fragmented technologies, leading to slow response times and increased workload for fraud specialists.
Innovation Solution
A chatbot system utilizing a large language fraud model with retrieval-augmented generation (RAG) processes user inputs to provide contextually relevant responses, integrating structured and unstructured data sources, transaction histories, and fraud prevention guidelines to automate fraud detection and recommendation of actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes and fragmented technologies are used for fraud detection, then system simplicity is maintained, but response time increases and productivity decreases
Solution Approach 1:
The patent combines multiple fragmented technologies including large language models, retrieval-augmented generation, vector databases, and traditional fraud detection systems into a unified automated platform. This integration enables the system to process unstructured customer interactions, retrieve relevant context, and generate fraud assessments automatically, thereby improving productivity while managing complexity through systematic architecture.
Solution Approach 2:
The system implements self-service capabilities through automated chatbot interactions that can independently analyze customer queries, retrieve relevant information from multiple data sources, assess potential fraud risks, and provide recommendations without requiring manual intervention from fraud specialists for every interaction. This automation significantly improves detection efficiency while reducing the operational burden on human analysts.
2Speed
If manual fraud detection processes are used, then system complexity is low, but response time to suspicious activities increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing customer interaction data, transaction histories, and fraud prevention guidelines in structured formats within vector databases before fraud detection is needed. This preprocessing enables rapid retrieval and analysis during actual fraud detection events, significantly reducing response time while the complexity is managed through automated data preparation pipelines.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational systems including large language models and retrieval-augmented generation. These systems automatically analyze customer interactions, retrieve relevant context from databases, and generate fraud assessments at machine speed, dramatically improving response time while the system complexity is justified by the automated intelligence required for rapid analysis.
3Measurement precision
If automated systems with multiple data sources are implemented, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the fraud detection system into distinct functional modules: a large language model for understanding customer interactions, a retrieval-augmented generation component for context extraction, vector databases for storing and retrieving structured and unstructured data, and an assessment engine for generating fraud evaluations. This segmentation improves detection accuracy by allowing each component to specialize in specific tasks while managing overall system complexity through modular architecture that enables independent development, testing, and maintenance of each segment.
4Loss of information
If retrieval-augmented generation is used to process user inputs, then response relevance improves, but computational resources and system complexity increase
Solution Approach 1:
The system introduces a retrieval-augmented generation intermediary layer between the large language model and the final response generation. This intermediary automatically retrieves relevant context from vector databases based on the customer interaction, enriches the input with this retrieved information, and then passes the enhanced context to the language model for response generation. This process improves context retention and response relevance while the intermediary manages complexity by automating the retrieval and integration process through standardized interfaces and algorithms.
Data Source
AI summary
Systems and techniques may generally be used for chatbot-based fraud assistance. An example method may include initiating a chatbot session with a user and receiving a prompt from the user related to suspected suspicious activity in an account. The method may include retrieving, using a Retrieval-Augmented Generation (RAG) component, contextual information from at least one of transaction data and a knowledge base. The method may include evaluating the prompt using a large language fraud model and the retrieved contextual information to determine a response.