Fraud Investigation with GenAI-Powered Graph Analytics

Quick Summary

Challenge

A company needed to build a fraud investigation system capable of identifying relationships between suspicious transactions and uncovering deeper fraud patterns across complex financial networks.

Solution

Tatras Data developed a GenAI-powered question answering system integrated with Neo4j, allowing investigators to explore transactional relationships through natural language queries backed by graph intelligence.

Result

Manual investigation effort dropped significantly, while accuracy and speed in detecting fraud patterns improved across use cases.

Tech Stack

AI: GenAI QA agents REACT inference layer | ML: Scikit-learn for pattern analysis | Data & Retrieval: Neo4j Graph DB Cypher generation Transaction graph traversal | Dev: LangChain PyTorch Llama Index Transformers | Viz: Interactive fraud investigation UI with dynamic graph rendering | Security: Controlled access to sensitive fraud data Cloud-native deployment Query logging for compliance

The Challenge

Fraud isn’t always obvious.

Suspicious transactions often hide in networks of small, connected actions — with indirect links, shared entities, and time-delayed sequences. The company needed a tool that could not only detect isolated incidents but map relationships between transactions, accounts, and behaviors across multiple levels of connection.

Manual investigation was too slow and shallow. Traditional tools couldn’t explain “how” transactions were related. And scalable intelligence was missing.

A Day in the Life: Before Our Solution

A fraud analyst investigates a flagged transaction.

They review individual records, then manually sift through related accounts, previous activities, and external databases to determine if it's a one-off event — or part of a larger pattern.

The process takes hours, often requires judgment calls, and can miss subtle links between accounts that appear unrelated at first glance.

Pain Points:

High manual workload for fraud analysts
Limited visibility into cross-transaction relationships
Static queries couldn't adapt to complex investigator needs
Missing hidden patterns due to format or data silo issues
Existing tools lacked conversational or visual exploration capabilities

Solution

1. Core Innovation

Tatras Data built a GenAI-powered fraud investigation platform designed to reason like an analyst — only faster.

Key components:

Neo4j-Powered Transaction Graph
Transactions, accounts, and entities are mapped as nodes and edges, enabling multi-hop queries and backtracking.
GenAI-Driven Cypher QA Agent
Investigators ask natural language questions. GenAI interprets the schema and generates dynamic Cypher queries.
REACT Inference Layer
After data is fetched, a reasoning agent interprets the results, highlights key connections, and summarizes fraud potential in plain English.
Visual Exploration UI
The results are shown as an interactive graph, letting analysts trace and expand nodes to follow connections without switching systems.

2. Key Features

Graph-Based Entity Modeling: Captures multi-entity fraud patterns across transactions
Natural Language Querying: Converts investigator questions into Cypher graph queries
REACT-Based Reasoning: Adds inference and insight beyond raw data
Interactive Graph UI: Lets users explore fraud paths visually and in real time
Scalable + Secure: Handles sensitive fraud data across large transactional volumes

3. Workflow Integration

The system is deployed as an internal tool for fraud analysts.

They use a conversational interface to ask questions and receive visual graph-based answers, complete with summaries, context, and investigative leads — all in a single dashboard.

Outcomes

✅ Significantly reduced manual effort in fraud investigations

🔍 Improved ability to trace and explain transactional relationships

⏱️ Faster fraud detection and pattern recognition

📈 Enhanced decision support with clear visualizations and insights

💡 Flexible query system adapts to new scenarios and investigation styles