The problem FinTrace solves

The Problem It Solves

Financial fraud is becoming harder to detect because criminals use complex company networks, shell entities, fake transactions, and hidden ownership structures.

Today, investigators spend a lot of time manually collecting and connecting information from different sources, which can lead to slow investigations and missed fraud connections.

FinTrace solves this by providing an AI-powered financial investigation platform that:

1.Detects financial fraud patterns such as shell companies, GST fraud, loan fraud, and suspicious networks.

2.Reveals hidden relationships between companies, directors, and transactions using graph intelligence.

3.Uses AI verification to analyze claims and provide explainable insights.

4.Creates evidence-based fraud reports that help investigators make faster decisions.

5.Reduces manual investigation time by bringing multiple analysis techniques into one platform.

In simple terms: FinTrace helps banks, auditors, and investigators find hidden fraud connections faster, with AI-powered evidence and transparent reasoning.

Challenges we ran into

Challenges We Ran Into

The backend was not publicly deployed because FinTrace depends on multiple infrastructure components, including Neo4j graph database services and AI API integrations that require secure environment configuration. For the final demonstration, we prioritized a stable and fully functional setup instead of exposing an unstable deployment with external dependency risks. The complete backend is deployment-ready and can be hosted with the required infrastructure configuration.

Building FinTrace involved solving several technical challenges while combining AI, machine learning, and graph intelligence into one platform.

🕸️ 1. Building a Real-Time Fraud Relationship Graph

Challenge:
Financial fraud is not limited to one transaction or company. The difficult part was identifying hidden relationships between companies, directors, transactions, and suspicious entities.

Solution:
We implemented a Neo4j-based graph intelligence layer to represent entities and relationships as connected data. This allowed us to visualize fraud networks and identify suspicious clusters instead of analyzing isolated records.

🤖 2. Integrating AI Verification with Reliable Outputs

Challenge:
AI models can produce inconsistent responses, and financial decisions require explainable and trustworthy results.

Solution:
We integrated** Gonka Router with multi-model verification, **structured JSON responses, Truth Score calculation, and Request ID tracking to make AI decisions more transparent and traceable.

🔍 3. Combining Multiple Fraud Detection Approaches

Challenge:
Different fraud types require different detection strategies, such as loan fraud, GST fraud, and shell company networks.

Solution:
We combined rule-based detection with machine learning models and graph analysis to create a unified fraud intelligence system.

⚡ 4. Making the System Fast and Demo-Ready

Challenge:
Processing multiple data sources and AI analysis pipelines while maintaining a smooth user experience was challenging.

Solution:
We optimized API workflows, handled failures gracefully, and designed an interactive dashboard that presents complex investigations in a simple way.