finance burden

Transparent AI-driven risk insights for finance

Comment

🏦 FinTrust AI — Building Trust in Every Transaction

🧠 Inspiration

In the modern financial world, compliance is everything. Banks, fintech startups, and payment providers are drowning in regulations — AML, **KYC, **FATF, **GDPR, you name it. Our team saw compliance officers spending hours on repetitive manual checks and writing reports that auditors still question.

We asked ourselves:

Can we build an AI system that doesn’t just automate compliance — but also *explains its reasoning transparently and securely?*

That idea became FinTrust AI, an **AI-powered compliance copilot that helps financial institutions analyze, explain, and document risk in a fully secure, AWS Bedrock–based environment.

⚙️ How We Built It

We combined FastAPI, **React, and **AWS Bedrock (Claude 3 Sonnet 4) to create a secure, end-to-end compliance assistant.

Architecture

  1. Frontend (React + Tailwind) – interactive dashboard to upload KYC files, review risk levels, and download audit-ready reports.
  2. Backend (FastAPI) – handles API routing, PII redaction, and Bedrock inference calls.
  3. AWS Bedrock – powers the reasoning engine using Claude 3 Sonnet 4 for contextual AML/KYC analysis.
  4. Vanta API – verifies compliance posture and security readiness.
  5. AWS Services –
  • S3 + KMS for encrypted document storage
  • DynamoDB / CloudWatch for audit logs
  • IAM roles for zero-trust access
    1. CI/CD – deployed via Docker + AWS Lambda (serverless).

Core Flow

User → React Dashboard → FastAPI Endpoint → AWS Bedrock (Claude 3 Sonnet 4) ↘ Reports stored securely in S3 (KMS-encrypted)

💡 What We Learned

  • How to leverage AWS Bedrock’s multi-model ecosystem for secure AI reasoning.
  • How to design IAM policies and KMS encryption for real-world compliance.
  • How to integrate explainability into LLM responses using structured JSON reasoning.
  • How

Vaibhnnn

Perfect — that’s the Project Story section for your hackathon submission (Devpost / AWS Hackathon). Here’s a ready-to-paste Markdown draft for your FinTrust AI project — polished, professional, and aligned with AWS Bedrock + FinTech Compliance theme.

🏦 FinTrust AI — Building Trust in Every Transaction

🧠 Inspiration

In the modern financial world, compliance is everything. Banks, fintech startups, and payment providers are drowning in regulations — AML, **KYC, **FATF, **GDPR, you name it. Our team saw compliance officers spending hours on repetitive manual checks and writing reports that auditors still question.

We asked ourselves:

Can we build an AI system that doesn’t just automate compliance — but also *explains its reasoning transparently and securely?*

That idea became FinTrust AI, an **AI-powered compliance copilot that helps financial institutions analyze, explain, and document risk in a fully secure, AWS Bedrock–based environment.

⚙️ How We Built It

We combined FastAPI, **React, and **AWS Bedrock (Claude 3 Sonnet 4) to create a secure, end-to-end compliance assistant.

Architecture

  1. Frontend (React + Tailwind) – interactive dashboard to upload KYC files, review risk levels, and download audit-ready reports.
  2. Backend (FastAPI) – handles API routing, PII redaction, and Bedrock inference calls.
  3. AWS Bedrock – powers the reasoning engine using Claude 3 Sonnet 4 for contextual AML/KYC analysis.
  4. Vanta API – verifies compliance posture and security readiness.
  5. AWS Services –
  • S3 + KMS for encrypted document storage
  • DynamoDB / CloudWatch for audit logs
  • IAM roles for zero-trust access
    1. CI/CD – deployed via Docker + AWS Lambda (serverless).

Core Flow

User → React Dashboard → FastAPI Endpoint → AWS Bedrock (Claude 3 Sonnet 4) ↘ Reports stored securely in S3 (KMS-encrypted)

💡 What We Learned

  • How to leverage AWS Bedrock’s multi-model ecosystem for secure AI reasoning.
  • How to design IAM policies and KMS encryption for real-world compliance.
  • How to integrate explainability into LLM responses using structured JSON reasoning.
  • How to combine AI security (Vanta) and financial compliance seamlessly.

🚧 Challenges We Faced

  • Restricting model access via IAM least-privilege without breaking Lambda execution.
  • Debugging Bedrock JSON streaming responses for large payloads.
  • Designing prompts that produce consistent, regulator-friendly risk explanations.
  • Balancing accuracy, reasoning transparency, and latency for live analysis.

🚀 Impact

FinTrust AI transforms hours of manual review into minutes of transparent, AI-assisted insight — without compromising data privacy. It demonstrates that AI in finance can be compliant, explainable, and secure when built on the right foundation — AWS Bedrock.

🛠️ Built With

  • Languages: Python (3.11), JavaScript (ES6)
  • Frameworks: FastAPI, React, Tailwind CSS
  • Cloud: AWS Bedrock (Claude 3 Sonnet 4), AWS Lambda, API Gateway
  • Storage: Amazon S3 + KMS, DynamoDB
  • Monitoring: CloudWatch, CloudTrail
  • APIs: Vanta API (for security & compliance validation)
  • DevOps: Docker, GitHub Actions

🔗 Try It Out

🎥 Video Demo

https://youtu.be/your-demo-link

Would you like me to generate a shorter 3-sentence summary version of this story (for the Devpost “Project Overview” box)? It’s usually what goes above the full description.

Built With

  • and-**aws-bedrock-(claude-3-sonnet-4)*-to-create-a-secure
  • compliance
  • end-to-end
  • react
  • we-combined-*fastapi
Share this project:

Updates