QERCAS: Quantum-Enhanced Regulatory Compliance Alert System QERCAS is a full-stack, AI-powered RegTech (Regulatory Technology) prototype for the investment banking sector. This system is designed to proactively detect and prevent financial compliance breaches by integrating a suite of next-generation technologies, including Explainable AI, Graph Neural Networks, and Post-Quantum Cryptography.

The platform provides a real-time dashboard for compliance officers to monitor live transactions, investigate high-risk alerts with AI-generated explanations, analyze suspicious networks, and query regulatory documents using natural language.

Live Dashboard

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Core Features Real-Time AI Analysis: A machine learning pipeline using Python (Scikit-learn/PyTorch) and asynchronous task queues (Celery, Redis) analyzes transactions in real-time, flagging them as Compliant, High-Risk, or Blocked.

Explainable AI (XAI): Integrates a SHAP (SHapley Additive exPlanations) engine to provide transparent, human-readable justifications for every AI-driven decision, ensuring model auditability and trust.

Graph Neural Network (GNN) Analysis: A GNN service (using NetworkX) dynamically builds and visualizes transaction networks to uncover sophisticated financial crime patterns like money laundering rings.

Natural Language Processing (NLP): An AI-powered regulatory search engine using a Hugging Face Transformers model allows officers to ask complex questions in plain English and receive precise answers.

Advanced Privacy & Security Modules: The architecture includes services for future-proof security:

Federated Learning: A simulated environment for training the core AI model across multiple institutions without sharing sensitive, private data.

Post-Quantum Cryptography (PQC): An integrated service using the Kyber algorithm to provide quantum-resistant encryption for critical data, protecting it from "Harvest Now, Decrypt Later" attacks.

Stack

• Backend: Django (Python 3.11+), Celery
• Broker: Redis
• Frontend: React (Node.js 18+ / npm)
• Task scheduling: Celery Beat or django_celery_beat
• Dev tooling: Docker Desktop (for Redis), Git, PowerShell (Windows)

Prerequisites

• Windows 10/11 with PowerShell
• Python 3.11+
• Node.js 18+ and npm
• Docker Desktop (for Redis)
• Git

Quick Start (local, Windows / PowerShell)

1) Clone

git clone cd

go to backend

cd backend

create & activate venv

python -m venv .venv ..venv\Scripts\Activate.ps1

install dependencies

pip install -r requirements.txt

if the file exists in your project, also run:

if (Test-Path ".\qercas_project\requirements.txt") { pip install -r .\qercas_project\requirements.txt }

#Database

• python manage.py migrate
• python manage.py createsuperuser

#Run Django Server

• python manage.py runserver

visit http://127.0.0.1:8000

Celery

cd backend ..venv\Scripts\Activate.ps1

Celery worker (Windows may require -P solo)

celery -A qercas_project worker -l info -P solo

Optional: Celery beat (for periodic tasks)

celery -A qercas_project beat -l info

Frotend

• cd ..\frontend
• npm install
• npm run dev

• visit http://localhost:3000