Fin-XAI: Explainable AI for Financial Risk Auditing

Fin-XAI is a distributed framework that bridges the gap between high-performance enterprise .NET applications and advanced Python-based Machine Learning.

Machine Learning models in finance are often treated as "Black Boxes." This project provides a novel architecture combining a .NET 8 Blazor Razor Class Library (RCL) with the analytical depth of Python's SHAP (Shapley Additive exPlanations) to provide real-time, transparency-first decision auditing for financial portfolios.

Interface

Fin-XAI Home Page

<img width="768" height="595" alt="image" src="https://github.com/user-attachments/assets/d7fb83fc-3982-4d67-a722-b525629f224b" />

Fin-XAI Dashboard

<img width="1900" height="867" alt="image" src="https://github.com/user-attachments/assets/fccc3199-72a1-4113-bd75-2e5d04a1788b" />

Architecture

The solution is divided into two primary environments:

1. Inference Engine (Python / FastAPI): A lightweight API that keeps a Random Forest classifier (trained on the UCI German Credit Dataset) loaded in memory. It calculates dynamic SHAP values on the fly to explain why a specific financial decision or risk score was assigned.
2. Client Dashboard (FinXAI.Universe NuGet / Blazor): A plug-and-play Razor Class Library containing the data models, HTTP services, and a fully interactive UI component (<FinXaiDashboard />). It uses JS Interop to handle client-side PDF/CSV exports without taxing the server.

Getting Started (Local Development)

To run the full simulator locally, you need both the Python engine and the .NET host running.