The creation of this project has been driven by th XMASS-Hack 2024 contest. Case#2: "Initial (Preventive) Compliance: Risk Prevention Using AI." sponsored by Blanc Labs a full-service technology consulting firm.
The Task Create a system that, based on the provided data about the bank's current clients, as well as additional information from open sources, social media, websites, and other company-related parameters, can predict the risk level of a new client.
I named my solution: ALARM system (Applicant Loan Analyzer for Risk Management). The ALARM is a web-based application designed to predict and assess the risk level of loan applicants with the minimum initial data provided, Idealy SSN/INN. The ALARM system leveraging advanced machine learning models along with publicly avaliable data provided by certain public goverment services, and empowers financial institutions to make data-driven analytics in order to help minimize rirsk and make best decisions.
The primary goal of the ALARM project is to provide an efficient and scalable solution for evaluating loan applications by:
- Analyzing applicant's initial data.
- Gather data from publicly available serives and make entry level check-up. How much real info has been provided, name, stock etc.
- Generating risk predictions based on pre-trained ML model on the real dataset with fraud-people features.
- Offering a user-friendly interface for inputting data and viewing predictions.
- Supporting cross-platform deployment on various operating systems.
- Cross-Platform Support: Powered by .NET Core, the application runs seamlessly on Linux, Windows, and macOS.
- Frontend: A responsive, interactive web interface built using React + Angular + Bootstrap for a modern user experience.
- Backend: An ASP.NET Core WebAPI-based REST API for data processing and model inference.
- Machine Learning Integration: Includes support for ML.NET or Python-based ML models wrapped as REST APIs.
- Secure Deployment: HTTPS configuration, and OpenAPI documentation for secure and standardized API usage.
- Test-Driven Development: Comprehensive backend and frontend test coverage using xUnit and Jest.
- Technology: React
- Purpose: Provides a user interface for loan officers to input applicant details and view risk analysis.
- Key Features:
- Interactive forms for applicant data entry.
- Real-time API integration for risk prediction.
- Responsive design using Angular and Bootstrap.
- Technology: ASP.NET Core 8.0
- Purpose: Implements REST APIs for handling data and serving predictions.
- Key Features:
- Controllers for applicant data processing and ML model inference.
- Docker support for scalable deployment.
- Integrated Swagger/OpenAPI for API documentation.
- Integration Options:
- ML.NET for in-platform model training and inference.
- Python-based models served via REST API endpoints.
- Purpose: Predict the risk associated with each applicant based on pre-processed input data.
- Backend Testing: xUnit for unit and integration tests.
- Frontend Testing: Jest and React Testing Library for component tests.
- Frontend: React 18.x, Node.js 20.x
- Backend: .NET 8.0
- Containerization: not yet included
- API Documentation: OpenAPI (Swagger)
- Database: not yet included
- React: 18.x
- ASP.NET Core: 8.0
- Docker: Compatible with Docker Desktop 4.x
- ML.NET: 2.x (if applicable)
- Node.js: 20.x
Contributions are welcome to improve the ALARM system. Please follow these steps:
- Fork the repository.
- Create a feature branch.
- Commit your changes.
- Submit a pull request.
For questions or support, feel free to reach out via GitHub Issues or contact us directly.