AI-BASED CREDIT RISK ASSESSMENT SYSTEM

Financial institutions require precise tools for loan applicant assessment. K-Softex engaged with a client to discuss specific operational hurdles in this area. The client’s requirements focused on predictive accuracy and reduced manual overhead.

Financial analytics dashboard for credit risk evaluation

PROJECT DESCRIPTION

The project aimed to create a platform that predicts loan repayment probability. This system needed to integrate with existing banking software, process varied data types, and provide explanations for its predictions to support loan officers.

CLIENT EXPECTATIONS

The client outlined several firm requirements for the collaboration:

A Python-based artificial intelligence engine for risk scoring
Secure API connections to existing CRM and core banking systems
A transparent model that identifies the main factors in each decision
Compliance adaptability for different regional regulations
A dedicated dashboard for financial analysts to monitor cases

The K-Softex team designed a solution using established machine learning frameworks. This approach provided a solid foundation for the complex analytical tasks. We constructed the platform around a modular architecture, which allowed for distinct data processing, model serving, and user interface components.

Our development plan addressed the need for both structured numerical data and unstructured document analysis.

We built custom data pipelines for feature extraction from transaction histories and external market indicators. The system generates a risk score alongside a summary of the top contributing parameters, such as income pattern stability or debt-to-income ratio shifts.

Backend and AI

Python / PyTorch / Django REST / FastAPI

Data engineering and storage

PostgreSQL / AWS S3 / Apache Spark / Apache Kafka

Infrastructure and DevOps

Microsoft Azure / Docker / Kubernetes / GitLab CI

Frontend

React / TypeScript / Redux

Credit scoring and risk assessment platform interface

THE RESULT

K-Softex delivered the platform that now assists loan officers by providing data-supported risk assessments. It connects directly to the institution’s primary systems via the implemented APIs. The analyst dashboard presents predictions and underlying reasons in a single view.

This implementation has lowered the time spent on manual review per application. As a result, the client reports improved consistency in initial applicant screening and has approved the platform for a wider rollout across additional branches.