01. The challenge

The client relied on an expert-driven credit risk assessment system, which, although effective, posed limitations. The manual nature of the process constrained the company’s ability to process a large volume of factoring requests, slowing down business growth. Their goal was to develop a machine learning-based system that could assess credit risk in line with existing business principles. The solution needed to improve efficiency, objectivity, and scalability, while ensuring transparency and alignment with the company’s operational processes.

02. Our solution

To address these challenges, QuantUp devised a tailored AI solution. Our approach involved:

Comprehensive Analysis:
We conducted an in-depth review of the client’s credit risk assessment processes, ensuring the solution aligned with their operational logic and business goals.

Model Development:
Two sets of machine learning models were constructed to give the client flexibility in their approach:
Black-box models with XAI (eXplainable AI): These models provided high predictive power alongside explainability, enabling users to understand the rationale behind risk assessments.
Transparent models: Designed for maximum interpretability, these models aligned with the client’s need for visible and straightforward decision-making processes.

Interactive Decision-Making Application:
An application was developed to allow the client to compare the two model types, evaluate their trade-offs, and choose the most suitable approach.

Model Integration and Deployment:
The selected model was integrated into a fully functional application, enabling automated, 24/7 credit risk assessments.

03. Result

The implemented solution delivered transformative results:

Automation: The factoring application process was fully automated, enabling real-time, 24/7 assessments.

Scalability: The client significantly increased the volume of factoring requests processed within a short period, driving rapid business growth.

Objectivity: Risk assessments became consistent and data-driven, reducing reliance on subjective expert judgments.

Operational Efficiency: Streamlined processes allowed the client to allocate resources more effectively, focusing on strategic growth areas.