Customer Stories
Leading Digital Lender Accelerates Loan Approvals in Partnership with Xebia and Microsoft
Xebia partnered with Microsoft to help a leading digital lending platform revolutionize its decision-making process and improve borrower-lender alignment with an AI/ML-powered Loan Rejection Analysis System.
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At a Glance
Challenge
High rejection rates and limited insight into rejection drivers slowed growth and efficiency.
Solution
AI/ML-based Loan Rejection Analysis System built on Azure and Databricks for automated insights, explainability, and transparency.
Results
75% model accuracy, 84% precision, reduced manual effort, faster approvals, and transparent lending decisions.
The Client
A leading fintech company that provides an online marketplace for connecting a diverse borrower base across varying credit histories and financial profiles with lenders for instant loans, cards, and other financial services.
The Challenge: Limited Visibility Slowed Smarter Lending Decisions
The client faced significant challenge—a 95% loan rejection rate during the first review cycle, mainly due to limited understanding of key rejection factors and a lack of effective borrower–lender matching mechanisms. Data was dispersed across systems, and manual analysis slowed decision-making, often creating inconsistencies in how applications were evaluated. Without actionable insights, the platform struggled to improve approval rates or optimize borrower–lender alignment. To overcome this, the company needed a scalable, AI-driven system capable of identifying rejection causes, providing explainable insights, and supporting faster, fairer, and more transparent decision-making.
The Solution: AI/ML-Based Loan Rejection Analysis System
Leveraging Microsoft Azure’s advanced data and AI capabilities, Xebia designed and deployed a Loan Rejection Analysis System built on Azure Databricks and machine learning pipelines. The solution automates the identification of rejection causes, supports predictive modelling, and offers explainable AI insights to improve transparency and efficiency in decision-making.
Key Features Delivered:
- Medallion Data Architecture: Organized raw to refined data through bronze, silver, and gold layers for high-quality and reliable analytics.
- Feature Store Integration: Centralized repository for consistent and reusable features across ML models.
- Machine Learning Models: Ensemble of Random Forest, XGBoost, and CatBoost, achieving 75% accuracy and 84% precision.
- SHAP Explainability: Provided clear insights into why each loan was accepted or rejected, improving transparency and trust.
- What-If Analysis: Enabled simulation of key to predict potential approval outcomes.
- Automated MLOps Pipeline: Continuous integration and deployment for training, validation, and monitoring within Azure Databricks.
The Results: Smarter, Faster, and Transparent Lending Decisions
The client transformed its manual loan evaluation process into an intelligent, data-driven decision engine with Xebia’s data and AI expertise. The company can now provide a fairer, insight-driven environment where lenders and borrowers can engage with greater trust and clarity. The robust, scalable, and transparent lending ecosystem improved operational efficiency and enhanced customer experience. By automating feature engineering, streamlining model deployment, and introducing explainability, the platform reduced decision time, increased operational consistency, and improved risk assessment.
The company was able to:
- Improve loan outcome prediction accuracy to 75%.
- Increase the precision of loan rejection classification to 84%.
Looking Ahead
With a scalable and explainable AI foundation on Microsoft Azure, the client is now positioned to extend the solution across multiple lending partners and financial products. As the system continuously learns from evolving data patterns, it will drive further reductions in rejection rates, strengthen compliance, and support a more inclusive and efficient lending ecosystem powered by intelligent automation.
Learn More about how Xebia and Microsoft are transforming enterprises.
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