Encapsulated partnered with Lending Club to build a C# and AWS-powered module that predicts default risk in real time, enabling faster, safer, and more transparent loan approvals.
Lending Club
FinTech & Lending
4 months
4 Engineers (C#, .NET, AWS AI) + 1 Data Scientist
Lending Club is a US-based digital lending platform that evaluates and originates consumer loans at scale. Their teams rely on data-driven underwriting and rigorous risk controls to deliver fast decisions without compromising compliance.
To strengthen application-time risk assessment, Lending Club engaged Encapsulated to build a Default-Risk Screening Module that flags potentially high-risk applications in real time, provides human-readable explanations for analysts, and captures complete audit trails for model governance.
We designed a C#/.NET service layer integrated with AWS AI services for probability-of-default scoring, added a rule engine for policy thresholds, and delivered an analyst console for review workflows—bringing speed, transparency, and consistency to the first line of credit decisioning.
Scoring < 300ms p95
Highly available scoring endpoints
SHAP-driven model insights
Before implementing the AI-powered risk screening module, Lending Club needed to strengthen its ability to detect potential defaults early, ensure transparency in credit decisions, and streamline analyst workflows.
Existing credit models struggled to provide real-time predictions of default probability at the point of application.
Applicant data was spread across multiple systems, making feature engineering and risk analysis complex and time-consuming.
Lack of integrated fraud detection signals increased exposure to identity fraud and synthetic loan applications.
Loan approvals had to be processed quickly to meet customer expectations without compromising accuracy.
Regulatory and internal audit teams required transparent explanations for every credit decision made by the models.
Encapsulated partnered with Lending Club to develop a C# and AWS-powered module that predicts default risk in real time, enhances fraud detection, and provides explainable insights for compliance-ready loan decisioning.
Built a robust, scalable microservice in C#/.NET to process applications and deliver risk scores within milliseconds.
Leveraged SageMaker models trained on Lending Club’s historical loan data to generate probability-of-default predictions.
Integrated AWS Fraud Detector to identify anomalies, identity theft, and synthetic loan applications.
Implemented SHAP-based model explanations to provide transparent reasoning for every risk score and meet audit standards.
Developed a secure interface for credit analysts to review flagged applications, adjust thresholds, and approve or decline with confidence.
Designed the system to be SOC 2-compliant, highly available, and capable of scaling with Lending Club’s growing application volumes.
The Default-Risk Screening Module enabled Lending Club to assess loan applications in real time, flag potential defaults, and provide analysts with transparent, explainable insights for faster decisions.
C#/.NET services with AWS AI for probability-of-default scoring, fraud detection, and explainable model outputs
Real-time probability-of-default predictions with model explainability to support compliance and audit readiness.
Integrated data sources across applications, credit files, and fraud signals to generate a holistic risk profile.
Analyst dashboards provided SHAP-based explanations, enabling consistent and defensible loan approval workflows.
A secure, scalable stack combining .NET engineering with AWS AI services to deliver real-time risk prediction, fraud detection, and explainable decisioning for Lending Club.
Whether you need to screen financial applications, personalise customer journeys, or fine-tune AI models with RAG, we can help. Encapsulated builds scalable, explainable AI solutions tailored to your business goals.
No commitment required • Free consultation • Tailored AI solution proposal