4 papers
FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints
Sultan Amed, Tanmay Sen, Sayantan Banerjee
Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, o…
Noise-adjusted turnover in estimated networks
Sultan Amed, Sayantan Banerjee
Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observe…
FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling
Sultan Amed, Tanmay Sen, Sayantan Banerjee
Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border shari…
PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations
Sultan Amed, Chan Yu Hang, Sayantan Banerjee
This paper presents PDx, an adaptive, machine learning operations (MLOps) driven decision system for forecasting credit risk using probability of default (PD) modeling in digital l…