Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Shashi Prabha
Sam Higginbottom University of Agriculture, Technology and Sciences
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Explainable Creditworthiness Scoring using a Calibrated Multimodal Framework Ankur Agarwal; Shashi Prabha; Raghav Yadav
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7507

Abstract

This research presents an explainable, calibrated multimodal framework for creditworthiness scoring. It integrates financial ratios with narrative risk disclosures by using the 823 firm SEC (Securities and Exchange Commission,) filings. In this approach we have used a class-weighted, L2-regularized logistic regression within a unified pipeline. It produces calibrated probabilities via isotonic regression. We have assessed the performance across discrimination (ROC-AUC, PR-AUC), reliability (Brier, calibration curves), operating thresholds (F1, asymmetric costs), and business diagnostics. We have also included the Decision Curve Analysis to cover the pattern of net benefit across practical cutoffs. Our implementation covers an ablation study to verify multimodal gains over financial-only and text-only baselines. The present research findings state that the LightGBM + SHAP benchmark depicts better interpretability along with improved prediction accuracy. Thus. the study provide an useful insights towards effective governance and transparent system requirements which makes this suitable for effective financial risk management.
Co-Authors Ankur Agarwal Raghav Yadav