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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Ankur Agarwal
Sam Higginbottom University of Agriculture, Technology and Sciences
Computer Science & IT Engineering

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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.
CME-Insights: A Cognitive Multimodal Explainability Framework for Credit Risk Assessment Using SEC Financial Filings Ankur Agarwal; Shashi Prabha; Raghav Yadav
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7678

Abstract

In financial data analysis, credit risk assessment has traditionally depended on financial ratios. However, a lot of useful information is hidden in company reports, especially in the textual sections. Most of the financial analytics models do not utilize the textual features of financial data. To address this gap, this work proposes the Cognitive Analytics, Multimodal and Explainability (CME-Insights) framework, which is designed to provide clear and data-driven support for financial decision making. The proposed approach combines numerical financial indicators and textual information from the Management Discussion and Analysis (MD&A) sections. These two modalities of data are extracted from SEC 10-K and 10-Q reports. The structured financial modality is drawn from a publicly available corporate credit rating dataset comprising 2,029 records. The model uses 25 financial features along with 11 text-based features. These text features capture characteristics such as sentiment, uncertainty, risk, legal tone, and readability. Loughran-McDonald financial dictionary and the VADER sentiment model are used to extract these features. The best results came from a stacked model that combines CatBoost, LightGBM, and Random Forest base learners with a Logistic Regression meta-classifier. This model achieved an accuracy of 89.36% when ticker symbols were included, and 86.24% without them. SHAP and LIME explainability approaches indicated that the features such as debt ratio and operating cash flow per share play a key role in predicting credit risk. Thus, the CME-Insights framework delivers more accurate and explainable credit risk assessment than traditional financial-ratio-only approaches.
Co-Authors Raghav Yadav Shashi Prabha