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Intelligent Borrower Profiling for Risk-Aware Loan Decision Making and Portfolio Optimization

リスク認識型融資決定とポートフォリオ最適化のための知的借り手プロファイリング (AI 翻訳)

Shankari Mohanakrishnan

Journal of Internet Services and Information Security📚 査読済 / ジャーナル2026-05-29#その他Origin: US経営インパクト: 資金調達対象セクター: finance
DOI: 10.58346/jisis.2026.i2.030
原典: https://doi.org/10.58346/jisis.2026.i2.030
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🤖 gxceed AI 要約

日本語

本研究は、LightGBMとアンサンブル学習を用いた多段階AIモデルを提案し、借り手のリスクプロファイリングと融資ポートフォリオ最適化を実現する。ESG指標などの多次元データを統合し、SHAPやLIMEで説明可能性を確保。Lending Clubデータセットで98%超の精度を達成し、従来手法を上回る性能を示した。

English

This study introduces a multi-stage AI model using LightGBM and ensemble learning for borrower risk profiling and loan portfolio optimization, integrating multi-source data including ESG metrics. With SHAP and LIME for explainability, it achieves over 98% accuracy on the Lending Club dataset, outperforming baseline models.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本の金融機関でもESG融資の拡大に伴い、非財務情報を含む借り手評価の高度化が求められている。本手法は、AIを用いた多面的なリスク評価モデルとして、国内の信用リスク管理に応用可能な示唆を提供する。

In the global GX context

Global financial institutions are integrating ESG factors into credit risk models. This paper presents a scalable AI framework for borrower profiling that incorporates ESG metrics, offering a data-driven approach to enhance risk-aware lending and portfolio optimization.

👥 読者別の含意

🔬研究者:Proposes a multi-stage AI model integrating LightGBM and explainable AI for credit risk assessment, valuable for researchers in fintech and risk modeling.

🏢実務担当者:Financial institutions can adopt this framework to improve loan decision-making and portfolio management by incorporating multi-source data including ESG metrics.

📄 Abstract(原文)

Lending environments have become more complex, and the frequency of loan defaults has increased, making it a need for intelligent, data-driven approaches for borrower evaluation. Traditional credit scoring approaches do not fully account for the credit risk of borrowers from a multi-dimensional perspective, such as financial, behavioral, and non-financial factors like ESG metrics. Understanding these trends, this study introduces an innovative multi-stage AI-based model named IBP-RLDPO, designed to enhance borrower profiling, risk-aware lending decisions, and portfolio optimization by using LightGBM, ensemble learning, feature engineering, and explainable AI. The framework integrates multi-source data, pre-processes the data, generates borrower risk profiles based on PD, LGD, and EAD models, and uses a risk-aware decision engine with portfolio-level optimization. Using SHAP and LIME for explainability layers can offer insights into the contributions of various features, which helps in facilitating transparent and ethical lending practices. On the Lending Club dataset of 887,379 loan records, an empirical assessment shows that it outperforms baseline and ensemble models with 98.25% accuracy, 78.5% recall, 98.25% precision, 82.5% F1-score, and 94.25% AUC. In a nutshell, IBP-RLDPO can improve risk adjudication of the decision-making process, help manage regulatory compliance, mitigate the risk of bad loans, and maximize returns on good loans. This research provides a full-scale and interpretable solution that is scalable for intelligent lending and optimized portfolio management, providing measurable business value and operational efficiency.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。