2018· International Journal of Intelligent Automation & Robotics Engineering· Vol 1, pp. 01-14· 0 citations
TL;DR
This paper proposes an Explainable Deep Reinforcement Learning (XDRL) framework to automate and personalize credit limit adjustments based on customer behavior, financial data, and macroeconomic indicators, and learns optimal credit limit strategies that balance risk, customer satisfaction, and profitability.
Abstract
In the ever-evolving financial landscape, dynamic credit limit adjustment plays a critical role in optimizing customer experience and risk management. Traditional methods often rely on static, rule-based systems that lack adaptability and transparency. This paper proposes an Explainable Deep Reinforcement Learning (XDRL) framework to automate and personalize credit limit adjustments based on customer behavior, financial data, and macroeconomic indicators. Our model learns optimal credit limit strategies that balance risk, customer satisfaction, and profitability. To ensure transparency and regulatory compliance, we integrate explainability modules—such as SHAP values and attention mechanisms—into the DRL pipeline. We evaluate the system on real-world or simulated credit data, demonstrating improvements in credit utilization, default prediction, and interpretability. This work paves the way for safer, more accountable AI-driven decision-making in the credit industry.
CreditR1 delivers calibrated PDs with evidence-grounded reasoning that supports internal model validation and human review that supports transferability beyond the Chinese A-share market remains an open empirical question.
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Traditional robo-advisory systems are based on static risk profiling and rule-based allocation strategies that do not change according to changing market conditions and changing preferences of the investors, resulting in sub-optimal risk-adjusted performance. This study proposes a behavior-aware personalized portfolio optimization framework that dynamically adapts the asset allocation to the investor's risk tolerance. The approach uses a multisource dataset of 110 equities on the NASDAQ and includes market stress indicators from Yahoo Finance data such as volatility and drawdown. A Proximal Policy Optimization (PPO) agent with the Hierarchical Risk Parity (HRP) weights is implemented and a behavior-aware reward function is developed to maximize the Sharpe metric while penalizing the drawdown, turnover and violating any risk. The model is tested with a walk-forward backtesting protocol with monthly rebalancing and transaction cost modelling to make sure that performance is realistically tested. Experimental results may be observed that the sharpe and sortino ratios of the proposed method are higher, the maximum drawdown is lower, and the turnover is lower than those of Mean-Variance, Risk Parity, HRP, and standard PPO baseline in the results. In addition, the volatility analysis tolerance provides an effective personalization for conservatives, moderate and aggressive investors.
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Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
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