Citation:

Sandeep Yadav, 2024. AI-Powered Hybrid Framework for Early Warning Signals in Startup Credit Risk Assessment, ESP Journal of Engineering & Technology Advancements  4(2): 143-148.

Abstract:

Startups pose unique credit risks due to their dynamic business models and limited financial histories, making early detection of potential defaults crucial for lenders. This research presents an AI-powered framework for predictive early warning signals in credit risk assessment, combining traditional and advanced machine learning models. The framework integrates Logistic Regression for baseline interpretability, an ensemble model (e.g., XGBoost) for handling complex feature interactions, and a Recurrent Neural Network (RNN) for analyzing sequential financial data. Robust feature engineering incorporates key indicators like cash flow, revenue growth, and external funding patterns, ensuring data relevance.The framework is evaluated using real-world startup financial data, leveraging metrics such as accuracy, ROC-AUC, and precision-recall to compare model performance. Results demonstrate that ensemble and RNN models significantly enhance predictive power over traditional approaches, while Logistic Regression ensures interpretability for regulatory compliance. By combining explainable methods with high-performing predictive models, this framework enables financial institutions to dynamically monitor startup credit quality, improve decision-making, and mitigate lending risks. This study highlights the potential of hybrid AI models in addressing credit risk challenges in volatile, data-scarce environments.

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Keywords:

Credit Risk Assessment, Predictive Modeling, Early Warning Signals, Startups, Logistic Regression, Ensemble Models, Xgboost, Recurrent Neural Networks (RNN), Machine Learning, Financial Risk Mitigation, Feature Engineering, Sequential Data Analysis, Credit Default Prediction, Hybrid AI Framework, Lending Risk Management.