Predictive Banking: Applying Generative Ai Models For Credit Risk Profiling Using Java-Based Pipelines

Abstract

The financial services sector faces mounting pressure to improve credit risk assessment accuracy while managing computational costs and regulatory compliance requirements. This research investigates the application of generative artificial intelligence models within Java-based data pipelines for enhanced credit risk profiling in retail and commercial banking operations. The study develops a comprehensive framework integrating transformer-based language models with traditional credit scoring mechanisms, implemented through scalable Java architectures utilizing Spring Boot and Apache Kafka for real-time data processing. Through analysis of anonymized loan application data encompassing 250,000 customer records across multiple risk categories, the research demonstrates that hybrid approaches combining generative AI feature extraction with gradient boosting classifiers achieve 89.3% accuracy in default prediction, representing 12% improvement over conventional logistic regression models. The implementation showcases practical deployment strategies addressing model explainability requirements under banking regulations, managing inference latency within 200ms service-level agreements, and handling diverse data formats including unstructured text from customer communications. Results indicate that generative models excel at extracting risk-relevant features from alternative data sources such as transaction narratives and customer service interactions, providing insights beyond traditional financial metrics. The research contributes actionable implementation guidance for financial institutions seeking to modernize credit risk infrastructure through AI adoption while maintaining robust governance and compliance frameworks.