Sequence-Aware Behavioral Credit Engine (SABRE): Deep Learning for Continuous Affordability and Early Distress Detection in Digital Lending

Authors

  • Praveen Kumar Sabbineni

Keywords:

Behavioral Credit Scoring, Sequence Modeling, Deep Learning, Financial Risk Assessment, Digital Lending

Abstract

Traditional credit risk assessment methodologies suffer from temporal limitations that prevent effectivedetection of emerging financial stress and behavioral changes in borrowers. The Sequence-AwareBehavioral Credit Engine (SABRE) framework addresses these constraints through deep learning sequence models that process time-ordered financial event streams to generate continuous

References

Dennis Glennon, Peter Nigro, "Evaluating the performance of Static versus Dynamic models of credit default: evidence from long-term Small Business Administration-guaranteed loans," ResearchGate, 2011. [Online]. Available: https://www.researchgate.net/publication/266591659

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Published

2026-01-08

How to Cite

Praveen Kumar Sabbineni. (2026). Sequence-Aware Behavioral Credit Engine (SABRE): Deep Learning for Continuous Affordability and Early Distress Detection in Digital Lending . Journal of Computational Analysis and Applications (JoCAAA), 35(1), 159–169. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4651

Issue

Vol. 35 No. 1 (2026): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.