Explainable AI-Driven Software Ecosystems for Mortgage Loan Risk Management and Sustainable IT Operations with Large-Scale Sign Language Integration
DOI:
https://doi.org/10.15662/IJARCST.2022.0502002Keywords:
Explainable Artificial Intelligence (XAI), Mortgage Loan Risk Management, Sustainable IT Operations, Software Ecosystems, Sign Language Recognition, Financial Technology, Accessible AI, Inclusive Design, Interpretable Machine Learning, Cloud-based SolutionsAbstract
In the evolving landscape of financial technology, the integration of Explainable Artificial Intelligence (XAI) into software ecosystems offers promising avenues for improving transparency, trust, and performance in mortgage loan risk management. This paper presents a novel framework that combines XAI-driven decision-making processes with sustainable IT operations, enabling financial institutions to optimize risk assessment while adhering to environmental and ethical standards. In parallel, the research addresses inclusivity challenges by incorporating large-scale sign language recognition and translation capabilities within the software architecture. This integration aims to enhance accessibility for hearing-impaired users, ensuring equitable access to mortgage services. The proposed ecosystem leverages interpretable machine learning models, scalable cloud infrastructures, and multimodal interfaces to deliver robust, sustainable, and inclusive solutions for the mortgage and financial services sector. Evaluation metrics highlight improvements in model explainability, energy efficiency, and user accessibility, marking a significant step toward responsible AI adoption in high-stakes domains.
References
1. Lundberg, S.M. & Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv:1705.07874. arXiv
2. K. Anbazhagan, R. Sugumar (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud. Indian Journal of Science and Technology 9 (48):1-5.
3. Sangannagari, S. R. (2021). Modernizing mortgage loan servicing: A study of Capital One’s divestiture to Rushmore. International Journal of Research and Analytical Reviews (IJRAI), 4(4), 5520–5532. https://doi.org/10.15662/IJRAI.2021.0404004 https://ijrai.org/index.php/ijrai/article/view/129/124
4. Ribeiro, M.T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. KDD 2016 / arXiv:1602.04938. arXiv
5. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
6. T. Yuan, S. Sah, T. Ananthanarayana, C. Zhang, A. Bhat, S. Gandhi, and R. Ptucha. 2019. Large scale sign language interpretation. In Proceedings of the 14th IEEE International Conference on Automatic Face Gesture Recognition (FG’19). 1–5.
7. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D., & Giannotti, F. (2018). A survey of methods for explaining black box models. ACM Computing Surveys / arXiv:1802.01933. arXiv
8. Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., et al. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. arXiv
9. Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., et al. (2018). Consistent individualized feature attribution for tree ensembles. arXiv:1802.03888. arXiv
10. Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. KDD 2016 / arXiv:1603.02754. arXiv
11. Barroso, L.A., & Hölzle, U. (2009). The datacenter as a computer: An introduction to the design of warehouse-scale machines. Morgan & Claypool. (Foundational systems view on energy-efficient large scale computing.) web.eecs.umich.edu
12. Srinivas Chippagiri , Savan Kumar, Olivia R Liu Sheng,‖ Advanced Natural Language Processing (NLP) Techniques for Text-Data Based Sentiment Analysis on Social Media‖, Journal of Artificial Intelligence and Big Data(jaibd),1(1),11-20,2016.
13. Beloglazov, A., & Buyya, R. (2012). Energy efficient resource management in virtualized cloud data centers. In: Proceedings (and earlier GreenCloud taxonomy work 2011). (Energy-efficiency taxonomy for clouds/data centers.) clouds.cis.unimelb.edu.au
14. Schwartz, R., Dodge, J., Smith, N.A., & Etzioni, O. (2019). Green AI. arXiv:1907.10597. (Call to include efficiency in evaluation.) arXiv
15. Albanesi, S. & Sahm, C. (2019). Predicting consumer default: A deep learning approach. NBER Working Paper (example empirical deep learning for default).
16. G Jaikrishna, Sugumar Rajendran, Cost-effective privacy preserving of intermediate data using group search optimisation algorithm, International Journal of Business Information Systems, Volume 35, Issue 2, September 2020, pp.132-151.
17. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal of Computer Science Applications and Information Technology, 5(1), 1–8. https://doi.org/10.15226/2474-9257/5/1/00146
18. Fannie Mae. (2021, Feb 8). Single-Family Historical Loan Performance Dataset update (dataset and notes; acquisition/performance update through Q3 2020). Fannie Mae capital markets dataset documentation. Fannie Mae
19. Freddie Mac. (historical Single-Family Loan-Level Dataset; documentation available via Freddie Mac research datasets). (Public loan-level dataset resources.) Freddie Mac
