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@article{176819,
        author = {P. Shiva kumar and K.Chiranjeevi and C. Manideep reddy and SUJITH},
        title = {Enhancing Loan Approval Accuracy and Accountability Using Decision Tree in Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6253-6256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176819},
        abstract = {The lending sector, being a critical component of the financial industry, has also been influenced by AI advancements. Through a case study in the banking sector, we explore how AI models can be designed, trained, and validated to provide not only accurate loan approval predictions. Traditionally, loan approval decisions have been reliant on manual assessment methods, often resulting in subjectivity, inconsistency, and sometimes even bias. The results of our case study indicate that the implementation of explainable AI but also fosters trust and trust and accountability. AI not only enhances the accuracy of loan approval decisions but also fosters trust and accountability. By shedding light on the contributing factors behind each decision, banks can make more informed and equitable choices reducing the potential for bias and increasing customer satisfaction. Enhancing Loan Approval Accuracy and Accountability Using Decision Tree in Machine Learning contributes to the evolving discourse on the responsible adoption of AI in the financial sector and underscores the potential benefits of incorporating transparency into AI-Powered loan approval systems.},
        keywords = {Decision Tree, Loan Approval, Explainable AI, Credit Risk, Accountability},
        month = {April},
        }