Predictive Analytics for Credit Risk Assessment: A Data-Driven Approach to Improving Credit Decision-Making

@article{1719857,
      author = {Dr. Chaitra K S, Sneha P Chikkamath},
      title = {Predictive Analytics for Credit Risk Assessment: A Data-Driven Approach to Improving Credit Decision-Making},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1870-1876},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719857.pdf},
      abstract = {This article discusses the role of predictive analytics in credit risk assessment. It explains how organizations can use historical customer and financial data to predict loan default, improve credit decisions, reduce financial losses, and strengthen risk management. The discussion is generalized and does not refer to any specific company. Statistical techniques such as descriptive analysis, t-test, chi-square test, and ANOVA are highlighted along with factors including credit score, annual income, debt-to-income ratio, employment experience, previous defaults, and loan purpose.},
      keywords = {Predictive Analytics, Credit Risk, Credit Scoring, Machine Learning, Financial Analytics, Loan Default},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719857}
  }