Beyond Traditional Credit Scoring: Developing AI-Powered Credit Risk Assessment Models Incorporating Alternative Data Sources

EasyChair Preprint 14332

Abstract

Traditional credit scoring models, reliant predominantly on historical financial data and credit histories, often fail to fully capture the complexities of an individual's creditworthiness, particularly in cases involving limited credit histories or non-traditional borrowers. This paper explores the development of advanced AI-powered credit risk assessment models that leverage alternative data sources to enhance predictive accuracy and inclusivity. By incorporating diverse datasets—such as social media activity, utility payments, e-commerce transactions, and employment history—these models aim to provide a more holistic view of an individual's financial behavior and risk profile. The integration of machine learning techniques, including natural language processing and anomaly detection, allows for the extraction of actionable insights from unstructured and structured data alike. This approach not only improves the precision of credit risk assessments but also expands access to credit for underserved populations. Through a comparative analysis of traditional versus AI-enhanced models, we demonstrate the potential of these innovative methodologies to transform credit risk evaluation, offering a more equitable and comprehensive framework for financial inclusion

Keyphrases: Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP)

@booklet{EasyChair:14332,
  author    = {Abi Litty},
  title     = {Beyond Traditional Credit Scoring: Developing AI-Powered Credit Risk Assessment Models Incorporating Alternative Data Sources},
  howpublished = {EasyChair Preprint 14332},
  year      = {EasyChair, 2024}}