arXiv:2606.10333v1 Announce Type: new Abstract: Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction models using borrowers' demographic, financial, and credit history data, collectively referred to as traditional data. Recent studies have demonstrated that alternative data, such as borrowers' mobile phone communication data, enable lenders to acquire fuller and more accurate profiles of borrowers' creditworthiness, thereby improving credit risk prediction performance. Nevertheless, alternative data
The proliferation of digital data and advancements in privacy-preserving AI techniques are enabling new approaches to sensitive financial applications like credit risk.
Improving credit risk prediction with alternative data, while safeguarding privacy, can expand access to credit and enhance financial inclusion for underbanked populations.
Credit risk models can become more accurate and inclusive by leveraging novel data sources, potentially reshaping lending practices and the competitive landscape of financial services.
- · Fintech companies
- · Underbanked populations
- · AI/ML providers
- · Data privacy solution providers
- · Traditional credit bureaus
- · Incumbent banks slow to adapt
- · Loan sharks
More accurate credit assessments reduce default rates and expand lending to previously underserved segments.
Increased competition and innovation in the credit market driven by data-driven insights and privacy-preserving AI.
Potential for new financial instruments and economic growth, but also ethical considerations around data use and bias.
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Read at arXiv cs.LG