June 5, 2024Proceedings on Engineering SciencesOpen Access

Customer Credit Worthiness in the Digital Age: A Management Approach to Machine Learning Application in Banking

View Full Paper

Ask AI

Bookmark

Machine learning models utilizing alternative big data enhance customer default forecasting accuracy while expanding financial inclusion in banking systems.
Literature and case study review identifies critical legal, ethical, and compliance challenges associated with processing alternative consumer data.
Regulatory oversight and technical upskilling are essential to ensure credit scoring algorithms maintain transparency rather than operating as black boxes.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Literature review demonstrates that machine learning models improve customer default forecasting in banking, indicating a need for regulatory compliance and model transparency.

Cite This Study

Manukyan et al. (2024) studied this question.

synapsesocial.com/papers/68e65fa0b6db6435875ee126 https://doi.org/10.24874/pes06.02.023

View Full Paper

Ask AI

Bookmark

Implication

Literature review demonstrates that machine learning models improve customer default forecasting in banking, indicating a need for regulatory compliance and model transparency.

Customer Credit Worthiness in the Digital Age: A Management Approach to Machine Learning Application in Banking

Key Points

Discussion

Implication

Cite This Study

Customer Credit Worthiness in the Digital Age: A Management Approach to Machine Learning Application in Banking

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider