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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI-POWERED CREDIT SCORING MODELS USING ALTERNATIVEDATASOURCESINEMERGINGMARKETS |
| Authors Name: | Sandhiya S , Vijayachander V S |
| Download E-Certificate: | Download |
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IJRTI_204841
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| Published Paper Id: | IJRTI2511100 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | ABSTRACT In emerging markets, access to conventional credit score systems is frequently restrained due to the shortage of formal financial histories, main to under banking and financial exclusion. Traditional credit score scoring models depend closely on historical credit records, which many capability debtors in these markets lack. This paper proposes an AI-powered credit scoring framework that leverages alternative facts sources together with mobile utilization patterns, software payments, social media hobby, and transaction behaviors to assess creditworthiness. We combine device learning techniques which includes logistic regression, random wooded area, and gradient boosting algorithms to develop adaptive models trained on non-traditional data. Feature engineering is employed to extract behavioral styles from uncooked facts, allowing a greater holistic knowledge of a man or woman’s credit risk. Our system is designed to be scalable, interpretable, and bias-conscious, with integrated equity metrics and explainability equipment like SHAP to make sure transparency. The model is examined the usage of a hybrid dataset combining telecom statistics, e-wallet transaction logs, and cellular money utilization from decided on African and Southeast Asian markets. Results reveal a fifteen–20% development in predictive accuracy over traditional scoring systems, and a giant boom in economic inclusion metrics by means of identifying creditworthy people formerly unscorable through traditional methods. This research highlights the transformative potential of AI-pushed credit scoring in bridging the space between financial institutions and underserved populations. Our findings endorse that with right regulation, privateness safeguards, and ethical AI practices, those models can revolutionize get admission to credit in emerging economies. |
| Keywords: | AI Credit Scoring, Financial Inclusion, Machine Learning, Emerging Markets, Alternative Data, Predictive Modeling, Fair AI |
| Cite Article: | "AI-POWERED CREDIT SCORING MODELS USING ALTERNATIVEDATASOURCESINEMERGINGMARKETS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a858-a863, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511100.pdf |
| Downloads: | 000244 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511100
Registration ID:204841
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a858-a863 Country: Namakkal, Namakkal, India Research Area: Other Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511100 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511100 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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