Comparative Analysis of Machine Learning Models for Credit Risk Assessment in FinTech Applications

Ayesha  Tariq; Muhammad Hassan  Raza

Credit risk assessment is a core function of financial technology (FinTech) platforms, underpinning lending decisions, pricing, and portfolio risk management. Over the past decade, machine learning (ML) has progressively displaced traditional statistical scorecards as the dominant analytical paradigm, driven by the availability of large, high-dimensional, and often alternative data sources together with the need for higher predictive accuracy. This review provides a comparative analysis of ML models used for credit risk assessment in FinTech applications, spanning traditional statistical classifiers, tree-based ensemble methods, and deep learning architectures. Following a structured literature search and screening process, we synthesize empirical evidence on predictive performance across benchmark and proprietary FinTech datasets, critically contrast model families under varying data conditions, and examine the trade-off between predictive power and interpretability that shapes real-world adoption. We further discuss the role of alternative data and explainable artificial intelligence (XAI) in expanding financial inclusion while preserving regulatory compliance, distinguishing global from local interpretability, and we outline open challenges, including data imbalance, concept drift, algorithmic fairness, privacy, and model governance. The review concludes with directions for future research, emphasizing hybrid and federated learning architectures, standardized benchmarking protocols, and explainability-by-design frameworks for credit risk applications in emerging FinTech markets.

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SUBMITTED: 23 June 2026
ACCEPTED: 02 August 2026
PUBLISHED: 5 August 2026
SUBMITTED to ACCEPTED: 41 days

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Tariq, A. ., & Raza, M. H. . (2026). Comparative Analysis of Machine Learning Models for Credit Risk Assessment in FinTech Applications. Data Intelligence and Informatics Practice, 1(1), 42−55. Retrieved from https://tecnoscientifica.com/journal/diip/article/view/1273

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Comparative Analysis of Machine Learning Models for Credit Risk Assessment in FinTech Applications

Data Intelligence and Informatics Practice

Volume 1 - Issue 1 - 2026

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