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A Review of Machine Learning and Deep Learning Techniques for Financial Risk Assessment: Loan Default Prediction and Fraud Detection

by Ayodeji Olusegun Akinwumi, Samuel Adebayo Oluwadare, Ilobekemen Perpetual Oladoja, Hussein Oluwaponmile Adefuwa
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 13 - Number 5
Year of Publication: 2026
Authors: Ayodeji Olusegun Akinwumi, Samuel Adebayo Oluwadare, Ilobekemen Perpetual Oladoja, Hussein Oluwaponmile Adefuwa
10.5120/ijaisfb208de3ecdf

Ayodeji Olusegun Akinwumi, Samuel Adebayo Oluwadare, Ilobekemen Perpetual Oladoja, Hussein Oluwaponmile Adefuwa . A Review of Machine Learning and Deep Learning Techniques for Financial Risk Assessment: Loan Default Prediction and Fraud Detection. International Journal of Applied Information Systems. 13, 5 ( Oct 2026), 47-67. DOI=10.5120/ijaisfb208de3ecdf

@article{ 10.5120/ijaisfb208de3ecdf,
author = { Ayodeji Olusegun Akinwumi, Samuel Adebayo Oluwadare, Ilobekemen Perpetual Oladoja, Hussein Oluwaponmile Adefuwa },
title = { A Review of Machine Learning and Deep Learning Techniques for Financial Risk Assessment: Loan Default Prediction and Fraud Detection },
journal = { International Journal of Applied Information Systems },
issue_date = { Oct 2026 },
volume = { 13 },
number = { 5 },
month = { Oct },
year = { 2026 },
issn = { 2249-0868 },
pages = { 47-67 },
numpages = {9},
url = { https://www.ijais.org/archives/volume13/number5/a-review-of-machine-learning-and-deep-learning-techniques-for-financial-risk-assessment-loan-default-prediction-and-fraud-detection/ },
doi = { 10.5120/ijaisfb208de3ecdf },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-05T23:58:05.877248+05:30
%A Ayodeji Olusegun Akinwumi
%A Samuel Adebayo Oluwadare
%A Ilobekemen Perpetual Oladoja
%A Hussein Oluwaponmile Adefuwa
%T A Review of Machine Learning and Deep Learning Techniques for Financial Risk Assessment: Loan Default Prediction and Fraud Detection
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 13
%N 5
%P 47-67
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The growth of digital lending, fintech services, and online financial transactions has increased the need for financial institutions to improve credit risk assessment and fraud detection. Traditional approaches increasingly struggle with the volume, velocity, and complexity of modern financial data, contributing to growing interest in machine learning (ML) and deep learning (DL). However, existing reviews often examine loan default prediction and fraud detection separately, with limited systematic comparison of ML and DL techniques across both risk areas. This review addresses this gap by systematically synthesising 109 studies on statistical, ML, and DL techniques applied to loan default prediction and fraud detection. The review examines statistical models, tree-based and ensemble methods, support vector machines, neural networks, recurrent architectures, attention-based models, and hybrid approaches. Findings show that performance varies across techniques, datasets, and financial risk contexts, making it difficult to establish a universally superior approach. Tree-based ensemble methods, particularly Random Forest, XGBoost, Gradient Boosting, and LightGBM, show strong performance on structured financial data, while recurrent architectures such as LSTM, GRU, and BiLSTM, with CNN and attention-based models, show particular benefits where risk information contains complex or sequential patterns. Several studies also reported improved performance from hybrid ML-DL approaches. Persistent challenges are class imbalance, limited financial data, model interpretability, data privacy, computational requirements, and deployment costs. Approaches such as SMOTE, synthetic data generation, explainable artificial intelligence, and federated learning have been explored to address these issues. Studies using African financial datasets remain relatively few compared with those from other regions. The study concludes that the suitability of ML and DL techniques for financial risk assessment depends on the nature of the risk, the characteristics of the data, and the modelling approach used.

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Index Terms

Computer Science
Information Sciences

Keywords

Loan Default Prediction Credit Risk Financial Fraud Ensemble Learning Recurrent Neural Networks Hybrid Models Class Imbalance Synthetic Data Explainable AI

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