# A systematic review and future directions for AI-driven detection of fraud patterns in SACCO transactions

## TL;DR

This paper reviews AI and machine learning methods for detecting fraud in SACCOs and highlights the need for better technology and hybrid models.

## Contribution

The study identifies gaps in SACCO-specific fraud detection and recommends hybrid models integrating AI with traditional methods.

## Key findings

- Traditional and digital fraud patterns coexist in SACCO transactions.
- Machine learning shows promise but faces challenges like class imbalance and interpretability.
- Hybrid models combining AI and traditional methods are recommended for SACCOs.

## Abstract

Fraud in Savings and Credit Cooperative Organizations (SACCOs) remains a major challenge that undermines financial inclusion and sustainability in developing countries. This study conducted a systematic literature review to examine both traditional and emerging fraud patterns and evaluate fraud detection methods with emphasis on artificial intelligence and machine learning applications. A comprehensive structured search across Web of Science, Scopus, and Google Scholar yielded 28 peer-reviewed studies published between 2015 and 2025 that met eligibility and quality criteria. The findings reveal that traditional fraud patterns such as member collusion, embezzlement, and asset misappropriation coexist with emerging digital fraud such as mobile payment fraud, phishing, card fraud, and cryptocurrency scams. While rule-based and audit-based detection remain ineffective, machine learning has demonstrated significant promise for real-time detection but faces challenges related to class imbalance, interpretability, and data privacy. The review identified a weak Information and Communication Technology (ICT) infrastructure, the absence of SACCO-specific fraud detection models, and hybrid frameworks. It concludes that hybrid models that integrate traditional audit methods with machine learning are recommended for SACCO-specific fraud detection frameworks. This study emphasizes the need for future research on explainable AI and privacy-preserving analytics to enhance fraud resilience in SACCOs.