AI-powered Fraud Detection in Enterprise Logistics and Financial Transactions: A Hybrid ERP-integrated Approach

Abstract

In the dynamic landscape of global logistics and supply chain management, financial transactions are increasingly vulnerable to sophisticated fraudulent activities. The rapid expansion of e-commerce, cross-border trade, third-party logistics (3PL) providers, and digital payment systems has led to a massive in- flux of transactional data, often dispersed across heterogeneous systems. This complexity provides ample opportunity for cybercriminals to exploit gaps in monitoring and control mechanisms. Traditional rule-based fraud detection sys- tems are inadequate for identifying modern fraud patterns that evolve quickly and often go unnoticed due to their subtlety. In this context, the integra- tion of Artificial Intelligence (AI), particularly machine learning (ML), natural language processing (NLP), and deep learning techniques, presents a transfor- mative approach to proactively detect, prevent, and mitigate financial fraud in logistics and supply chain ecosystems. This paper presents a comprehensive investigation into AI-powered fraud detection mechanisms tailored specifically for logistics and supply chain financial environments. We begin by exploring the multifaceted nature of fraud in this domain, including invoice fraud, pro- curement fraud, duplicate payments, identity theft, cargo theft, and collusion between internal and external entities. These issues not only result in signif- icant financial losses but also undermine trust, delay operations, and impair long-term strategic partnerships. To address these challenges, AI technologies offer capabilities that extend beyond the static limitations of rule-based sys-tems. By continuously learning from historical and real-time data, AI models can identify anomalies, behavioral deviations, and hidden patterns that may indicate fraudulent activity. A major contribution of this research is the de- velopment and evaluation of a hybrid AI architecture combining supervised learning models (e.g., Random Forest, Gradient Boosting Machines, and Sup- port Vector Machines) with unsupervised learning techniques (e.g., Isolation Forests and Autoencoders) for anomaly detection. These models are trained and tested using real-world datasets sourced from logistics platforms and simu- lated financial transaction logs to validate their efficacy. Additionally, we incor- porate NLP techniques to process unstructured data, such as emails, shipping instructions, and contracts, to detect language patterns indicative of phishing attempts or fraudulent documentation. The research also integrates temporal analytics and graph-based models to uncover collusion networks and recurring suspicious activities across suppliers, vendors, and intermediaries. To enhance model accuracy and interpretability, feature engineering is conducted on vari- ables such as transaction frequency, delivery anomalies, mismatch in vendor banking details, and geographic discrepancies. A feedback mechanism is also integrated where flagged transactions are reviewed by human auditors and fed back into the system to fine-tune the model iteratively. This human-in-the-loop (HITL) framework ensures accountability, reduces false positives, and supports compliance with financial regulations and auditing standards. Furthermore, this paper highlights the importance of explainable AI (XAI) in gaining stakeholder trust. In sectors where transparency is critical, especially in finance and logistics, black-box AI models may not be acceptable unless supported by interpretable frameworks. We employ SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide insights into how each decision was reached, thereby assisting auditors, compliance officers, and supply chain managers in making informed decisions. An important aspect of this study is the integration of AI fraud detection systems with enterprise resource planning (ERP) platforms such as SAP and Oracle and supply chain management (SCM) systems like Workday and Manhattan Associates. The real- time deployment of AI modules within these platforms allows for proactive fraud prevention instead of reactive loss mitigation. Case studies are presented demon- strating how leading logistics providers have leveraged AI models to reduce false payment approvals, flag ghost vendors, and intercept financial anomalies before funds are disbursed. Additionally, the research addresses key challenges such as data privacy, regulatory compliance (e.g., GDPR, CCPA), and ethical concerns related to automated decision-making. The deployment of privacy-preserving AI models using federated learning and differential privacy techniques is exam- ined, ensuring that sensitive financial and logistical data is protected throughout the fraud detection pipeline. The results of the experiments show a significant improvement in fraud detection accuracy, reduced time to detection, and lower false positive rates compared to traditional methods. Our hybrid AI framework achieved a precision score over 93 percentage and a recall rate exceeding 90 percentage, indicating high reliability in detecting both known and novel fraud patterns.  Moreover, the integration of behavioral analytics and graph theory models enabled the identification of fraud rings and suspicious supply chain relationships that were not detectable through conventional approaches.