Intelligent Model Design for Detecting Internal Fraud by Bank Branch Employees Using a Hybrid Behavioral‑Statistical Analysis and Machine Learning Approach

Internal financial fraud committed by bank branch employees—due to their direct access to operational processes, information systems, and sensitive customer data—is considered one of the most significant threats to financial security and the integrity of the banking system. Such fraudulent activities not only cause direct financial losses for banks but can also undermine public trust in the banking sector. In recent years, significant progress has been made in detecting financial fraud such as money laundering, credit fraud, and cybercrime; however, the detailed analysis of branch employees’ behavior as a key factor in internal fraud has received comparatively less attention. Many existing monitoring systems focus primarily on analyzing customer transactions and pay limited attention to the behavioral patterns of employees interacting with banking systems. Consequently, the development of intelligent analytical models capable of integrating and analyzing both behavioral and transactional data of employees has emerged as a key requirement in the field of banking supervision and inspection.

The aim of this research is to design and present an intelligent model for detecting internal fraud committed by bank branch employees, with a focus on analyzing their transactional and operational behavior. This study seeks to develop an analytical framework for identifying fraudulent patterns in banking activities by combining statistical behavioral analysis techniques with machine learning algorithms. In this approach, employee behavior during banking operations is considered an important source of information, and abnormal behavioral patterns that may indicate fraudulent activity are examined. The use of statistical analyses to extract hidden relationships among behavioral and transactional variables, along with machine learning algorithms to detect complex and nonlinear patterns, enables the development of efficient and intelligent monitoring systems.

Implementation and validation of the proposed model are based on the use of data extracted from core banking systems. These data include transactional information such as types of banking operations, transaction volume and amount, timestamps, and sequences of activities recorded in operational systems. In addition, behavioral data include login patterns, frequency and type of user activities, interactions with operational systems, and other behavioral indicators related to job activities. Combining these two categories of data makes it possible to extract effective behavioral and statistical indicators for identifying suspicious activities and provides a suitable basis for training and evaluating intelligent fraud detection models.

The proposed framework is designed as a layered hybrid model consisting of three main layers. The first layer is the behavioral layer, where variables related to employee behavioral patterns and system interactions are extracted and analyzed. This layer aims to identify indicators that reveal abnormal changes in employees’ operational behavior. The second layer is the statistical analysis layer, in which relationships among behavioral and transactional variables are examined using statistical modeling methods and fuzzy logic. The use of fuzzy logic in this layer enables handling uncertainty and ambiguity in behavioral data and facilitates the identification of hidden patterns in employee behavior. The third layer is the machine learning layer, where algorithms such as artificial neural networks and support vector machines are used to analyze integrated data and detect complex fraud patterns. Leveraging the capability of machine learning algorithms to process large and multidimensional datasets significantly enhances the detection of fraudulent and abnormal behaviors.

It is expected that combining behavioral analysis, statistical modeling, and machine learning algorithms within an integrated framework will increase the accuracy of internal fraud detection and reduce false alarms. Integrating employee behavioral data with transactional data enables the identification of complex and multidimensional fraud patterns—patterns that are often undetectable using traditional monitoring methods. Additionally, the use of fuzzy logic alongside machine learning algorithms can help manage uncertainty in behavioral data and improve the model’s generalizability across different organizational conditions. These features allow the proposed model to perform effectively in dynamic and complex banking environments.

The main innovation of this research lies in the development of a localized hybrid framework for detecting internal fraud in the banking system, in which behavioral variables of employees and organizational structural characteristics are incorporated alongside technical and transactional data. By focusing on analyzing the operational behavior of branch employees, this model introduces a new approach to intelligent banking supervision and can serve as a supporting tool for internal audit and inspection units within banks. Implementing such a model can enhance the efficiency of supervisory processes, enable faster identification of internal fraud patterns, and ultimately help reduce financial fraud and improve the overall health of the banking system.