Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.

The development of artificial intelligence for recognizing fraudulent banking operations has received significant attention in recent years. This is due to the growing number of fraudulent activities in the banking industry, which have resulted in significant financial losses for banks and their customers. AI-based systems have the potential to effectively identify and prevent fraudulent activities in real time, providing a significant advantage over traditional fraud detection methods. Detecting fraudulent banking operations involves using AI and machine learning algorithms to analyze large amounts of data from multiple sources, including transaction records, customer information, and network logs. These algorithms can identify patterns and anomalies in the data that may indicate fraudulent activities, such as unauthorized access, unusual transaction patterns, and suspicious behavior. A bank transaction involves any activity related to a bank account, which can be carried out online or offline between all parties involved. The process concludes when a written or electronic order is submitted to the bank, using internet banking systems, communication systems, or payment instruments. Bank transactions fall into two categories: genuine and fraudulent transactions. The latter refers to those that violate financial circulation rules or were not authorized. Common types of banking fraud include wire fraud, identity theft, account takeover, money laundering, and accounting fraud. As fraud becomes increasingly sophisticated, we must develop new methods to protect ourselves against it. Below are the five most common methods of preventing bank fraud: artificial intelligence, biometric data, consortium data, standardization of high technologies, and machine learning. Following the outbreak of the COVID-19 pandemic and the war in Ukraine, fraudulent operations related to bank transactions have become even more common due to the significant shift toward online transactions as well as the creation of numerous charitable funds that criminals use to deceive users. Therefore, it is necessary to create reliable automated algorithms to recognize and prevent operations that threaten the finances and accounts of individuals, violate taxation or financing rules or laws, and so on. The presented study focused on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. This study aimed to develop machine learning models that recognize fraudulent banking transactions, especially during the COVID-19 pandemic and war in Ukraine when online transactions and charitable funds have become more prevalent.

Our project focused on using machine learning models to identify fraudulent banking transactions. We also applied preprocessing techniques to compare and select the most effective outcomes from bank data. To accomplish our goals, we took the following steps: Developed several machine learning models using various methodologies and strategies. Compared and assessed the models from the previous stage using both quantitative and visual criteria. Analyzed the results obtained and drew a conclusion about the research objective. This study focused on using machine learning models to detect fraudulent banking transactions. The research aimed to develop algorithms that can accurately recognize such transactions. The methods used included preprocessing techniques and machine learning algorithms. The significance of this work lies in the potential of the proposed method to improve the detection of fraudulent banking transactions, especially during the pandemic when many transactions have shifted online and during times of war when there are many charities and events collecting money. The task of recognizing fraudulent bank transactions using machine learning involves identifying a particular transaction at a specific moment in time as either real or fraudulent based on previous historical data about other transactions. This process uses binary logic, where the transaction is either real or fraudulent, and classification algorithms are suitable for performing the task using machine learning methods. This paper proposes applying several classification algorithms that recognize the type of transaction based on certain features, along with preprocessing techniques. In order to effectively identify fraudulent transactions, the machine learning algorithm must have access to a comprehensive historical database of such activities. The existing collection of legitimate transactions that have yet to be flagged is encrypted to maintain the confidentiality and privacy of the financial institution’s clientele. However, this encryption does not hinder the algorithm’s ability to perform. Financial institutions can effectively detect and prevent fraudulent transactions by training models using this carefully selected dataset. Implementing AI technology in detecting fraudulent banking operations poses several challenges, including the utilized algorithms’ lack of transparency and interpretability. The intricacies of these algorithms can sometimes be challenging to comprehend, which can impede the identification and rectification of errors. Furthermore, using AI in fraud detection raises essential concerns regarding privacy, as personal data are subject to analysis and utilization in the decision-making process. These challenges require careful consideration to ensure AI-powered fraud detection systems’ safe and accurate implementation. The application development based on AI for fraudulent banking operations recognition is an active area of research and development, with significant potential to improve the efficiency and accuracy of fraud detection. However, addressing the challenges and concerns associated with using AI in fraud detection is essential to ensure its effectiveness and ethical use in the banking industry. Limitation of studies in the financial field. Studies using artificial intelligence to detect bank fraud are valuable. However, it is important to note that this study focuses on identifying fraudulent transactions in online banking only, while other types of financial fraud may require different detection methods. Additionally, the study’s reliability and generalizability may be affected by its limited sample size in the financial field. Data availability is also a crucial factor, as high-quality data re needed to train and test machine learning algorithms. The accuracy of the model can be compromised by incomplete and insufficiently diverse datasets, leading to false positives in real-world situations. Another challenge is the potential for human biases in the selection and analysis of data, which can impact the method’s validity and reliability. It is also important to avoid overfitting, where the model performs well on the training dataset but poorly on the test dataset due to its complexity and limited generalizability.

To meet the goals outlined in this study, we employed classification algorithms. These algorithms use features to determine the class of a given object. Machine learning relies on labeled training data to categorize new observations. To make accurate predictions for future observations, these algorithms must first analyze a dataset of examples with features and corresponding classes. They are considered supervised learning techniques because they map input variables (x) to discrete output functions (y) that represent categories rather than numerical values. The output of classification algorithms is not continuous but discrete. The classification algorithm learns from labeled input data, where input data have a corresponding output. The goal of classification algorithms is to limit the category of a given dataset, and they are widely used to predict the output for categorical data. From the information provided in the previous paragraph, it is clear that training any classification model requires a data set that shows the relationship between certain feature sets of an object and its class or category. This is why, to fit our classification model to the given task, we chose to use the dataset named Credit Card Fraud Detection obtained from the Kaggle platform. The platform provides the ROC graph curve and AUC metric for each algorithm and technique. A similar set of metrics is provided for other implementations as well. It is also easy to run the program using the Kaggle platform (which was used to create this software solution), which, by default, supports running Jupyter notebooks. To start, the “Run all” was pressed button to continue the sequential execution of all commands. An alternative solution is using any software that supports Jupyter notebooks. The concrete program implementation as saved as a notebook on the Kaggle platform and started by loading a dataset using the Pandas library. The research workflow was organized as given in Figure 3. Figure 3 represents the stages of a high-level algorithm for a machine learning program solution, which includes dataset selection and loading, feature standardization, random undersampling, model fitting, model testing, and outputting the best model. The presented method uses several mathematical elements: Machine learning algorithms, Evaluation metrics, Data preprocessing technique.

The machine learning algorithms chosen to be used here were (1) random forest, (2) k-nearest neighbors, (3) logistic regression, (4) stochastic gradient descent classifier, (5) decision tree, (6) naïve Bayes, and (7) support vector machine. Decision tree. In machine learning, a decision tree is a tree structure that shares similarities with a flowchart. Each internal node of the tree represents an attribute check, while each branch corresponds to the outcome of the check. Finally, every end node, or final node, contains a class label. The initial set is divided into subsets to train the decision tree based on checking the attribute’s value. The described process is recursive partitioning, repeated recursively for each derived subset. The recursion split ends in case of partitioning are no longer beneficial for predictions. Classification based on decision tree methods does not require knowledge of the domain or parameter tuning, making it an excellent choice for exploring knowledge. Furthermore, decision trees handle large amounts of data and typically provide high accuracy. Decision tree induction is a standard inductive approach for learning classification data. To classify instances, decision trees sort them through the tree, starting at the root and ending at a leaf node that classifies the instance. Random Forest. Specialists have used random forest methods for classification and regression tasks. This machine-learning-based algorithm is based on a flexible and user-friendly algorithm consisting of decision trees. The strength of the forest increases with the number of trees. The algorithm creates decision trees using randomly selected data samples, obtains predictions from each tree, and selects the best solution by voting. Additionally, it indicates feature importance. The algorithm works in the following steps: (1) selecting random samples, (2) building a decision tree, (3) voting, and (4) selecting the prediction result as the final prediction. Logistic regression is a commonly used statistical model for classification and predictive analytics, estimating the probability of an event based on a given set of independent variables. Logistic regression transforms the odds using a logit transformation, the logarithm of the odds, or the natural logarithm of the odds. Support vector machine (SVM) is a supervised learning algorithm for classification tasks and regression assignments. SVM creates a decision boundary, or hyperplane, that divides n-dimensional space into classes. The hyperplane is created by selecting extreme points, or support vectors, that help define the boundary. K-nearest neighbors (KNN) is a supervised learning nonparametric classifier. This classifier utilizes proximity to classify or predict the grouping of an individual data point. KNN is commonly used as a classification algorithm and assigns a class label based on the majority vote of nearby data points. For classification tasks with multiple classes, the class label is assigned with more than 25% of the vote rather than a strict majority of over 50%. The stochastic gradient descent (SGD) classifier is an approach that is straightforward and remarkably efficient in adapting linear classifiers and regressors to convex loss functions, including logistic regression and support vector machine. However, SGD has recently received substantial attention in the realm of large-scale learning. This is due to the success of SGD in tackling vast and sparse machine-learning challenges commonly encountered in natural language processing and text classification. Due to the sparsity of the data, the classifiers developed using SGD have rapidly expanded to deal with problems with over 105 training examples and over 105 features. The naïve Bayes classifier is a probabilistic machine learning model for classification tasks. The classifier’s foundation is based on Bayes’ theorem. The naïve Bayes classifier assumes that all predictors or features are independent, meaning that the presence of one feature does not affect the other. The Bayes formula is represented as the following equation: P(AB)=P(BA)P(A)P(B), where P(A) is the event A probability, P(B) is the event B probability, and P(BA) is the probability of event B occurring when event A occurs. In probability theory, the probability of an event A occurring is denoted by P(A), while the probability of event B occurring is denoted by P(B). Furthermore, the conditional probability of event B happening certainly that event A has happened is represented as P(BA).

Stacked generalization is a widely used method that integrates multiple low-level models to improve the overall predictive accuracy of a high-level model. This technique is based on estimating the biases of the high-level model concerning a given learning dataset. The estimation process involves generalizing the biases in a second space, using the original models’ predictions as inputs and the correct answers as outputs. Stacked generalization is considered an enhanced version of cross-validation, aggregating individual models into a higher-level model. Recently, a new method for grouping machine learning models based on random forest as a meta-algorithm has been developed, and its mathematical formulation is presented below. Randomly generate the following from the original dataset K cross-sectional data sets: a11,…,a1B, a21,…,a2B,…, aK1,…,aKB, where K is the number of subsets, B is the size of the subset, and alb is the observation of the lth sample. The task is to train K-independent weak classifiers f1(.),…,fk(.)…

Based on the paper, here are the specific improvements I can make to an AI system for fraudulent banking operation recognition:

• Improvement: Implement the paper's stacked generalization approach, which combines multiple weak classifiers (decision tree, logistic regression, SVC, KNN, SGD, naïve Bayes, random forest) with a meta-model to achieve an AUC of 0.954, outperforming the best individual model (logistic regression at 0.946).

• What it can do: The system can reduce bias and variance simultaneously, preventing overfitting while improving overall detection accuracy beyond any single algorithm.

• Improvement: Integrate the paper's random undersampling technique to address the highly imbalanced dataset (492 fraudulent transactions out of 284,807 total, i.e., 0.17% fraud rate).

• What it can do: The system can effectively learn from extremely rare fraudulent events without being overwhelmed by the majority class, reducing false negatives where actual fraud goes undetected.

• Improvement: Apply the paper's standardization technique (X' = (X - μ) / σ) specifically to non-encrypted features (time and amount) while preserving the PCA-transformed features.

• What it can do: The system can normalize transaction amounts and timing across different scales, ensuring that large-value transactions don't dominate the model's learning and that temporal patterns are properly weighted.

• Improvement: Use the paper's evaluation framework with ROC curves and AUC metrics as the primary performance indicator, rather than relying solely on accuracy or F1 scores.

• What it can do: The system can provide threshold-independent performance assessment, allowing operators to tune the classification threshold based on their specific tolerance for false positives versus false negatives in real-world deployment.

• Improvement: Implement the paper's approach of training seven distinct algorithms (decision tree, random forest, logistic regression, SVC, KNN, SGD, naïve Bayes) with grid search for hyperparameter optimization.

• What it can do: The system can automatically select the best-performing algorithm for a given dataset distribution, adapting to different banking environments without manual algorithm selection.

• Improvement: Structure the system as a binary classifier that determines whether a specific transaction at a given moment is genuine or fraudulent, using historical transaction data as training input.

• What it can do: The system can process individual transactions in real-time, providing immediate fraud detection alerts without requiring batch processing or historical context of the current transaction.

• Improvement: Incorporate the paper's handling of PCA-encrypted features, which maintains confidentiality of customer data while still enabling effective fraud detection.

• What it can do: The system can operate on encrypted financial data without compromising detection accuracy, making it compliant with data protection regulations while maintaining security.

• Improvement: Design the system to run on cloud-based notebook environments (like Kaggle) that require no local computing power.

• What it can do: The system can be deployed and accessed from any device with internet connectivity, enabling rapid implementation across multiple banking institutions without infrastructure investment.

1. Real-time fraud detection with 95.4% AUC accuracy, capable of flagging suspicious transactions as they occur.

2. Adaptive algorithm selection that automatically identifies the best-performing model for the specific data distribution of each banking institution.

3. Handling of extreme class imbalance (fraud rates as low as 0.17%) without sacrificing precision or recall.

4. Privacy-compliant operation on encrypted transaction data, protecting customer identities while maintaining detection capability.

5. Cross-platform deployment requiring only a web browser and internet connection, eliminating hardware dependencies.

6. Threshold tuning capability allowing banks to adjust sensitivity based on their risk tolerance and regulatory requirements.

7. Ensemble robustness that maintains high performance even when individual algorithms fail on specific transaction patterns.

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study`s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0,946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.