The findings demonstrate that a software-based prototype integrating machine learning with image analysis can effectively simulate the core functions of a physical counterfeit-detecting banknote counter.
Abstract
Counterfeit currency circulation remains a persistent threat to economic integrity, particularly in cash-dependent economies where manual verification is both time-consuming and error-prone. This paper presents the design and simulation of a currency counting system integrated with an automated counterfeit detection mechanism. The proposed system employs image processing techniques—including grayscale conversion, Gaussian blur, Canny edge detection, and Contrast Limited Adaptive Histogram Equalization—to extract discriminative features from scanned currency note images. Four feature categories are utilized: color histogram, texture, edge, and Oriented FAST and Rotated BRIEF keypoint features. A Random Forest classifier, trained on a labelled dataset of genuine and counterfeit Nigerian Naira note images, performs binary classification of each uploaded note. The system subsequently counts total notes, segregates genuine from counterfeit samples, and computes the aggregate monetary value of authenticated notes only. A web-based interface, developed using Streamlit, provides an accessible and interactive platform for real-time note scanning and result visualization. Experimental testing confirmed that the system correctly processes uploaded images, applies the trained classification model, and returns accurate counting and valuation outputs. The findings demonstrate that a software-based prototype integrating machine learning with image analysis can effectively simulate the core functions of a physical counterfeit-detecting banknote counter. Future work will incorporate ultraviolet, infrared, and magnetic sensor modules alongside hardware implementation using a microcontroller-driven mechanical platform.
Financial Fraud threatens global financial stability gravely. Fraud comes with important economic losses and has negative effects on public trust. Traditional rule-based systems and older methods of machine learning cannot detect these patterns of fraud, especially when facing complex or continuously changing fraud patterns in the era of high-speed and high-volume transactions. This research proposes a deep learning technique for the detection of financial fraud by using Convolutional Neural Networks on reshaped tabular transaction data. The proposed CNN model utilizes spatial feature extraction by reprocessing 1D financial records into 2D matrices. This method aims in recognizing local relationships among features as indicative of fraudulent activity. Preprocessing steps for this proposed system include class balancing, scaling of features, and one-hot encoding. It is tested on a highly imbalanced credit card fraud dataset from Kaggle. Then, the classification accuracy was 99.23%. An accuracy of 98.6%, a recall of 97.9%, an F1-score of 98.25%, and an AUC-ROC of 0.992 were also reported. These results indicate that the CNN model can capture the hidden patterns of fraud well when compared to traditional methods. Furthermore, they are less dependent on manual feature engineering and adaptive to evolving fraud strategies.
Sardar Ali, Abdulmajeed Adil Yazdeen, Rozin Majeed Abdullah et al.· Science Journal of Universit...· 0 citations
Credit card theft costs the global banking system billions of dollars every year and erodes user confidence in electronic
payments. Fraudulent transactions are rare (usually < 0.2 % of all records) and this rarity poses a basic problem for automated
detection: a classifier trained on raw data learns to predict the majority class. The resulting model is surprisingly accurate in
general, yet fails to detect much of the fraud it was created for. To directly address such imbalance, this research integrates a
bespoke Random Forest classifier with the Synthetic Minority Over-sampling Technique (SMOTE). The pipeline is tested on the
public European credit card transaction dataset with 284,807 transactions with just 492 (0.172%) fraud cases. The SMOTE-RF
model therefore performs better than Logistic Regression, Decision Trees and the normal Random Forest without oversampling,
with a precision of 0.947, a recall of 0.921, an F1-score of 0.934, a Matthews Correlation Coefficient (MCC) of 0.929 and an
AUC-ROC of 0.983. An ablation research reveals that most of the recall gain is driven by SMOTE, whereas precision is mostly
enhanced by tuning Random Forest hyperparameters. All these results indicate that SMOTE-RF is a feasible and interpretable
solution for real-world fraud detection.
S. Mohite, Puneet Nahar· International Journal for Re...· 0 citations
For identifying fraud within a vast array of digitally conducted financial transactions, it is imperative for financial transaction systems to be scalable and accurate. One of the common uses of machine learning in the financial sector is credit card fraud detection, particularly when manual monitoring of the massive volume of financial transactions is impractical. The proposed system is designed using the XGBoost classifier. This approach improves the ability to detect fraudulent credit card transactions through various behavioral and geographical aspects, including transaction time, transaction frequency, and the distance between the cardholder and merchant locations. Experimental results indicate that the proposed system is capable of detecting fraudulent transactions with a precision of approximately 99.8% and a high ROC-AUC score. The scalable framework designed in this study can assist financial organizations in improving the security and effectiveness of fraud detection systems.
J. Kumar, J. Vasu, Kalagadda Kiran Kumar· 2026 6th International Confe...· 0 citations
UPI fraud has become a major challenge due to the rapid growth of digital payments. Fraudulent activities such as
fake payment requests, phishing, identity theft, and unauthorized transactions can cause significant financial losses to users and
banks. Therefore, detecting fraud at an early stage is important to ensure secure online transactions. This project develops a UPI
Fraud Detection System using the Random Forest machine learning algorithm. The system is trained using a dataset that
contains different transaction details such as transaction amount, transaction time, payment method, location, device
information, and transaction history. The Random Forest algorithm is chosen because it provides high accuracy, handles large
datasets effectively, and reduces the chances of incorrect predictions. The model analyses transaction patterns and classifies
each transaction as either genuine or fraudulent. Its performance is evaluated using standard metrics such as accuracy,
precision, recall, and F1-score. The results show that the Random Forest algorithm performs better than many traditional
machine learning algorithms in detecting fraudulent transactions. The proposed system helps banks and digital payment
platforms identify suspicious transactions quickly, reducing financial losses and improving user security. In the future, the
system can be enhanced by integrating real-time transaction monitoring, user behaviour analysis, and advanced AI techniques
to improve fraud detection accuracy and provide a safer digital payment experience
Avu Siva Dinesh Kumar, G. Kumar· International Journal for Re...· 0 citations
The rise of digital payments has magnified credit card fraud issues the complexity, scope and therefore the attack strategies have evolved to become an enormous obstacle for the traditional, static (rule based) and single- modal methods of ML in learning dynamic transactional patterns. In this paper we introduced a multimodal fraud detection approach combining the typical numerical features characterizing single transactional entities with their time- series transaction behaviour so that we could achieve higher accuracy in identifying frauds. The former represents properties for each transaction; temporal patterns in user spending are captured by a Gated Recurrent Unit (GRU), a network effectively modelling sequences of events (purchase history in our context) and then combined to finally perform binary classification in which either fraudulent or real transaction class is detected. We use the public domain 284,807 transaction records from credit card dataset; fraud transactions account for 0.172 of the samples. The experiment shows our novel framework (multimodal GRU) outperformed existing ML algorithms and single-mode techniques, having the recall reach 0.89, F1-score value 0.90 and AUC score 0.99. It’s highly scalable and efficient to use in real time monitoring applications. Our proposed method of combining two types of features: a set of single number characteristics and sequence-based information to successfully solve this challenging problem in modern finance.
J. J, S. S, Sabari Rr· 2026 International Conferenc...· 0 citations
Financial fraud causes significant economic loss and erodes trust in digital payment ecosystems. Traditional rule-only
systems struggle with evolving attack patterns, while pure black-box machine learning models are difficult for analysts to
interpret during investigations. This paper presents **FraudX**, an end-to-end explainable fraud detection framework that
combines supervised learning with rule-based categorization and human-readable explanations. The system generates a labeled
synthetic transaction dataset of 10,000 records with 20+ behavioral and contextual features, trains a Random Forest classifier
with standardized preprocessing, and deploys inference through an interactive Streamlit dashboard. For each transaction,
FraudX outputs a probability-based *Suspicion Score*, a threshold-controlled suspicious flag, a prioritized *Fraud_Type* label,
and concise *Suspicion_Reasons*. Experimental evaluation on held-out synthetic test data reports 95.2% accuracy, 94.8%
precision, 93.5% recall, and 94.1% F1-score. The proposed architecture demonstrates how operational thresholding, featureimportance-driven explanations, and analyst-oriented visualization can be integrated into a practical fraud triage workflow
suitable for academic demonstration and prototype deployment.
Harshali P. Patil· International Journal for Re...· 0 citations
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A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
