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.
The results indicate that the CNN model can capture the hidden patterns of fraud well when compared to traditional methods, and 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
This research integrates a bespoke Random Forest classifier with the Synthetic Minority Over-sampling Technique (SMOTE), and 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
A UPI Fraud Detection System using the Random Forest machine learning algorithm that helps banks and digital payment platforms identify suspicious transactions quickly, reducing financial losses and improving user security.
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
FraudX is presented, an end-to-end explainable fraud detection framework that combines supervised learning with rule-based categorization and human-readable explanations and 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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