2023· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
The fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner is demonstrated.
Abstract
Financial fraud is moving very swiftly in today’s technologically advanced where everything is connected. This makes it extremely challenging for banks and other institutions of finance to follow the rules, preserve their customer trust, and run their businesses with integrity. Traditionally based on rules, detection systems can miss both small and big dangers when the number of transactions goes up and schemes for fraud get more intricate. Combining Machine Learning (ML) with Customer Relationship Management (CRM) data models is a powerful and versatile technique to stop fraud in this instance. Machine learning algorithms can identify hidden problems and anticipate fraud faster and more precisely by integrating information from CRM systems about past interactions with customers, behavior, and transactions in general. This work investigates a methodology that incorporates supervised and unsupervised methods of learning with enhanced CRM datasets in order to create sophisticated detection of fraud models. The methodology demonstrates data preprocessing, standardized feature engineering, and model training based on real financial parameters, including transaction frequency, alterations in typical customer behavior, and assessment of risk ratings. It also says that integrating CRM makes it less difficult for businesses to see the big picture of their customers, which enables these individuals to go from checking transactions by themselves to making decisions according to the situation. The suggested technique is to continually acquire knowledge and enhance the model so that it can keep up with the latest fraud strategies while minimizing the number of false positives that might adversely affect actual customers. This paper demonstrates the fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner.
Financial transaction fraud is an ongoing threat with significant economic loss and on customers' trust. This paper discusses Fraud Detection in detail with machine learning technique on a given data set of a transaction. We investigate the patterns revealed from the users and the transactions in the database when the user is performing fraudulent transactions, and test several classification models that can be used to detect fraud, which includes logistic regression model, random forests, support vector machines, gradient boosting, and neural networks. The performance of the models is investigated in terms of accuracy, precision, recall, F1 score and ROC-AUC metrics. Based on our experiments, the best detection overall performances are obtained for the tree-based ensemble models (Random Forest and XGBoost) with XGBoost getting the most optimum fraud Recall and F1-Score. Through the data analysis results (such as account age, transaction frequency etc.) and the model comparison, we expound an improved method which is based on combining the ensemble of best models with data imbalance countermeasures to increase the recall of fraudulent cases. We also have an end-to-end machine learning pipeline on Python to detect frauds from preprocessing the data, training the models, evaluating them, and deploying for fraud prediction. Also, a literature review of twenty-five recent studies on fraud detection is given, and the algorithms used, datasets and major contributions of these studies were summarized. The textbook ensemble technique, as proposed gives better fraud detection performance as it gains on the order of ~3-5% improvement against the best single model performance on F1-score, with acceptable precision, thereby underscoring the usefulness of hybrid modeling with specialized techniques for this field. The results emphasize that utilizing various models and domain-specific feature engineering can be of great benefit in fraudulent transaction detection, while also neg
The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.
Jabulani Khumalo, Min Joon Kim· Global Knowledge Academy· 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
It is found that using more advanced fraud detection systems can reduce financial losses and increase customer trust, which supports overall economic stability and highlights the need to tailor solutions to specific regional and operational conditions.
Sungho Kim, R. Rafi, Shahadat Hossain et al.· International Journal of Gri...· 0 citations
The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction.
G. S. Rahul Gorpade, A. M· International Research Journ...· 0 citations
Purpose: Public sector financial systems process large volumes of payments, procurement transactions and grants and remain exposed to fraud, corruption and error. Traditional audit-based controls detect only a small share of irregular transactions and usually do so late. This study examines whether machine learning can improve detection of fraudulent transactions in public sector financial systems and compares model families under conditions resembling real government data, including severe class imbalance and scarce fraud labels. Design/methodology/approach: The study builds a synthetic public-sector transaction dataset of 60,000 records that reproduces documented properties of government payment data, including low fraud prevalence, vendor concentration and period-end clustering. Four models are trained and compared: logistic regression, random forest, XGBoost and isolation forest. Models are evaluated using precision, recall, F1 score, area under the receiver operating characteristic curve and area under the precision-recall curve, since accuracy alone misleads under imbalance. Findings: XGBoost achieved the strongest balance of precision and recall (F1 = 0.762, AUC-ROC = 0.992), followed by random forest (F1 = 0.748). Logistic regression achieved high recall but low precision, producing many false alerts. Isolation forest, which is an unsupervised method, performed reasonably well without labelled fraud, which matters since most agencies lack reliable labels. Amount, vendor age, single-bidder procurement and prior irregularity counts were the strongest predictors. Originality/value: This study contributes a reproducible, coded evaluation framework for public sector fraud analytics and gives practitioners evidence-based guidance on model choice under label scarcity and class imbalance, which are conditions that dominate real government settings.
Keywords: Fraud Detection, Machine Learning, Public Sector, Financial Systems, Public Procurement, Anomaly Detection, Government Auditing, XGBoost.
Emmanuel Afriyie Okyem, Robert Kwasi Aboagye Mensah Yeboah, T. Adukpo· Finance & Accounting Res...· 0 citations
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