Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Chung, Jiwon; Lee, Kyungho

doi:10.3390/s23187788

Cited by 5 publications

(2 citation statements)

References 23 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the realm of fraud detection, the significance of recall is notably magnified, as highlighted in the literature [39]. This emphasis stems from the understanding that the consequences of overlooking a genuine case of fraud carry far more weight than mistakenly flagging a legitimate transaction as suspicious.…”

Section: Discussionmentioning

confidence: 99%

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Núñez Delafuente,

Astudillo,

Díaz

2024

Mathematics

View full text Add to dashboard Cite

Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust. The prevalent reliance on supervised learning models for detecting such manipulations, while showing promise, faces notable hurdles due to the dearth of labeled data and the inability to recognize novel manipulation tactics beyond those explicitly labeled. This study ventures into addressing these gaps by proposing a novel detection framework aimed at identifying suspicious hourly manipulation blocks through an unsupervised learning approach, thereby circumventing the limitations of data labeling and enhancing the adaptability to emerging manipulation strategies. Our methodology involves the innovative creation of features reflecting the behavior of stocks across various time windows followed by the segmentation of the dataset into k subsets. This setup facilitates the identification of potential manipulation instances via a voting ensemble composed of k isolation forest models, which have been chosen for their efficiency in pinpointing anomalies and their linear computational complexity—attributes that are critical for analyzing vast datasets. Evaluated against eight real stocks known to have undergone manipulation, our approach demonstrated a remarkable capability to identify up to 89% of manipulated blocks, thus significantly outperforming previous methods that do not utilize a voting ensemble. This finding not only surpasses the detection rates reported in prior studies but also underscores the enhanced robustness and adaptability of our unsupervised model in uncovering varied manipulation schemes. Through this research, we contribute to the field by offering a scalable and efficient unsupervised learning strategy for stock manipulation detection, thereby marking a substantial advancement over traditional supervised methods and paving the way for more resilient financial markets.

show abstract

Section: Discussionmentioning

confidence: 99%

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Núñez Delafuente,

Astudillo,

Díaz

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…To assess credit card fraud while considering the skewness of fraud instances, the study [40] makes use of logistics regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and autoencoder (AE) as they can handle skewed data better than other models, the AE model performs better. KNN, linear discriminant analysis (LDA), and linear regression are used in the study [41] to investigate credit card fraud, by addressing the skewed nature of the credit card fraud data and using cross-validation techniques, KNN showed higher performance. Using ARIMA model for fraud detection based on daily transaction counts, the study [14] carried out anomaly detection, the model is contrasted with four industry-standard anomaly detection algorithms: the box plot, isolation forest(IF), local outlier factor (LOF), and Kmeans models.…”

Section: Machine Learning Methods In Fraud Detectionmentioning

confidence: 99%

Financial Fraud Detection Using Value-at-Risk With Machine Learning in Skewed Data

Usman,

Abdullahi,

Liping

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The significant losses that banks and other financial organizations suffered due to new bank account (NBA) fraud are alarming as the number of online banking service users increases. The inherent skewness and rarity of NBA fraud instances have been a major challenge to the machine learning (ML) models and happen when non-fraud instances outweigh the fraud instances, which leads the ML models to overlook and erroneously consider fraud as non-fraud instances. Such errors can erode the confidence and trust of customers. Existing studies consider fraud patterns instead of potential losses of NBA fraud risk features while addressing the skewness of fraud datasets. The detection of NBA fraud is proposed in this research within the context of value-at-risk as a risk measure that considers fraud instances as a worst-case scenario. Value-at-risk uses historical simulation to estimate potential losses of risk features and model them as a skewed tail distribution. The risk-return features obtained from value-at-risk were classified using ML on the bank account fraud (BAF) Dataset. The value-at-risk handles the fraud skewness using an adjustable threshold probability range to attach weight to the skewed NBA fraud instances. A novel detection rate (DT) metric that considers risk fraud features was used to measure the performance of the fraud detection model. An improved fraud detection model is achieved using a K-nearest neighbor with a true positive (TP) rate of 0.95 and a DT rate of 0.9406. Under an acceptable loss tolerance in the banking sector, value-at-risk presents an intelligent approach for establishing data-driven criteria for fraud risk management.

show abstract

Encoder–decoder graph neural network for credit card fraud detection

Cherif,

Ammar,

Kalkatawi

et al. 2024

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Cited by 5 publications

References 23 publications

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Financial Fraud Detection Using Value-at-Risk With Machine Learning in Skewed Data

Encoder–decoder graph neural network for credit card fraud detection

Contact Info

Product

Resources

About