Credit Card Fraud Detection using Machine Learning: A Study

Tiwari, Pooja; Mehta, Simran; Sakhuja, Nishtha; Kumar, Jitendra; Singh, Ashutosh Kumar

doi:10.48550/arxiv.2108.10005

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…During production for cell therapy manufacturing, we envisage sample contamination events as rare. Consequently, future model optimization must be considered through this data imbalance, as observed in fraud detection for better anomaly prediction ( 30 – 32 ). Alternatively, it might be possible to replace the metagenome classifier and binary classifiers with a single step natural-language processing (NLP)-based approach using, for example, DNABERT ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Strutt,

Natarajan,

Lee

et al. 2023

Microbiol Spectr

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Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7–14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP <71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7–14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Strutt,

Natarajan,

Lee

et al. 2023

Microbiol Spectr

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“…AI has specific consumer protection risks, including biased, unfair, or discriminating outcomes for consumers, as well as concerns related to data management and utilization. Many AIrelated financial concerns are not unique to AI, but the complexity of approaches, the dynamic flexibility of AIbased models, and the high autonomy of sophisticated AI applications may accentuate these vulnerabilities [9,10].…”

Section: Introductionmentioning

confidence: 99%

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

Pranav Kumar Chaudhary, Aakash Kishore Chotrani

2024

jes

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AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.

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“…Moreover, the co-residency of virtual machines of multiple users on a common physical server often leads to malicious activities like, data hampering and leakage of cloud user's confidential data [10,[92][93][94][95][96][97][98][99][100][101][102]. The malicious user or hacker launches one or multiple of virtual machines and exploits multiple network routes by achieving co-residency with the target virtual machine [29,[103][104][105][106][107][108][109][110][111][112]. The vulnerabilities and susceptibilities of virtual machines management layer and hypervisor facilitates malicious user to get access to target physical machine and compromise all the virtual machines hosted on it.…”

Section: Introductionmentioning

confidence: 99%

A Review on Secure Cloud Resource Management

Chhabra¹,

Saxena²,

Rekha³

2023

Preprint

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<p>Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.</p>

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Credit Card Fraud Detection using Machine Learning: A Study

Cited by 8 publications

References 34 publications

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

A Review on Secure Cloud Resource Management

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