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Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.