Browsing by Author "Ayeh, Samuel Amening"

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    A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions
    (Decision Analytics Journal, 2023-03-06) Afriyie, Jonathan Kwaku; Tawiah, Kassim; Pels, Wilhemina Adoma; Addai-Henne, Sandra; Dwamena, Harriet Achiaa; Owiredu, Emmanuel Odame; Ayeh, Samuel Amening; Eshun, John; 0000-0001-6997-7969
    Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine learning models: logistic regression, random forest, and decision trees to classify, predict, and detect fraudulent credit card transactions. We compare these models’ performance and show that random forest produces a maximum accuracy of 96% (with an area under the curve value of 98.9%) in predicting and detecting fraudulent credit card transactions. Thus, we recommend random forest as the most appropriate machine learning algorithm for predicting and detecting fraud in credit card transactions. Credit Card holders above 60 years were found to be mostly victims of these fraudulent transactions, with a greater proportion of fraudulent transactions occurring between the hours of 22:00GMT and 4:00GMT.
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    A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions
    (Decision Analytics Journal, 2023-03) Afriyie, Jonathan Kwaku; Tawiah, Kassim; Pels, Wilhemina Adoma; Addai-Henne, Sandra; Dwamena, Harriet Achiaa; Owiredu, Emmanuel Odame; Ayeh, Samuel Amening; John Eshun; https://orcid.org/0000-0001-7881-3069
    Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine learning models: logistic regression, random forest, and decision trees to classify, predict, and detect fraudulent credit card transactions. We compare these models’ performance and show that random forest produces a maximum accuracy of 96% (with an area under the curve value of 98.9%) in predicting and detecting fraudulent credit card transactions. Thus, we recommend random forest as the most appropriate machine learning algorithm for predicting and detecting fraud in credit card transactions. Credit Card holders above 60 years were found to be mostly victims of these fraudulent transactions, with a greater proportion of fraudulent transactions occurring between the hours of 22:00GMT and 4:00GMT.