Browsing by Author "Pels, Wilhemina Adoma"

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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.
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    Shrinkage Methods for Estimating the Shape Parameter of the Generalized Pareto Distribution
    (Journal of Applied Mathematics, 2023) Pels, Wilhemina Adoma; Adebanji, Atinuke O.; Twumasi-Ankrah, Sampson; Minkah, Richard; https://orcid.org/0000-0001-7881-3069
    The generalized Pareto distribution is one of the most important distributions in statistics of extremes as it has wide applications in fields such as finance, insurance, and hydrology. This study proposes two new methods for estimating the shape parameter of the generalized Pareto distribution (GPD). The proposed methods use the shrinkage principle to adapt the existing empirical Bayesian with data-based prior and the likelihood moment method to obtain two estimators. The performance of the proposed estimators is compared with the existing estimators (i.e., maximum likelihood, likelihood moment estimators, etc.) for the shape parameter of the generalized Pareto distribution in a simulation study. The results show that the proposed estimators perform better for small to moderate number of exceedances in estimating shape parameter of the light-tailed distributions and competitive when estimating heavy-tailed distributions. The proposed estimators are illustrated with practical datasets from climate and insurance studies.