International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Product Owner’s Guide to Machine Learning: Case Study - Credit Card Fraud Detection

Author(s) Sachin Gadiyar, Archana Umakanth
Country United States
Abstract Machine Learning (ML) models are increasingly being integrated into the financial industry to enable development of intelligent, data driven products. For Product Owners to successfully guide teams and drive the strategic use of ML in their products, it is crucial to understand the end-to-end process of ML model development. This paper provides an accessible framework for product owners, using an example of Credit Card Fraud Detection, to grasp the key stages involved in building ML models. It covers the foundational concepts, including data preprocessing, model selection, training and evaluation. Additionally, it explores critical topics such as model performance metrics, collaboration with data science and engineering teams, and the importance of continual monitoring and iteration. By demystifying the ML process, this paper equips product owners with the knowledge to effectively prioritize features, manage expectations, ensure ethical practices, and leverage ML to drive product innovation. The goal is to empower product owners to make informed decisions, communicate effectively with technical teams, and ultimately deliver impactful ML-powered products that meet both business objectives and customer needs.
Keywords Artificial Intelligence, Machine Learning, Credit Cards, Fintech, Data Science, Analytics, Fraud Detection, Random Forest, XGBoost, ANN
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-05
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.38168
Short DOI https://doi.org/g86xs5
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