Advancing Credit Card Testing Automation: Integrating Serverless Computing at Capital One; Ethical Paradigms and Socio-Technical Equity in Financial Algorithms

The technical project undertaken at Capital One focuses on leveraging software engineering principles to streamline credit card test automation processes. This initiative aimed to develop a sophisticated full-stack web application designed to optimize the efficiency of test case management and execution within the agile development framework used by Capital One's engineering teams. Leveraging modern technologies and cloud services, the project sought to address key challenges in test automation, including scalability, usability, and integration with existing financial systems. By improving these processes, the project contributes to the broader goals of enhancing operational efficiency, reducing time-to-market for new financial products, and ultimately improving customer satisfaction in the fintech sector.
The STS thesis, "Algorithmic Biases in Financial Decisions," explores the ethical challenges shown in the deployment of artificial intelligence within the financial industry through a literature review. It critically examines the mechanisms through which biases are encoded into algorithmic decision-making systems, significantly impacting fairness and transparency in financial services. The research draws upon an array of scholarly works, case studies, and regulatory frameworks to dissect the socio-technical underpinnings of these biases. Through an analysis leveraging frameworks like the Actor-Network Theory (ANT), the thesis advocates for a concerted effort among technologists, policymakers, and financial institutions to address and mitigate the effects of these biases. The proposed solutions emphasize the importance of ethical AI development, the adoption of transparent algorithmic processes, and the enhancement of regulatory measures to safeguard consumer rights and ensure equitable financial outcomes.