Journal of Artificial intelligence and Machine Learning

AI-Driven Decision Engine Implementation in Retail Banking: A Multi-Criteria Analysis of Credit Card Approval Using TOPSIS Method

2025-02-02T10:05:01+00:00

This research delves into the deployment and efficacy of an AI-powered decision engine within the intricate domain of retail banking, with a particular emphasis on optimizing credit card approval mechanisms. In an era where financial institutions face mounting pressures to balance operational efficiency with meticulous risk mitigation, the study underscores the escalating demand for systems that marry precision with automation.What renders this investigation particularly compelling is its exhaustive exploration of how artificial intelligence can revolutionize conventional banking frameworks. By integrating diverse decision-making criteria into a cohesive system, the study highlights transformative potential. A vivid illustration is provided through a case study at a Bank, where the implementation of an AI-driven decision engine not only expedited credit card approvals but also catalyzed measurable gains in operational efficiency and revenue streams. This juxtaposition of granular detail with broad strategic insights underscores the profound implications of such technological integration.The research methodology employs the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to evaluate credit card applications based on six key criteria: income level, credit score, employment stability, existing debt, recent credit inquiries, and age. The study analyzes a dataset of five alternatives using weighted normalization and ideal solution comparison techniques to determine optimal credit approval decisions.Results demonstrate that the AI-driven system achieved a 20-40% improvement in operational efficiency and a 60% reduction in decision-making time. The TOPSIS analysis revealed clear differentiation among applicants, with the highest-performing candidate achieving a close coefficient value of 0.85835, significantly outperforming other alternatives. The implementation also led to a 98% reduction in security incidents and generated an additional $3 million in annual revenue.The results underscore that the integration of an AI-driven framework with the TOPSIS method delivers an intricate yet robust mechanism for evaluating credit card applications. Notably, the leading applicant secured a closeness coefficient of 0.85835, a figure that vividly highlights the system’s precision in distinguishing applicants with stronger and weaker financial credentials. This accomplishment not only underscores the viability of AI-powered decision-making engines but also reveals their potential to revolutionize conventional banking processes. By weaving advanced algorithms into legacy systems, financial institutions can not only elevate the accuracy of credit assessments but also streamline operational workflows and refine the overall customer experience, thus creating a multifaceted value proposition.

Data Engineering At Scale: Streaming Analytics With Cloud And Apache Spark

2025-01-22T12:30:00+00:00

This Study in modern healthcare systems, efficient data engineering is critical for processing vast amounts of real-time data generated by hospitals and medical devices. This article explores the transformative potential of integrating cloud-based technologies, specifically AWS and Databricks, with Apache Spark for real-time streaming analytics. Leveraging Databricks’ Lakehouse architecture and Unity Catalog enhances data governance and security through Identity and Access Management (IAM) and encryption mechanisms.

This framework addresses challenges such as fragmented data pipelines, compliance concerns, and the latency of traditional data processing systems. Apache Spark's distributed computing and AWS's robust infrastructure provide scalable, high-performance analytics pipelines. Unity Catalog ensures secure, unified data access, meeting stringent healthcare compliance requirements like HIPAA. For example, patient admission and vital data streaming through Spark’s structured streaming enabled a 40% reduction in hospital response times. With increasing adoption of AI in healthcare, the proposed architecture bridges the gap between raw data ingestion and real-time actionable insights, enhancing patient outcomes.

The methodology and results underscore the framework's scalability and its potential to revolutionize healthcare data engineering.

AI and Diversity, Equity, and Inclusion (DEI): Examining the Potential for AI to Mitigate Bias and Promote Inclusive Communication

2025-01-29T05:13:58+00:00

Artificial intelligence (AI) has the potential to significantly advance Diversity, Equity, and Inclusion (DEI) in the workplace by addressing biases in communication and decision-making processes. This paper examines how AI can be employed to identify, mitigate, and even eliminate biases that often arise unconsciously in human interactions. AI systems, when designed and implemented responsibly, can analyze language, behavior, and decision patterns to promote more equitable outcomes. AI-powered tools, such as real-time feedback mechanisms, translation services, and accessibility features, can enhance inclusive communication by accommodating diverse needs and perspectives. However, the effectiveness of AI in promoting DEI is not without challenges. Ethical concerns, such as the risk of AI systems perpetuating or amplifying existing biases, necessitate rigorous oversight, careful design, and continuous monitoring. The paper highlights the importance of addressing these ethical considerations to prevent unintended consequences and ensure that AI contributes positively to DEI efforts. By critically exploring the intersection of AI and DEI, this paper emphasizes the potential of AI to create more inclusive and equitable workplaces, while also calling for a thoughtful and responsible approach to its deployment.