Cognitive AI Systems in Financial Transactions: Enhancing Accuracy and Efficiency

Authors

Arunkumar Paramasivan

Abstract

Cognitive AI systems are transforming the landscape of financial transactions by enhancing accuracy, efficiency, and compliance. This article delves into the applications and advantages of cognitive AI in financial services, focusing on its impact on operational processes and decision-making. Through machine learning, natural language processing, and advanced data analytics, cognitive AI enables faster and more reliable insights, automates routine tasks, and identifies anomalies that could signal fraud. These systems play a crucial role in improving compliance with regulatory requirements, assisting financial institutions in adhering to evolving standards and minimizing legal risks. However, the adoption of cognitive AI is not without challenges. Issues such as data privacy, transparency in AI decision-making, and the potential for algorithmic bias raise ethical and operational concerns. Additionally, the complex nature of AI requires robust governance frameworks to ensure accountable, fair, and secure use in financial contexts. This paper also discusses potential frameworks and best practices to mitigate these risks, underscoring the importance of transparency and interpretability in AI-driven financial solutions. Through an in-depth analysis of cognitive AI’s role in financial transactions, this study aims to provide insights into the technologies’ transformative potential and the considerations necessary for responsible adoption in the industry.

Keywords

Cognitive AI, financial transactions, operational efficiency, decision-making, compliance, AI governance, ethical considerations, data privacy, transparency, algorithmic bias, financial services, machine learning, natural language processing, anomaly detection, regulatory compliance

Cognitive AI Systems in Financial Transactions: Enhancing Accuracy and Efficiency. Arunkumar Paramasivan. 2019. IJIRCT, Volume 5, Issue 5. Pages 1-10. https://www.ijirct.org/viewPaper.php?paperId=2412084