Evaluating Artificial Intelligence in Banking A Complex Proportionality Assessment (COPRAS) Approach
Year: 2024
Type: article
Abstract: Artificial Intelligence (AI) is revolutionizing the banking industry by enhancing operational efficiency, personalizing customer experiences, and improving decision-making processes. AI technologies, such as machine learning, natural language processing, and predictive analytics, are being leveraged to streamline operations, detect fraudulent activities, and provide tailored financial advice. Banks are using AI-driven algorithms to analyze vast amounts of data in real-time, enabling them to offer personalized financial products and services, optimize risk management, and automate routine tasks. AI Chabot’s and virtual assistants are transforming customer service by providing instant support and addressing queries around the clock. Additionally, AI helps in credit scoring and loan approvals by assessing a broader range of variables, leading to more accurate and equitable decisions. Overall, AI is driving innovation in banking, offering enhanced security, efficiency, and customer satisfaction. Research Significance: The significance of Artificial Intelligence (AI) in banking lies in its transformative impact on efficiency, security, and customer engagement. AI technologies enable banks to process vast amounts of data swiftly, improving decision-making and operational efficiency. They enhance fraud detection and risk management through advanced predictive analytics and anomaly detection. AI-driven personalization offers tailored financial solutions, improving customer satisfaction and loyalty. Furthermore, AI automation reduces operational costs and minimizes human error. As the banking industry faces increasing competition and evolving regulatory demands, AI provides a crucial competitive edge, driving innovation and adapting to dynamic market conditions. Methodology: The Complex Proportionality Assessment (COPRAS) method is a multi-criteria decision-making method that ranks options according to several conflicting criteria It assesses the proportionality of each alternative concerning the desired outcomes. The method involves normalizing criteria values, calculating weighted scores for each alternative, and then determining the overall performance by comparing these scores. COPRAS provides a systematic approach to decision-making, allowing for a comprehensive evaluation of alternatives by considering their relative advantages and disadvantages across various criteria. This method is particularly useful in complex decision environments where multiple factors need to be balanced. Alternative: Chabot’s for Customer Service, Fraud Detection Systems, Automated Loan Approval, Personalized Financial Advising, Credit Scoring Models, Anti-Money Laundering (AML) Systems, Robotic Process Automation (RPA) for Back-office Tasks, AI-driven Investment Management. Evaluation Parameters: Cost Reduction, Efficiency Improvement, Customer Satisfaction, Accuracy, Scalability. Result: According to the results, Credit Scoring Models has the lowest score, while Personalized Financial Advising has the highest rank
DOI: 10.46632/daai/3/3/46
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4406234965