Implementation of Artificial Intelligence for Financial Process Innovation of Commercial Banks
Внедрение искусственного интеллекта для инновационного развития финансовых процессов коммерческих банков
2023-09-12
SCID: 54.1/7fncszy8
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artificial intelligencecommercial bankscredit risk managementfinancial forecastingpredictive modeling
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Abstract (AI)
Purpose: The purpose of this study is to investigate and illuminate the transformative potential of artificial intelligence (AI) in the context of enhancing financial services within Jordanian commercial banks, with a specific focus on credit risk management. By researching into the applications of AI within this sector, the study aims to provide insights into how AI technologies can reshape traditional banking practices and improve the overall efficiency and effectiveness of credit risk management processes. Theoretical framework: The study is grounded in the theoretical framework of technological innovation and strategic management. It draws from the literature on AI adoption in the financial industry and its implications for operational efficiency, risk assessment, and customer experience. Additionally, the study incorporates concepts related to data analysis, machine learning, and predictive modeling as key components of AI-driven transformation within the banking sector. Method/design/approach: To achieve the research objectives, a systematic research design is employed, utilizing survey methods as the primary data collection tool. A sample of 143 employees from major banks located in Amman, Jordan, is selected for participation. The survey encompasses questions designed to gather information about the current state of AI integration, challenges faced, and potential benefits within credit risk management and other financial services. This quantitative approach allows for the collection of structured data that can be statistically analyzed to uncover trends and patterns. Results and conclusion: The findings of the study highlight the substantial potential of AI integration in revolutionizing the operations of Jordanian commercial banks. AI technologies enable more accurate credit assessment, precise analysis of market risks, enhanced financial forecasting capabilities, robust validation of risk models, and advanced evaluation of creditworthiness. Furthermore, the study reveals that AI offers the opportunity for personalized customer service solutions, thereby improving the user experience and guiding customers toward suitable financial services. In conclusion, the study underscores the positive impact of leveraging AI-driven innovation on financial performance and profitability within Jordan's banking sector. Research implications: This study has implications for academia and the banking industry, contributing to knowledge about AI's strategic use in financial innovation and its application in Jordanian commercial banks for credit risk management and customer service enhancement. Originality/value: This research stands out by focusing on Jordanian banks' AI adoption, providing distinct insights into challenges and opportunities in a specific context. Its value lies in guiding banks to effectively integrate AI, enhancing credit risk management and financial services for improved performance and innovation.
Key Findings
1
A survey of 143 employees from major Amman banks examines current AI integration, implementation challenges, and potential benefits.
2
AI integration is reported to improve credit assessment accuracy, market-risk analysis, financial forecasting, risk-model validation, and creditworthiness evaluation.
3
The research frames AI-driven banking transformation around data analysis, machine learning, predictive modeling, technological innovation, and strategic management.
4
The study investigates AI’s transformative potential for financial services and credit risk management in Jordanian commercial banks.
Research Object
Artificial intelligence integration in Jordanian commercial banks, particularly their credit risk management processes
Research Subject
The effects of AI integration on the accuracy, efficiency, and capabilities of credit risk management, including credit assessment, market-risk analysis, financial forecasting, risk-model validation, and creditworthiness evaluation
Publication Details
Publication Date
2023-09-12
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