Artificial Intelligence Applications and Financial Forecasting Accuracy in Banking Platforms: Evidence from Jordan

Alassuli, A, Eltweri, A ORCID: 0000-0002-5253-5678, Thuneibat, NS, Al-Hajaya, K and Ismail, SM (2026) Artificial Intelligence Applications and Financial Forecasting Accuracy in Banking Platforms: Evidence from Jordan. Administrative Sciences, 16 (3). ISSN 2076-3387

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Abstract

The continued digitalisation of banking systems has raised a demand for more reliable data-based decision-making, in particular when referring to financial forecasts as covered by e-banking applications. This research also investigates the usage of AI-based decision-making systems to facilitate forecasting effectiveness in Jordanian commercial banks. Field research was carried out and 390 employees, working at 14 commercial banks in Jordan, responded to an organised questionnaire. Although the minimum required sample size was 384 respondents, a total of 390 valid responses were collected and used in the final analysis, thereby exceeding the minimum sample requirement. This research concentrates on three dominant categories of AI applications, including expert systems (ES), machine learning (ML), and Robotic Process Automation (RPA), which together are analysed for their effect on forecasting results in the context of customer churn, debt repayment, as well as investment analysis. The results of the multiple regression analysis indicate that AI applications contribute to improvements in forecasting accuracy, with machine learning and RPA showing relatively stronger effects. Expert systems were found to support investment analysis and debt repayment forecasting; however, their influence on customer churn prediction was more limited. In general, the findings indicate that AI applications are not confined to routine automation but are increasingly used as decision-support tools that assist financial analysis and forecasting activities in banking systems.

Item Type:	Article
Uncontrolled Keywords:	artificial intelligence; digital decision-making; banking platforms; machine learning; robotic process automation; financial forecasting accuracy; digital finance; 35 Commerce, Management, Tourism and Services; 3507 Strategy, Management and Organisational Behaviour; Machine Learning and Artificial Intelligence; Data Science; Networking and Information Technology R&D (NITRD); 3507 Strategy, management and organisational behaviour; 4407 Policy and administration
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Liverpool Business School
Publisher:	MDPI
Date of acceptance:	26 February 2026
Date of first compliant Open Access:	1 September 2026
Date Deposited:	01 Sep 2026 12:52
Last Modified:	01 Sep 2026 12:52
DOI or ID number:	10.3390/admsci16030122
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29257

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