IIARD International Institute of Academic Research and Development

Journal of Accounting and Financial Management (JAFM )

E-ISSN 2504-8856
P-ISSN 2695-2211
VOL. 10 NO. 10 2024
DOI: 10.56201/jafm.v10.no10.2024.pg47.63

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Robotics and Natural Language Processing and the Financial Performance of Deposit Money Banks (DMBs) in Nigeria

Joshua Adewale Adejuwon, & Felix Unuesiri

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Abstract

This study investigated the effect of Robotics and Natural Language Processing (NLP) on the Financial Performance of Deposit Money Banks in Nigeria. The study analysed secondary data of selected DMBs for the period 2015 -2023 (9 years). The data were sourced from the Annual Reports of the DMBs, the Central Bank of Nigeria (CBN) Statistical Bulletin and World Development Indicators to establish cause-effect relationships between the variables. The population of the study was the 27 DMBs in Nigeria as at 31st July, 2023, while the sample size was five (5) DMBs (Access Bank, Zenith Bank, UBA, First Bank and GT Bank)The sampling technique used was the non-probability convenience sampling method chosen based on the availability of the financial statements of the DMBs for the period under study. The study employed Error Correction Model (ECM) for time series regression to analyse equilibrium relationships in short run and long run behaviours. The hypothesis was used to test the impact of deployment of robotics on the financial performance of DMBs in Nigeria. The resultant coefficients were positive, but not significant both during pre-and post-Robotics adoption (coefficient = 0.49718, p<0.05 for pre and 0.32080, <0.05 for post Robotics adoption respectively. Therefore, we reject the null hypothesis and accept the alternate hypothesis to conclude that development and deployment of robotics had impacted positively, though non-significantly on the financial performance of DMBs in Nigeria. The hypothesis was also used to test the impact of deployment of NLP on the financial performance of DMBs in Nigeria. The resultant coefficients were positive, but also not significant both during pre-and post-Robotics adoption (coefficient = 0.33833, p<0.05 for pre and 0.30642, p<0.05 for post Robotics adoption respectively. Therefore, we reject the null hypothesis and accept the alternate hypothesis to conclude that development and deployment of NLP

keywords:

Robotics, Natural Language Processing (NLP), Financial Performance, Deposit

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