Financial Institution Readiness and Adoption of Machine Learning Algorithm and Performance of Select Banks in Rivers State, Nigeria

International audience The study investigated how banks’ readiness and adoption of machine learning algorithms affect performance of banks in Rivers State. The study assessed the preparedness of financial institutions in incorporating MLA to enhance their performance. The concentration is on the banking sector and the aim is to uncover the level of readiness and the elements that may guide the adoption of ML in the financial business. This study focused on how organizational leadership clarity, employees' willingness to change, and access to technology affects efficiency, effectiveness, and productivity of banks in Rivers State. Data for the study were collected using structured questionnaires distributed to 133 respondents, with only 120 valid questionnaires. The Spearman rank correlation coefficient (SRPCC) method was employed in the study at the 5% threshold. The SRPCC test revealed that organizational leadership clarity, employees' attitude to change, and access to technology all had a significant impact on measures of improved service delivery (efficiency, effectiveness, and productivity) in banks in Rivers State. Finally, banks' readiness and adoption of machine learning algorithms have beneficial and consequential relationship with service delivery performance. According to the study, banks should implement strong organizational leadership clarity to ensure their readiness and willingness to use machine learning algorithms to improve service delivery outcomes. Employees' attitudes towards the acceptance and use of machine learning algorithms should be encouraged and improved through knowledge transfers, as it serves as a springboard for improved service delivery performance amongst banks.