The average number of deposit accounts a bank employee handles has increased to 2,495 as financial institutions rely heavily on technology to improve operational efficiencies, a report by the regulator shows.
Technological advances like machine learning (ML) and artificial intelligence (AI) are being deployed by banks to monitor staff performance and understand customers.
Data from the Central Bank of Kenya (CBK) Bank Supervision Annual Report shows an increase of 30 million deposit account holders relative to 1,800 new bank employees in 2023.
While 2023 was marred by economic turmoil punctuated with tax increases for both businesses and formal employees, the report shows that during this period, the number of deposit account holders rose to 94,643,325.
CBK credits this to mobilisation by banks.
This is an increase of 29,089,237. At the same time, the number of bank employees increased marginally to 37,933 compared to 36,107 in 2022.
The additional 29 million deposit account holders represent the highest increase ever recorded by the regulator according to the provided data.
The report notes that in 2023, a bank employee was, on average, handling 2,495 deposit accounts, whereas in 2022, an employee was handling 1,816 deposit accounts.
“The increase in efficiency is explained by the increase in the number of deposit account holders compared to the increase in the number of staff,” explains CBK in the report.
In 2022, the number of deposit account holders had dropped by 761,611 to 65,554,088, while the number of staff increased by 3,667 to 36,107.
The number of staff in 2023 increased by 1,826 amid an additional 29 million deposit account holders.
CBK notes in the report that the increase in deposit account holders in 2023 was due to the mobilization of more deposit accounts by banks.
“Due to economic recovery and the reopening of businesses in 2023, after dealing with the pandemic, the number of deposit accounts and staff increased due to increased recruitment in 2023,” the report says.
Such a significant increase in deposit accounts, while beneficial for banking businesses, also raises risks concerning compliance with regulations provided by CBK and meeting customers’ needs.
It is here that technology steps in, as the report states that the banking sector has adopted artificial intelligence (AI) and machine learning (ML) in their operations through Big Data.
AI and ML are being used to improve operational efficiencies, predict customer behavior, and manage risks more effectively.
Customer service is one of the areas the CBK report lists where banks have implemented this technology.
The report documents that financial institutions have deployed solutions that monitor customer sentiments on various digital platforms, including social media.
“The feedback is summarized into major thematic concerns for deeper analysis to understand and address customer needs and preferences,” the CBK report says.
It adds: “Institutions have also introduced chatbots to streamline digital banking and improve customer onboarding and transactions on internet and mobile banking platforms.”
Apart from customer service, banks are also using AI and ML in anti-money laundering areas, product segmentation and personalisation, cybersecurity, and fraud risk management.
“Banks have deployed AI solutions to monitor electronic communications by staff in the trading room to detect outliers and irregularities,” the report says on how technology is being deployed in fraud risk management.
It adds that banks also subscribe to fraud risk management solutions for payment cards provided by international card schemes and for account transactions.
“These solutions provide a risk score that predicts the potential of fraud in an authorization attempt,” the report says.
ML is being deployed to tackle cybersecurity as these solutions are being used to detect potential insider and external threats.
While insider threats are monitored by collecting and analyzing data on network use patterns, work hours, and approved devices on the network, external threats are checked by identifying outliers or unusual activities in customers’ transaction patterns.
“Moreover, cybersecurity tools such as Security Incident and Event Management (SIEM) are ML-enabled to automatically detect and respond to threats by quarantining suspicious processes, applications, and devices on the network, thus limiting the attack surface in case of a security incident,” the report says.
Banks have also leveraged ML’s Cluster and Ensemble applications to group similar data points from various clusters, combining outputs for optimal grouping for product segmentation and personalization.
“By grouping their customers based on similarities, institutions offer tailored products and services to new and existing customers,” the report says.
These changes are also taking effect in embedded finance, a concept at the intersection of financial services and technology, where financial services are integrated with (or embedded in) non-financial platforms such as ride-hailing applications or e-commerce.
The report says AI/ML technologies are transforming various aspects of finance, from customer service and risk assessment to fraud detection.
