How AI is redefining Kenya's banking sector

Absa Bank Kenya Consumer Network Director Peter Mutua and Nairobi West Regional Manager Phyllis Muthusi flag off the activation truck at the Bank's Imaara Mall Branch. In today's digitally driven and data-led business landscape, emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising how banks interact with customers, boosting efficiency and elevating the customer experience. Studies have shown that AI applications will be indispensable especially in the banking sector, with Deloitte predicting that banks can boost their front-office productivity by up to 27-35 per cent by leveraging generative AI. Digital transformation is becoming a priority for business, with myriad transformative benefits including increased efficiency and service speed, as well as improved risk management. It has the potential to enhance transactional transparency and compliance, reduce operational cost, help with better customer experience and offer new and innovative products development opportunity based on valuable data insights. Bankers are embracing these innovative technologies, rapidly integrating AI into banking processes to transform various aspects of the industry, from customer service to fraud detection. For instance, leading banks like Absa Bank has been at the forefront of this transformation, utilising AI-powered chatbots to handle customer inquiries and implementing advanced fraud detection systems to safeguard their customers' assets. According to the Central Bank of Kenya (CBK) bank supervision annual report 2023, the Kenya Banking Sector has adopted and implemented AI and (ML) to improve operational efficiencies, predict customer behaviour, and manage risks more effectively. This has not only proven efficient to banks and customers, but it has also paid off in contributing to the banks' returns. The McKinsey Global Institute (MGI) estimates that across the global banking sector, gen AI could add between $200 billion and $340 billion in value annually, or 2.8 to 4.7 per cent of total industry revenues, largely through increased productivity. One of the key areas where banks have extensively deployed AI and ML is enhancement of customer experience through digital propositions. Customer experience is the main driver of any business, and these technologies have been key in enhancing it by offering bespoke services at a faster turnaround time. Banks are using these technologies to provide personalised recommendations, answer questions, and perform transactions, thereby reducing the need for human intervention and significantly improving response times. AI-powered chatbots and virtual assistants are also becoming increasingly sophisticated, capable of handling a wide range of customer inquiries round the clock. AI and ML algorithms are also making strides in fraud detection and prevention. With AI capabilities, banks have acquired a technical way to assess vast data, map out and plan to anticipate fraud by analysing vast amounts of transaction data in real-time thereby identifying patterns and anomalies that may indicate fraudulent activity. This helps banks to quickly detect and respond to potential threats, thereby reducing financial losses and enhancing security for customers. In risk management, AI and ML offer powerful tools for analysing creditworthiness and managing risk portfolios. These technologies can provide more accurate assessments of credit risk by processing and analysing large datasets enabling banks to make better-informed lending decisions. This not only minimises the risk of default but also allows for development of more tailored financial products and services. AI has helped banks make faster lending decisions, more accurate analysis of credit quality enhancing returns and minimising losses for financial institutions. Jeremiah Mutune, the Pan-African Head of Trade & Working Capital Digital Propositions at Absa. [Courtesy] The use of AI and ML is growing rapidly as banks reassess their systems and integrate the technology to improve operational efficiency. Automating routine tasks such as data entry, compliance checks, and report generation can lead to substantial time and cost savings. Furthermore, AI-powered systems can streamline processes such as loan approval and customer onboarding, reducing the time and effort required to complete these activities. These technologies will also be integral in defining banking for the future which will be increasingly personalised to customer tastes anticipating their needs and providing tailor made solutions for them. The ability of AI and ML to enable banks to offer highly personalised services to their customers has made them indispensable. By analysing customer data, these technologies can identify individual preferences and behaviours, allowing banks to tailor their products and services accordingly. This personalisation can enhance customer loyalty and satisfaction, as well as open up new revenue streams for banks. Despite the numerous benefits, the integration of AI and ML in banking is not without challenges. Issues such as data privacy, regulatory compliance, and the need for a skilled workforce to manage and maintain these technologies must be addressed. By enhancing customer experience, improving fraud detection, optimising risk management, and increasing operational efficiency, these technologies can drive significant advancements in banking services. -The writer is Pan-African head of trade and working capital digital propositions at Absa

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