Why more service providers are deploying AI in customer engagement

The deployment of AI and ML tools presents a growing opportunity to improve customer engagement and boost revenues for financial services providers. [iStockphoto] Financial service providers are increasingly turning to artificial intelligence (AI) and machine learning (ML) technologies to enhance customer engagement, in what could alter traditional dynamics of how companies interact with their customers. Analysis of annual reports from leading financial service providers and telecommunication firms in Kenya indicates broad adoption of AI and ML technologies in various tasks including, customer segmentation, scoring and analysis of customer feedback. This points to one of the pivotal shifts in how adoption of the technology is slowly and steadily, affecting how the country's corporate sector operates, and how customers obtain their goods and services. On one hand, the deployment of AI and ML tools presents a growing opportunity to improve customer engagement and boost revenues for financial services providers that hold vast data sets of customer habits and spending behaviours. On the other hand, concern has been raised that certain flaws in the technology, such as algorithmic biases, cultural and geographic under-representation of poorer countries and marginalised communities could be magnified and hinder effective customer service. In its latest annual report, Kenya's leading telecommunications service provider Safaricom PLC states that it will continue investing in AI alongside other technological tools to refine its understanding of customers alongside its segmentation model. The segmentation places customers under four broad categories; Youth, Strivers, Aspirers and Achievers based on their data analytics of their consumption patterns. In the last financial year the firm reported a 10.5 per cent and 6.8 per cent growth in customer and merchant numbers. At the same time,1.6 million revenue-generating customers on the M-Pesa Super App conducted more than 896 billion transactions that propelled the app to generate Sh6 billion in revenue over the same period of time. KCB Group, another prominent player in the financial services sector stated in its investor briefings that it has implemented decision-driven data analytics to enhance customer offerings, focusing on generating cross-sell and upsell opportunities through lead generation algorithms. The lender says it utilised tailored behavioral scoring models for new and existing customers and optimised loan limit management to expand its digital lending offerings. Large language models are further used by the lender to analyze customer feedback for improved customer experience. The increased usage of artificial intelligence in customer engagement and within the business value chain is not limited to financial services providers. The state utility company Kenya Power stated in its latest annual report that one of its strategic objectives is to improve business process effectiveness and enhance network efficiency. This will be done through implementing a digital transformation strategy that includes building infrastructure for business intelligence and data analytics capabilities. The health, insurance and aviation sectors are also some areas where business leaders are pushing ahead with the deployment of AI and ML tools and promising billions in future investment in the technologies. AI and ML technologies can detect patterns, subtle trends and associations much faster than human beings. When coupled with large data sets like historical transactional data, economic and markets reports, AI and ML can assist businesses roll out versatile and dynamic products and services that would otherwise have taken years or decades to develop. At the same time, the use of AI and ML to monitor and assess risk has been credited with identifying anomalies in large-scale transactions that can alert companies to fraud, thereby boosting their cybersecurity standing. Corporates spend a significant portion of their capital on research and development and the cost savings promised by deploying AI and ML technologies, coupled with the projections of boosted earnings make it a necessary plank in the strategy of most businesses. However, critics of the technology have pointed out that the gap between increased industry deployment and the development of consumer protection safeguards is vast and only growing. The private sector, which devotes significant budgetary allocations to understand and adopt these technologies still largely relies on testing frameworks used for traditional algorithms and standard enterprise system development. This means much of the threat posed by the new technologies and by malicious actors wielding it for harm remains unknown. Just this month AI development firm Anthropic revealed that it had detected the first coordinated hacking operation run largely autonomously using its AI language model. Biases in data sets that over-represent some populations, languages and geographies over others further present the challenge of worsening inequality among consumer segments, which could erode prospective gains of customer engagement and loyalty. As more companies turn to AI and ML technologies which are becoming increasingly widespread and affordable, there is a pressing need for all players across the board to make a concerted effort to understand not just the uses and possibilities of the technology to do good, but also work to identify the existing pitfalls that could undermine traditional efforts to reach their customers. - The writer is the managing partner, Maudhui House, a public affairs consultancy

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