A Chatbot for personalized financial advisory and investment planning in Kenya

Abstract

Financial illiteracy in Kenya remains a significant barrier to economic participation, limiting individuals' access to investment opportunities, insurance products, stock trading, retirement planning, and effective savings strategies. This research addressed the critical gap in affordable, transparent, and customized financial advisory services by developing an AI-powered chatbot tailored specifically for the Kenyan market. The study employed a mixed-methods approach combining exploratory, design-based, and experimental research methodologies to create and evaluate the chatbot system. Data was collected from 1,000 survey respondents representing diverse demographic and economic segments across Kenya, revealing key financial challenges and technology preferences. The chatbot architecture integrated natural language processing using BERT-based models, real-time market data from the Nairobi Securities Exchange and Central Bank of Kenya, and personalized recommendation algorithms. The system achieved 91.76% accuracy in intent recognition and 90.23% in parameter extraction across diverse financial domains. Notable innovations included bilingual support for English and Swahili with code-switching capability, mobile money integration, and culturally adapted financial advice. Performance evaluations demonstrated sub-50ms response times for 95% of queries, making the system accessible even on low-bandwidth connections. The chatbot successfully democratized access to professional-quality financial advisory services, particularly benefiting underserved populations who previously lacked such resources. This research contributes significantly to financial inclusion efforts in Kenya by demonstrating that AI-powered systems can effectively bridge the financial literacy gap when properly contextualized for local markets. The findings have important implications for developing similar solutions in other emerging economies facing comparable financial inclusion challenges.