Multi-Modal AI Systems for Personalized Financial Planning (Published)
Multi-modal artificial intelligence (AI) systems are transforming personalized financial planning by integrating diverse data sources, including text, speech, images, and structured financial records. These systems utilize natural language processing for document analysis, computer vision for extracting financial data, machine learning for predictive analytics, and speech recognition for voice-based financial interactions. By analyzing transaction histories, market trends, and individual financial behaviors, AI-driven platforms generate tailored recommendations for budgeting, investment strategies, debt management, and risk assessment. The integration of real-time analytics enhances decision-making accuracy, enabling more efficient wealth management and fraud detection. However, ethical and privacy concerns arise due to extensive data collection and potential biases in AI-driven financial recommendations. Ensuring fairness, transparency, and regulatory compliance is critical to maintaining trust in automated financial advisory systems. Encryption, secure authentication, and explainability frameworks are essential for mitigating risks associated with data security and algorithmic bias. Future advancements, including blockchain integration for secure transactions, explainable AI for transparency, and quantum computing for complex financial modeling, are expected to further enhance financial planning. Addressing ethical considerations while optimizing AI-driven financial decision-making is crucial for ensuring the responsible implementation of AI in the financial sector.
Keywords: AI ethics, data security, multi-modal AI, personalized financial planning, predictive analytics
The Convergence of CCAI, Chatbots, and RCS Messaging: Redefining Business Communication in the AI Era (Published)
This article examines the transformative convergence of Conversational AI (CCAI), intelligent chatbots, and Rich Communication Services (RCS) in modern business communication. The integration of these technologies represents a paradigm shift from traditional messaging systems toward sophisticated, context-aware engagement platforms that deliver personalized customer experiences at scale. As organizations across industries increasingly recognize conversational interfaces as essential components of their digital strategy, this convergence addresses longstanding limitations in customer engagement by enabling consistent interactions across multiple channels. The article analyzes how advanced NLP capabilities, machine learning algorithms, and contextual awareness combine with RCS features like rich media sharing, interactive elements, and verified business profiles to create powerful communication ecosystems. Through case studies spanning retail, financial services, and healthcare sectors, the article demonstrates how this technological integration delivers measurable improvements in customer satisfaction, operational efficiency, and conversion rates. It further explores implementation challenges, ethical considerations, and future trends including multimodal communication, emotional intelligence, and decentralized architectures, providing a comprehensive framework for understanding how these technologies are collectively redefining business communication in the AI era.
Keywords: AI ethics, Conversational AI, Customer Engagement, Multimodal Communication, Rich Communication Services
Navigating the Ethical Dilemma of Generative AI in Higher Educational Institutions in Nigeria using the TOE Framework (Published)
Generative AI tools stand at the threshold of innovation and the erosion of the long-standing values of creativity, critical thinking, authorship, and research in higher education. This research crafted a novel framework from the technology, organization, and environment (TOE) framework to guide higher educational institutions in Nigeria to navigate the ethical dilemma of generative AI. A questionnaire was used to collect data from twelve higher institutions among lecturers, students, and researchers across the six (6) geopolitical zones of Nigeria. The structural equation modeling was used to analyze the data using the SPPS Amos version 23. The results revealed that factors such as perceived risks of generative AI, Curriculum support, institutional policy, and perceived generative AI trends positively impact the need for a generative AI ethical framework in higher educational institutions in Nigeria. Furthermore, the study contributes to the adoption of theory to navigate the ethical dilemma in the use of generative AI tools in higher educational institutions in Nigeria. It also provides some practical implications that suggest the importance of inculcating ethical discussions into the curriculum as part of institutional policy to create awareness and guidance on the use of generative AI.
Keywords: AI ethics, Higher Education, TOE framework., ethical framework, generative AI