Vijayakumar Bharathi Sethumadhavan

The global financial services industry stands at an inflection point where serving the world’s 1.4 billion unbanked adults has transformed from a development challenge into a commercial imperative. This case study analyzes 13 financial institutions that have achieved profitable inclusion, chosen for their strategic diversity and proven ability to generate revenue while serving populations traditionally deemed unviable. Through a systematic analysis of their strategic frameworks, from Capitec’s biometric revolution in South Africa to HesabPay’s blockchain innovation in Afghanistan, our case proposes a five-pillar architecture enabling sustainable profitability at scale. We define scale as the capacity to serve a large customer base, handle a high transaction volume, and achieve extensive geographic reach. These institutions demonstrate that by removing documentation barriers, building mobile-first distribution, integrating with local ecosystems, offering simplified products, and adopting a community-centric approach, they create competitive advantages that redefine banking success. The evidence strongly suggests that financial inclusion represents one of the sector’s largest untapped growth opportunities.

Understanding the complexities of buying, selling, and holding stocks is crucial for institutional and individual investors to make informed decisions. Despite their significance, many investors face challenges in this area. Accurate stock price forecasting is a vital tool that aids investors in making profitable decisions. This study evaluates stock trends and patterns with an in-depth analysis of the Bombay Stock Exchange (BSE) stock data. We utilized various techniques, including time-series analysis, machine learning, and deep-learning models. This investigation spanned two distinct datasets: one with and one without COVID-19 stock price data. By comparing the outcomes, we seek to identify the most effective model for stock price prediction. Our findings indicate that each model has its strengths and limitations. Time series models accurately forecast short-term stock prices, whereas machine learning models demonstrate superior generalization capabilities. Deep learning models, however, stand out for their ability to predict long-term stock prices more accurately. Understanding each model's performance nuances is crucial for institutional and individual investors and regulators to optimize their strategies and decision-making processes.

This case study examines the implementation of digital payment solutions in fragile states. It focuses on how technological innovation can address financial inclusion challenges in regions with limited banking infrastructure. Our case explores the transformation of HesabPay, a local financial technology platform, into a comprehensive digital payment system that facilitates humanitarian aid distribution while promoting financial inclusion. Our case demonstrates how digital payment solutions can effectively support local economic activities in challenging environments. Additionally, the case explains the role of blockchain technology in ensuring transparency and security alongside the platform’s impact on vulnerable populations in conflict-affected areas. Our analysis reveals how multi-modal access approaches can overcome technological constraints and enhance financial service delivery. Our case contributes to understanding the potential of digital payment platforms in fragile states. We offer insights for organizations seeking similar solutions while maintaining operational transparency and supporting humanitarian objectives.