How Data and Automation Transformed Small Business Lending Amid COVID-19 (Published)
The global COVID-19 pandemic triggered unprecedented economic disruptions, severely impacting micro, small, and medium enterprises (MSMEs) across developing economies. In Nigeria, small businesses, already grappling with limited access to credit, encountered additional constraints as traditional loan disbursement systems became overwhelmed by the volume and urgency of pandemic-related relief applications. Manual lending processes, characterized by bureaucratic delays and in-person verifications, proved ill-equipped to handle the crisis, prompting an accelerated shift toward data-driven digital solutions. In response, financial institutions—ranging from commercial banks to fintech startups—deployed automation technologies to streamline loan origination, eligibility assessments, fraud detection, compliance reporting, and customer engagement. These technologies not only enhanced the speed and accuracy of credit delivery but also contributed to greater transparency and accountability in the disbursement of public funds.This paper investigates the transformative role of automation in Nigeria’s small business lending landscape during COVID-19. Using a mixed-method research design, we surveyed 500 key stakeholders, including small business owners, financial service providers, fintech innovators, and regulatory officials. The findings reveal that automation significantly improved loan approval timelines, increased user satisfaction, and enhanced fraud prevention capabilities. Furthermore, the study underscores automation’s long-term potential in deepening financial inclusion, improving regulatory oversight, and driving operational efficiency within Nigeria’s financial sector. By offering empirical insights, this research contributes to the evolving discourse on digital transformation in emerging markets and provides a framework for future innovation in crisis-resilient financial systems.
Keywords: Automation, COVID-19, Digital Transformation, Financial Services, Nigeria, small business lending
Analysis and forecasting the outbreak of Covid-19 in Ethiopia using machine learning (Published)
Coronavirus outbreaks affect human beings as a whole and can be a cause of serious illness and death. Machine learning (ML) models are the most significant function in disease prediction, such as the Covid-19 pandemic, in high-performance forecasting and used to help decision-makers understand future situations. ML algorithms have been used for a long time in many application areas that include recognition and prioritization for certain treatments. Too many ML furcating models are used to deal with problems. In this study, predict a pandemic outbreak using the ML forecasting models. The models are designed to predict Covid-19, depending on the number of confirmed cases, recovered cases and death cases, based on the available dataset. Support Vector Machine (SVM) and Polynomial Regression (PR) models were used for this study to predict Covid-19 ‘s aggressive risk. All three cases, such as confirmed, recovered and death, models predict death in Ethiopia over the next 30 days. The experimental result showed that SVM is doing better than PR to predict the Covid-19 pandemic. According to this report, the pandemic in Ethiopia increased by half between the mid of July 2020. Then Ethiopia will face a number of hospital shortages, and quarantine place.
Keywords: COVID-19, Forecasting, coronavirus, machine learning, polynomial regressing, support vector machine