Recent research:
The body of work presented consolidates a multidisciplinary focus at the intersection of Artificial Intelligence, Finance, and Governance within the Ecuadorian context. Across applied AI for forecasting sales and bankruptcy risk, the author investigates how machine learning (notably Random Forest) and financial indicators can illuminate market segments, profitability, and credit risk for manufacturing firms and SMEs during and after the Covid-19 era. Several studies analyze financial performance, liquidity, and non-performing loans, linking them to macro shocks and policy responses, while also addressing corporate reputation and governance. Methodologically, the research employs quantitative modeling (Altman Z-Score, regression-like correlations, Spearman links) on official financial statements and public registries, and situates findings within practical implications for managers, policymakers, and investors. The overarching impact is to equip firms and regulators with data-driven insights to improve decision-making, resilience, and well-being of stakeholders in a developing economy, with a recurring emphasis on measurement, benchmarks, and the systemic effects of the pandemic on financial health and governance frameworks. This synthesis reveals a coherent theme: leveraging AI and econometric methods to interpret financial performance, risk, and governance to foster sustainable growth and happiness in workplaces and communities.
Key Topics:

• Artificial Intelligence, Machine Learning & Data Science
• Business, Economics, SMEs, Finance & Entrepreneurship
• Optimization, Operations Research & Decision Support
• Standards, Validation, Benchmarks & Measurement Best-Practice
• Governance, Law, Policy, Ethics & Regulation
• Social Sciences, Media, Political Communication & Disinformation
• Research Methods, Measurement, Bibliometrics & Academic Practice