We study the economic effects of artificial intelligence deployment in emerging financial markets, focusing on AI-driven mobile credit scoring in underdeveloped economies. In regions with limited formal banking infrastructure, algorithmic lending platforms use alternative data, such as mobile transaction histories and repayment behavior, to expand access to credit for individuals without traditional financial records. This innovation has the potential to alter market participation, improve risk assessment, and reshape capital allocation in developing economies. Using data from sources like the World Bank Global Findex, IMF Financial Access Survey, and mobile lending adoption records across multiple countries, we estimate how AI-based credit expansion affects financial participation, small business formation, household liquidity, and overall credit market integration across demographic groups and income levels. We implement a difference-in-differences framework comparing regions before and after AI adoption while controlling for macroeconomic conditions, demographic characteristics, and institutional factors. To model borrower choice between traditional banks and mobile lenders, we estimate a BLP demand model that allows for heterogeneous consumer preferences and substitution patterns across financial products. The structural model is then used to simulate changes in market structure, credit allocation, and consumer surplus under alternative adoption and regulatory scenarios, accounting for variation in regulatory quality and digital infrastructure.