The digitalization of financial services has intensified volatility and operational complexity in FinTech (Financial Technologies) ecosystems, requiring adaptive liquidity management strategies. This study proposes a real-time stochastic inventory framework for FinTech liquidity optimization, combining (s,S) policy structures with advanced risk metrics, including Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). Using transaction and volatility data from the Central Bank of Turkey and the Banking Regulation and Supervision Agency, Monte Carlo simulations model stochastic liquidity flows under market uncertainty. The optimized policy recommends a reorder point of 775,000 TRY and a target liquidity level of 1,450,000 TRY, maintaining a buffer proportional to daily volatility. Results show that dynamic adjustment of liquidity thresholds balances operational efficiency and risk mitigation, outperforming static EOQ (Economic Order Quantity) and fixed-order approaches. Stress tests confirm adaptability under increased volatility, longer lead times, and higher transaction costs. This framework provides a robust, tail-risk-aware approach for real-time FinTech liquidity management, bridging operational research and financial risk practices.