Improper Value at Risk (VaR) estimation contributed to the collapse of institutions like Lehman Brothers (2008) and Barings Bank (1995), where inadequate risk modeling failed to capture extreme losses. For individuals, similar missteps in underestimating investment risks or unexpected expenses can jeopardize financial stability. This study introduces a PySpark-driven risk modeling framework to safeguard personal finances, adapting institutional-grade techniques such as Monte Carlo simulations, stress testing, VaR, and economic capital estimation. Applied to retirement planning ($500,000–$1,000,000 initial savings, $40,000 annual expenses, 6% return, 15% volatility), the framework estimates a 52.3–94.5% probability of savings lasting 30 years. A refined scenario with a $1,000,000 portfolio, 4% inflation-adjusted withdrawals, and simulated market crashes reveals success rates dropping to 41.5% under stress, underscoring the need for robust risk management. By leveraging PySpark’s scalability, this framework bridges institutional and personal finance, offering a data-driven tool for financial resilience and fintech innovation.