In the age of Generative AI, what if the most complex feature engineering could be replaced by simple storytelling? This talk introduces a novel paradigm for predictive analytics that challenges traditional modeling workflows. We demonstrate a powerful technique: translating raw, structured data—like transaction logs or application usage data—into coherent, text-based narratives, or "stories." We then feed these stories directly into Large Language Models (LLMs) and prompt them for a predictive score. This approach leverages the deep contextual understanding of LLMs to perform tasks that typically require bespoke models and intricate feature engineering. We will explore real-world case studies, demonstrating how "stories" crafted from credit card transactions can accurately predict major life events. Similarly, we'll show how narratives of a user's app behavior can enable an LLM to detect subtle anomalies indicative of fraud, outperforming brittle, rule-based systems. Join us to discover how transforming your data into stories can unlock a new frontier of predictive power and operational efficiency.