This research introduces a novel restructuring of the chain‑ladder method to bridge the gap between traditional aggregate insurance reserving and modern individual claims modeling. By redefining the algorithm to project current observations directly to ultimate claim outcomes, the authors create a simplified framework for applying machine learning without needing to forecast complex intermediate variables. The paper demonstrates this approach through neural networks, proving that granular data like claim status and reporting delays can significantly improve accuracy. A key technical contribution is the backward extrapolation technique, which ensures that these advanced models remain mathematically consistent with established industry standards. Through real‑world data applications, the study confirms that this methodology effectively handles reported but not settled claims while mitigating potential estimation biases. Ultimately, the authors provide a practical roadmap for insurers to transition from legacy triangle‑based methods to sophisticated, claim‑level predictive analytics.