How can the vanishing/exploding gradients problem lead to overfitting in business analytics models?

When gradients vanish, the model stops learning important patterns and only memorizes the training data, which makes it fit noise and overfit. When gradients explode, the weights jump wildly and the model oscillates, forcing it to chase every tiny fluctuation in the data instead of general trends. Both situations cause the model to capture idiosyncratic details that do not hold in new data, so predictions degrade on unseen business metrics. For example, a sales forecast network that keeps exploding gradients will keep adjusting to every daily price spike, learning the spike pattern rather than the underlying demand curve, and will fail when the spike pattern changes. Thus, gradient instability can turn a model into a memorizer rather than a generalizer, leading to overfitting.