What are examples of predictions produced by prebuilt ML models in FDI?

• A. Customer segmentation only.
• B. Sales forecasts.
• C. Collection risk and date predictions.
• D. Inventory counts only.

Answer: (C)

Predictions produced by prebuilt ML models in FDI are geared toward finance and cash flow outcomes, helping you anticipate how accounts will behave and when money is likely to come in. The strongest example is predicting collection risk and the expected payment date. This type of output directly supports accounts receivable and cash-flow planning: it tells you which invoices are at risk of not being paid and when you’re likely to receive payments, so you can prioritize actions and manage working capital.

Customer segmentation, while useful, is about grouping customers by similarities rather than forecasting a future payment event. Sales forecasts focus on revenue or demand at a broader level, not specifically on collection behavior. Inventory counts describe current stock or forecasts of stock levels, which isn’t the same as predicting payment-related outcomes.