What is the frequency at which the prebuilt AI/ML models train (for example, Collection Risk and Date Predictions)?

What is the frequency at which the prebuilt AI/ML models train (for example, Collection Risk and Date Predictions)?

• A. Weekly.
• B. Daily.
• C. Monthly.
• D. Yearly.

Answer: (C)

Retraining AI/ML models is about balancing keeping insights up-to-date with keeping resources and stability reasonable. Prebuilt models like Collection Risk and Date Predictions rely on patterns in data that drift over time, so they need refreshes that reflect new information. A monthly retraining cadence typically captures these new signals and changes without the overhead and potential instability of training every day or week. It also avoids the model becoming stale if data doesn’t change dramatically on a daily basis. Training yearly would mean missing important shifts in risk and date dynamics. So monthly provides a practical, balanced frequency that keeps the models current while managing compute cost and rollout impact.

Retraining AI/ML models is about balancing keeping insights up-to-date with keeping resources and stability reasonable. Prebuilt models like Collection Risk and Date Predictions rely on patterns in data that drift over time, so they need refreshes that reflect new information. A monthly retraining cadence typically captures these new signals and changes without the overhead and potential instability of training every day or week. It also avoids the model becoming stale if data doesn’t change dramatically on a daily basis. Training yearly would mean missing important shifts in risk and date dynamics. So monthly provides a practical, balanced frequency that keeps the models current while managing compute cost and rollout impact.