LTFM / Use cases
Today each of these questions is its own model, with its own features, tuning and upkeep. With LTFM it's the same model and a different table.
The richest longitudinal records and the strictest model review. If LTFM passes here, it passes anywhere.
A probability per claim or policy, the reasons behind it, and an abstain flag for cases it hasn't seen.
Late-arrival probabilities per shipment, still current when routes and suppliers change.
Forecasts and churn risk for new products and new stores too, where history is short.
Failure-risk probabilities and maintenance-cost forecasts, with uncertainty for each machine, to plan servicing and budgets.
Watch the platform demosExperiment one: 17 of 22 dated macro calls right, Brier score 0.146.
If you answer yes to all three, LTFM can likely help, whatever your industry.
Rows for things (customers, assets, orders) with a history of what happened to them.
Will it default, churn, arrive late, return, escalate? How much will sell?
A decision, a budget, a staffing plan or a customer depends on it, so you want to know how sure the prediction is.
Whether the reviewer is a bank's model-risk team or a COO, LTFM shows its work.
LTFM runs inside your environment and predicts on live cases. Nothing it says changes a decision yet.
LTFM and your current approach are compared on the same cases: accuracy, calibration, stability over time.
Your reviewers check the backtests, calibration and reasons, as for any model.
LTFM predicts in production. Where it abstains, your current process still decides.
Every pilot starts in shadow mode. LTFM predicts on your cases next to what you use today and changes no decisions. Tell us what you want to predict and we'll reply with a pilot plan.
KNOWW