Many online tools use simplified assumptions to generate a range. That can be useful if you’re just trying to understand what categories of losses are sometimes included. However, Ferndale cases often turn on details that automated models don’t “see,” such as:
- How the incident happened in real traffic conditions (visibility, lane control, timing, speeding evidence, distraction, weather, and signals)
- Whether fault is likely to be disputed by another driver, a commercial party, or a property operator
- What documentation exists early (police reports, witness statements, dashcam/cell video, medical records)
- Whether an insurer is likely to argue causation (that the death was caused by something other than the incident)
When those facts aren’t entered accurately—or can’t be verified—AI projections can drift far from what a real claim is worth.


