Insurance

Data mapping

Entity

policy

Group

agency

Profile

generic; configure time windows with verticals.register

Reference implementation

from datetime import datetime, timedelta
from vertiq import plugins, rules
from vertiq.mapping import Mapping, apply, repair
from vertiq.tasks import predict_event, predict_value

# rows: the source export as a list of dicts, e.g. list(csv.DictReader(open("export.csv")))
mapping = Mapping.parse({"entity": "Police Nr", "time": "Loss Date", "type": None, "value": "Claim Amount", "group": "Agence", "attrs": ["Cover", "Channel"], "date_format": "%d/%m/%Y"})
m, notes = repair(mapping, rows)      # validates column names, value types and date format against the rows
ds, problems = apply(m, rows)         # rows that fail to load are reported, not imputed

@plugins.feature("insurance")
def claims_365(entity, before, cut):   # receives only events recorded before the cutoff
    return {"claims_365": sum(1 for e in before if e.at >= cut - timedelta(days=365))}

task = predict_event(ds, target="", window_days=90, pack="insurance")
print(task.status, task.report())     # "trained" only if the model outperformed the baseline on held-out entities
if task.status == "trained":
    print(task.score("POL482910"))  # probability with contributing factors

size = predict_value(ds, target="", window_days=365, pack="insurance")
print(size.status, size.report())

flags = rules.run(ds, [
    rules.repeat(14, key="Cover"),   # a second claim under the same cover within 14 days, for review
])
print(flags["summary"])

This code is executed by the test suite (tests/test_guides.py) against generated records with these column names.