Insurance Premium Forecasting

Predict insurance premium prices using panel data with 50 policyholders tracked over 100 time periods.

Dataset Exploration

The Insurance dataset includes policyholder information and claim history over time. Features include claims frequency, claim amounts, fraud detection flags, and risk scores.

Panel data structure: 50 policyholders tracked over 100 time periods (5,000 total rows, 14 columns).

Fit Data (first 5 rows)

Time	Individual	Claims Filed	Log Claim Amt	Fraud Flag	Claims Freq.	Claims Size	Regional Risk	Claims×Fraud	Auto	Policy Risk	Premium Paid	Premium Price
1	1	1	7.88	0	2	2	2	0	1	2	809	1281.76
2	1	1	7.07	0	3	1	2	0	1	2	809	1310.97
3	1	2	7.16	0	2	1	3	0	1	2	809	1283.5
4	1	1	8.06	1	2	2	1	1	1	2	809	1295.01
5	1	2	7.49	0	3	1	2	0	1	2	809	1293.94

Forecast Data (first 3 rows)

The forecast file covers future periods without the Premium Price target column.

Time	Individual	Claims Filed	Log Claim Amt	Fraud Flag	Claims Freq.	Claims Size	Regional Risk	Claims×Fraud	Policy Risk	Premium Paid
101	1	1	7.27	0	3	2	1	0	1	809
102	1	2	6.52	0	3	3	1	0	1	809
103	1	2	7.54	1	2	1	3	2	1	809

Download sample datasets from GitHub

Code Walkthrough

Step 1: Initialize

Python

import pandas as pd
from datfid import DATFIDClient

client = DATFIDClient(token="your_DATFID_token")

Step 2: Fit the Model

Python

url_fit = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Insurance.xlsx"
df = pd.read_excel(url_fit)

result = client.fit_model(
    df=df,
    id_col="Individual",
    time_col="Time",
    y="Premium Price",
    current_features="all",
    filter_by_significance=True
)

Step 3: Forecast

Python

url_forecast = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Insurance_forecast.xlsx"
df_forecast = pd.read_excel(url_forecast)

forecast = client.forecast_model(df_forecast=df_forecast)

Analysis Results (Model Fit)

Formula

Premium Price ~ α1*Intercept + α2*Policy Type: Home + α3*Premium Paid + α4*Policyholder Risk + α5*Policy Type: Auto + β1*Claims Filed + β2*Log Claim Amount + β3*Fraud Detection Flag + β4*Policyholder Claims Frequency + β5*Policyholder Claims Size + β6*Regional Risk + β7*Claims Filed × Fraud Detection Flag

Alpha Estimates (Time-Invariant)

Variable	Estimate	T-stat	Interpretation
Intercept	1,509.62	690.12	Baseline premium price
Premium Paid	+0.113	78.26	Past premium paid strongly predicts current pricing
Policyholder Risk	+18.39	41.24	Each risk category adds ~$18 to premium
Policy Type: Home	-22.00	27.32	Home policies are ~$22 cheaper than baseline
Policy Type: Auto	-23.35	25.36	Auto policies are ~$23 cheaper than baseline

Beta Estimates (Time-Varying)

Variable	Estimate	T-stat	Interpretation
Log Claim Amount	+24.58	49.78	Higher claim amounts increase premium by ~$25 per log unit
Claims Filed	+20.08	40.84	Each additional claim increases premium by ~$20
Claims Frequency	+10.54	30.22	Higher claim frequency adds ~$11 per unit
Regional Risk	+5.13	14.65	Higher-risk regions add ~$5 to premium
Claims Size	+4.81	13.77	Larger claim size adds ~$5 to premium
Fraud Detection Flag	+13.13	8.86	Fraud flag adds ~$13 to premium
Claims × Fraud	+1.02	1.45	Not statistically significant (p=0.15)

Model Performance

0.746

R² Overall

0.857

R² Between

0.617

R² Within

19.14

MAE

574.39

MSE

Try it yourself: Select "Insurance" in the Free Playground.