STATSML 604: Car Loan Propensity Prediction using Logistic Regression

Overview

Predicting whether a customer is likely to take a car loan can significantly improve a bank’s ability to design targeted campaigns, manage credit risk, and optimize resource allocation. In this tutorial, we build a Logistic Regression model to classify a customer's car loan propensity using their profile and transaction behavior.

Objective

Build and deploy a classification model that predicts if a customer is likely to opt for a car loan, based on:

• Demographic and behavioral data

• Financial account balances

• Derived income and loan features

Sample Dataset Structure

Fields & Description

Field Name	Data Type	Description
customer_id	STRING	Unique identifier
age	INT	Customer age
gender	STRING	Male / Female / Other
marital_status	STRING	Married / Single / Divorced
employment_status	STRING	Employed / Self-employed / Retired etc.
annual_income	DECIMAL	Yearly income
credit_score	INT	Credit bureau score
checking_balance	DECIMAL	Balance in checking account
savings_balance	DECIMAL	Balance in savings account
monthly_debit_volume	DECIMAL	Monthly average spending
monthly_credit_volume	DECIMAL	Monthly average income
loan_history	ARRAY<STRUCT>	Past loans (type, amount, status)
existing_auto_loan	BOOLEAN	Existing car loan
owns_vehicle	BOOLEAN	Whether customer owns a car
propensity_car_loan	FLOAT	Target: Likelihood (0-1) of taking a loan

Derived Features

These features are engineered to improve model performance:

Feature	Formula / Logic
savings_to_income_ratio	savings_balance / annual_income
debt_to_income_ratio	(monthly_debit_volume * 12) / annual_income
avg_monthly_net_income	monthly_credit_volume - monthly_debit_volume
loan_count	COUNT(loan_history)
previous_auto_loans	COUNT WHERE loan_type = 'Auto'
good_credit_flag	credit_score >= 700
high_cash_reserve_flag	checking_balance + savings_balance > 10000

Feature Weight Intuition

Feature	Weight	Explanation
Not having an auto loan	0.30	More likely to consider buying
Doesn’t own a vehicle	0.20	May need a car, hence loan
Good credit score	0.20	More eligible for credit
Income > $60K	0.10	Likely to get approved
Checking balance > $2K	0.10	Has funds for down payment
Net income > $2K	0.10	Better repayment capacity

Model Definition

DROP MODEL IF EXISTS customer_propensity_using_LogisticRegression;

CREATE MODEL customer_propensity_using_LogisticRegression
TRANSFORM (
  vector_assembler(array(
     age,
     annual_income,
     credit_score,
     checking_balance,
     savings_balance,
     monthly_debit_volume,
     monthly_credit_volume,
     existing_auto_loan,
     owns_vehicle,
     savings_to_income_ratio,
     debt_to_income_ratio,
     avg_monthly_net_income,
     good_credit_flag,
     high_cash_reserve_flag
  )) features
)
OPTIONS (
  MODEL_TYPE = 'logistic_reg',
  LABEL = 'propensity'
)
AS
SELECT
  *,
  CASE WHEN propensity_car_loan > 0.5 THEN 1 ELSE 0 END AS propensity
FROM vw_customer_bank_profile_data_train
ORDER BY RANDOM()
LIMIT 50000;

Model Evaluation

SELECT * FROM model_evaluate(
  customer_propensity_using_LogisticRegression, 
  1, 
  (
    SELECT
      *,
      CASE WHEN propensity_car_loan > 0.5 THEN 1 ELSE 0 END AS propensity
    FROM vw_customer_bank_profile_data_train
  )
);

Results

Metric	Value
AUC ROC	0.9362
Accuracy	0.9361
Precision	0.9367
Recall	0.9372

Predict on New Customers

SELECT * FROM model_predict(
  customer_propensity_using_LogisticRegression, 
  1,
  (
    SELECT
      *,
      CASE WHEN propensity_car_loan > 0.5 THEN 1 ELSE 0 END AS propensity
    FROM vw_customer_bank_profile_data_predict
  )
);

Use Cases

• Targeted Campaigns: Focus offers on high-propensity segments

• Loan Eligibility Filtering: Pre-qualify candidates automatically

• Customer Risk Profiling: Understand financial behavior deepl