Traditional Logistic Regression vs. Modern Machine Learning in Credit Scoring: A Practical Overview

Author(s): Can Demir

Originally published on Towards AI.

Credit scoring has been around for decades, helping lenders decide who’s likely to pay back a loan (and who isn’t). On one side, there’s the tried-and-true logistic regression scorecard approach — simple, transparent, and easy to explain. On the other side, we have machine learning models — powerful, flexible, and often more accurate but harder to interpret. Let’s take a guided tour of these two worlds, see how they differ, and then dive into a Python example showing how to implement both traditional and advanced models on synthetic credit data.

1. Logistic Regression Scorecards: “The Old Faithful”

For many years, credit risk professionals have relied on logistic regression to develop credit scorecards. If you’ve ever had a credit score, it was probably generated by a logistic regression model working quietly behind the scenes.

Why So Popular?

1. Simplicity and Clarity
   Logistic regression is essentially a single linear equation (in log-odds space). Each predictor — like income, loan amount, or credit history — has a single coefficient. If the coefficient is positive, that feature increases the probability of default; if negative, it decreases it.
2. Regulatory Friendliness
   In many countries, lenders must provide specific reasons for credit denials. With a logistic regression model, you can look at the top coefficients that drove the decision and point to them in plain English (e.g., “Your income-to-loan ratio was too high.”).
3. Manageable Feature Selection
   It’s common to start with a large pool of variables, then narrow it down to maybe 8–12 of the most predictive ones. This keeps the model stable and easier to monitor over time.

Limitations

• Linear Assumption: Logistic regression can miss complex interactions unless you manually add interaction terms or segment the data.
• Heavy Feature Engineering: You often need to bin or transform variables (like using Weight of Evidence binning) to capture non-linearities.
• Might Lose Small Accuracy Gains: Simpler models can sometimes underperform more sophisticated methods if the data has intricate patterns or interactions.

Despite these limits, logistic regression remains a top choice because it’s understandable and has a strong track record.

2. Machine Learning Models: “The New Kids on the Block”

Over the last decade, financial institutions have started exploring machine learning (ML) algorithms — decision trees, random forests, gradient boosting (like XGBoost), and neural networks — to detect subtle patterns in borrower data that might be missed by a simple linear model.

Advantages

1. Higher Predictive Power
   ML models can uncover complex relationships in the data, often outperforming logistic regression in terms of accuracy, AUC, or other metrics.
2. Automatic Feature Discovery
   Tree-based models can find useful splits or interactions on their own (e.g., “High loan amount AND short credit history” is especially risky).
3. Flexible with Different Data Types
   ML models can easily handle large sets of variables, alternative data sources, and non-linear trends without extensive manual transformations.

Drawbacks

• Interpretability (“Black Box”)
  Random forests or neural networks can be tough to explain. How do you tell a customer or a regulator why the model denied their application when it’s based on hundreds of decision trees or thousands of neural connections?
• Regulatory Concerns
  Lenders must still explain decisions in a clear, understandable way. If a model is too opaque, it can run into compliance issues.
• Possible Overfitting and Bias
  With more complexity comes a higher risk of overfitting, or of unintentionally learning biases buried in historical data.

Even so, ML models keep gaining traction, particularly where small increases in prediction quality can translate into big financial returns.

3. A Hands-On Example with Python

Let’s illustrate everything with some sample code. We’ll:

1. Generate a synthetic credit dataset (so we’re not using any real, private data).
2. Train four models: logistic regression, random forest, XGBoost, and a basic neural network.
3. Compare their performance (AUC).
4. Use SHAP and LIME to interpret a “black-box” model.

You can run this in a local Python environment or something like Google Colab. Make sure you have packages like scikit-learn, pandas, numpy, xgboost, shap, and lime installed.

3.1 Generate Synthetic Credit Data

import numpy as np
import pandas as pd

N = 5000

ages = np.random.randint(21, 70, size=N)
incomes = np.random.normal(loc=60000, scale=15000, size=N)
incomes = np.clip(incomes, 10000, 150000)

loan_to_income = np.random.uniform(0.1, 0.5, size=N)
loan_amounts = loan_to_income * incomes + np.random.normal(0, 5000, size=N)
loan_amounts = np.clip(loan_amounts, 2000, None)

credit_history = np.random.randint(0, 30, size=N)
home_owner = np.random.binomial(1, p=0.5, size=N)

# Simulate default probability using a logistic function
coef_intercept = -3.0
coef_ratio = 3.5
coef_history = -0.05
coef_age = -0.02
coef_home = -0.6

loan_income_ratio = loan_amounts / (incomes + 1e-6)
log_odds = (coef_intercept 
 + coef_ratio * loan_income_ratio 
 + coef_history * credit_history
 + coef_age * ages
 + coef_home * home_owner)
default_prob = 1 / (1 + np.exp(-log_odds))
defaults = np.random.binomial(1, p=default_prob, size=N)

data = pd.DataFrame({
 'age': ages,
 'income': incomes.astype(int),
 'loan_amount': loan_amounts.astype(int),
 'credit_history_yrs': credit_history,
 'home_owner': home_owner,
 'default': defaults
})

data.head()

We’re simulating factors like age, income, loan amount, credit history length, and home ownership. Each borrower gets a “default = 1 or 0” label, drawn from a logistic function of their features.

3.2 Train/Test Split

from sklearn.model_selection import train_test_split

X = data.drop('default', axis=1)
y = data['default']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

3.3 Logistic Regression

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

model_lr = LogisticRegression(max_iter=500, solver='lbfgs')
model_lr.fit(X_train, y_train)

y_pred_prob_lr = model_lr.predict_proba(X_test)[:, 1]
auc_lr = roc_auc_score(y_test, y_pred_prob_lr)
print(f"Logistic Regression AUC: {auc_lr:.3f}")

print("Coefficients:")
for name, coef in zip(X_train.columns, model_lr.coef_[0]):
 print(f"{name}: {coef:.3f}")

AUC is around 0.84. Coefficients should align with our simulation logic: higher loan amount → higher risk, older age → lower risk, and so on.

3.4 Random Forest

from sklearn.ensemble import RandomForestClassifier

model_rf = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
model_rf.fit(X_train, y_train)

y_pred_prob_rf = model_rf.predict_proba(X_test)[:, 1]
auc_rf = roc_auc_score(y_test, y_pred_prob_rf)
print(f"Random Forest AUC: {auc_rf:.3f}")

print("Feature importances:")
for name, imp in zip(X_train.columns, model_rf.feature_importances_):
 print(f"{name}: {imp:.3f}")

AUC: ~0.88. Random forest automatically captures non-linearities and interactions. We can also see the approximate “importance” of each feature.

3.5 XGBoost

from xgboost import XGBClassifier

model_xgb = XGBClassifier(n_estimators=100, max_depth=4,
 use_label_encoder=False, 
 eval_metric='logloss',
 random_state=42)
model_xgb.fit(X_train, y_train)

y_pred_prob_xgb = model_xgb.predict_proba(X_test)[:, 1]
auc_xgb = roc_auc_score(y_test, y_pred_prob_xgb)
print(f"XGBoost AUC: {auc_xgb:.3f}")

We see ~0.89 here, reflecting that gradient boosting can often squeeze out a bit more accuracy than a random forest.

3.6 Basic Neural Network

from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

model_nn = MLPClassifier(hidden_layer_sizes=(8, 8), 
 activation='relu', 
 max_iter=200, 
 random_state=42)
model_nn.fit(X_train_scaled, y_train)

y_pred_prob_nn = model_nn.predict_proba(X_test_scaled)[:, 1]
auc_nn = roc_auc_score(y_test, y_pred_prob_nn)
print(f"Neural Network AUC: {auc_nn:.3f}")

Likely results: ~0.88, similar to random forest. Neural networks don’t always dominate tabular data unless you add more complexity or have enormous datasets.

4. Explaining a Black-Box Model with SHAP and LIME

Machine learning often comes with a big challenge: interpretability. Luckily, there are powerful libraries like SHAP and LIME that help us open the black box.

4.1 SHAP (SHapley Additive Explanations)

SHAP calculates how each feature value contributes (positively or negatively) to an individual prediction relative to a baseline.

import shap

explainer = shap.TreeExplainer(model_rf)
shap_values = explainer.shap_values(X_test)

# Let's pick one test instance
sample_idx = 0
sample_input = X_test.iloc[[sample_idx]]
sample_pred = model_rf.predict_proba(sample_input)[0,1]
print("Predicted probability of default:", sample_pred)

print("SHAP values for this instance:")
for name, val in zip(X_test.columns, shap_values[sample_idx]):
 print(f"{name}: {val:.3f}")

This shows how each feature pushes the model’s prediction up or down from the average. You can do global summaries (e.g., average absolute SHAP value per feature) or local (per-customer) explanations.

4.2 LIME (Local Interpretable Model-agnostic Explanations)

LIME approximates a model’s behavior locally around a specific instance by fitting a simple surrogate model.

!pip install lime
from lime.lime_tabular import LimeTabularExplainer

explainer_lime = LimeTabularExplainer(
 training_data=X_train.values,
 feature_names=X_train.columns.tolist(),
 class_names=['No Default','Default'],
 discretize_continuous=True,
 mode='classification'
)

exp = explainer_lime.explain_instance(
 data_row=X_test.iloc[sample_idx].values, 
 predict_fn=model_rf.predict_proba,
 num_features=5
)

print("LIME Explanation:")
for feature, weight in exp.as_list():
 print(f"{feature}: {weight:.3f}")

For a given sample, LIME outputs:

loan_amount > 13000: +0.25
credit_history_yrs <= 5: +0.08
age <= 32: +0.03
...

interpreted as “these conditions, in this local region, are pushing the prediction toward higher default probability.”

5. Where Do We Go From Here?

Logistic regression has been the backbone of credit scoring because it’s transparent and has stood the test of time. But machine learning is incredibly appealing for its higher predictive accuracy, especially when there’s a lot of data and hidden patterns. So which path should lenders take?

• Combine Both Approaches
  It’s common to use ML to discover important features and interactions, then build a human-friendly logistic regression model around them. Alternatively, some teams keep a traditional scorecard as the “champion” model and use ML as a “challenger” for certain segments.
• Leverage Explainability Tools
  Tools like SHAP and LIME make it possible to satisfy regulatory demands for transparency, even if the core model is a complex ensemble.
• Stay on Top of Bias and Fairness
  Whether it’s logistic regression or a random forest, data can contain biases. Thorough checks and fairness metrics are essential — especially for high-stakes decisions like credit.
• Scale Up Responsibly
  ML models might need more data and computing power, but if carefully managed, they can provide a real competitive edge by better identifying credit risk.

Ultimately, the future of credit scoring will likely be a blend of trusted methods and newer technologies. As data grows and institutions get more comfortable with interpretability techniques, machine learning is poised to become a standard part of the credit risk toolkit. But the lessons learned from decades of logistic regression — keep it transparent, understandable, and well-managed — will remain crucial. After all, a model is only as good as how well we understand and govern it.

Thanks for reading! If you’re working on credit scoring models, consider experimenting with these approaches on your own data. Whether you stick with logistic regression or embrace a deep ensemble, remember that clarity, fairness, and accountability are just as important as predictive power.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI