Machine Learning Case Note: Logistic Regression and Machine Learning in R for Financial Risk Analysis

In the world of modern finance, organisations operate in an environment defined by uncertainty, fast-moving markets, and increasing regulatory scrutiny. Whether assessing the likelihood of a borrower defaulting, detecting fraudulent card transactions, or forecasting early mortgage prepayments, financial institutions rely heavily on data-driven models to quantify and manage risk. Among the many machine-learning methods available today, logistic regression remains one of the most widely adopted tools because it delivers the perfect balance of interpretability, statistical rigour, and predictive power. Its ability to generate clear probability-based predictions makes it indispensable for scorecards, risk segmentation, portfolio monitoring, and model validation frameworks across the industry.

While advanced machine-learning models such as random forests, XGBoost, and neural networks have gained popularity, logistic regression continues to serve as the backbone of risk analytics. This is largely due to its transparency—risk managers, auditors, and regulators can easily understand how each variable influences the likelihood of an event such as default or fraud. In practical applications, the ability to explain a model is often just as important as its ability to predict accurately. For this reason, logistic regression remains the anchor model upon which more complex analytics pipelines are built, validated, and benchmarked.

At the same time, the financial sector is experiencing rapid growth in the scale and complexity of available data. Customer behaviour is captured through digital footprints, transaction networks, credit histories, and macroeconomic indicators. With this expansion comes new challenges—imbalanced datasets, nonlinear relationships, and operational constraints that demand robust and scalable modelling approaches. Using R, analysts can combine the strengths of logistic regression with flexible simulation, preprocessing, and evaluation tools, making it possible to build high-quality models even in complex or imperfect data environments.

This guide provides a comprehensive, end-to-end walkthrough of applying logistic regression and machine-learning concepts in R for financial risk analysis. Through three realistic case studies—credit default modelling, fraud detection, and mortgage prepayment forecasting—you will learn how to simulate financial datasets, prepare and scale features correctly, estimate probabilities using logistic regression, evaluate predictive performance, and interpret the economic significance of each variable. Whether you are developing a regulatory scorecard, building risk dashboards, or exploring predictive modelling for the first time, the techniques in this guide form a strong foundation for modern financial analytics and decision-making.

1. Why Logistic Regression for Financial Risk?

In financial risk analysis, many core questions are binary:

• Will this borrower default on their loan?

• Is this card transaction fraudulent?

• Will this mortgage be prepaid early?

These are classic classification problems. Logistic regression is widely used because:

• It estimates probabilities (e.g. ( P(\text{default}=1 \mid X) )).

• It is transparent and interpretable: each coefficient shows how a risk factor affects odds of an event.

• It integrates well with regulatory, scorecard, and portfolio risk frameworks.

This guide uses R to build logistic regression models for three simulated financial risk case studies:

1. Credit Default Risk

2. Fraud Detection (Imbalanced Data)

3. Mortgage Prepayment Risk

For each, we’ll:

• Simulate realistic financial data

• Split into train/test sets

• Scale features

• Fit logistic regression models

• Evaluate with confusion matrices and ROC–AUC

• Interpret coefficients and probabilities

At the end you’ll get a single end-to-end R script that runs all three case studies.

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2. Environment Setup in R

You’ll use a few core packages:

# Install once if needed:
# install.packages(”pROC”)

library(pROC)   # For ROC curves and AUC

We will also rely on base R (no tidyverse required) to keep everything simple and portable.

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3. Logistic Regression Recap

Logistic regression models the log-odds of a binary outcome:

[ \log\left( \frac{p}{1-p} \right) = \beta_0 + \beta_1 x_1 + \dots + \beta_k x_k ]

where:

• ( p = P(Y=1 \mid X) ) (e.g. probability of default)

• ( x_j ) are features such as income, loan amount, credit score

• ( \beta_j ) are coefficients estimated from data

In R, we fit this with:

model <- glm(target ~ ., data = train_data, family = binomial)

and get predicted probabilities with:

prob <- predict(model, newdata = test_data, type = “response”)

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4. Case Study 1 – Credit Default Risk

4.1. Simulate Credit Default Data

We simulate a loan portfolio with:

• Features: income, age, loan_amount, interest_rate, credit_score, num_late_payments, unemployed

Subscribe to download the complete, end-to-end workflow in R … … …

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