Credit Risk Analysis LSTM

High-precision loan default prediction using stacked LSTM layers and sequential financial modeling.

July 2025• Team: 3 Developers• Role: Data Scientist

About this Project

This project focuses on predicting credit risk using Long Short-Term Memory (LSTM), a variant of Recurrent Neural Networks (RNNs) optimized for financial sequential data. By capturing long-term temporal dependencies in credit history and financial behavior, the system identifies high-risk loan applicants with significantly higher accuracy than traditional linear models. The analysis incorporates business-critical metrics such as Default Capture Rate and Approval Rate to maximize institutional profitability.

Tech Stack

Python

TensorFlow

Keras

LSTM

Scikit-learn

SMOTE

Tools Used

Jupyter Notebook

Hugging Face Spaces

Pandas

Seaborn

SMOTE

Key Features

Sequential Model Architecture

▸LSTM Layers: Stacked Long Short-Term Memory layers to capture complex temporal patterns in debt behavior.
▸Dropout Optimization: Integrated dropout layers to prevent overfitting on specific credit profiles.
▸Binary Optimization: Sigmoid activation output with Adam optimizer and binary cross-entropy loss function.

Financial Data Preprocessing

▸3D Reshaping: Data transformation into `[samples, timesteps, features]` format for deep learning sequential input.
▸SMOTE Sampling: Implementation of Synthetic Minority Over-sampling to balance default vs. non-default cases.
▸Feature Scaling: Numerical normalization and categorical encoding for employment and loan-type stability.

Risk & ROI Analytics

▸Profitability Analysis: Comparative study on ROI/cost savings from avoiding defaults vs baseline approval rates.
▸Risk Segmentation: Automatic classification of applicants into Low, Medium, and High-risk tiers based on probability scores.
▸Advanced AUC Tuning: Hyperparameter tuning via GridSearchCV to maximize Default Capture Rate.

Comprehensive Evaluation

▸Default Capture Rate: Measuring the exact proportion of actual defaults identified by the model.
▸Business Metrics: Evaluating model impact via Approval Rate vs institutional Risk Appetite.
▸Interactive Visualization: Deployment on Hugging Face Spaces for real-time model interaction and prediction testing.

Highlights

Stacked LSTM Neural Network

SMOTE Imbalance Handling

Financial ROI Analysis

Installation

Model Environment

git clone https://github.com/Arfazrll/CreditRisk_Analysis
pip install -r requirements.txt

Preprocessing Data

# Data must be reshaped for LSTM
X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))

Training & Eval

python train_lstm.py
# Outputs Accuracy, Precision, Recall, and AUC metrics

Challenges & Solutions

Challenge

Imbalance Financial Datasets

Solution

Applied SMOTE (Synthetic Minority Over-sampling Technique) to ensure the LSTM model learns to identify rare default events as effectively as frequent low-risk ones.

Challenge

Vanishing Gradient in Deep RNNs

Solution

Utilized LSTM gates (Input, Forget, Output) to maintain long-term memory gradients, essential for capturing years of credit history.

Challenge

Overfitting on Loan Profiles

Solution

Implemented early stopping and dropout strategies to ensure the model generalizes across diverse demographic and financial sectors.

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