[Day 195] Reading about bank term deposit subscription prediction models
Hello :)
Today is Day 195!
One of my lab mates sent me a few papers to skim through to help for his team's project related to predicting bank deposit subscriptions. Thanks to ChatGPT skimming is very easy now. Below are the outputs from ChatGPT on the five papers I got.
Introduction:
- Direct marketing is essential for personalized client communication in banking.
- Predictive analytics and machine learning offer new opportunities for refining marketing strategies.
- The research aims to enhance direct marketing's effectiveness by applying sophisticated analytical models.
Literature Review:
- Examines eight studies on machine learning and data mining in banking.
- Highlights methodologies like the S_Kohonen network, Improved Whale Optimization Algorithm, META-DES-AAP, and various machine learning models.
- Emphasizes the importance of time deposits, customer credit products, and telemarketing success in banks.
Proposed Methodology:
- Data Collection and Exploration: Utilizes Kaggle datasets and explores them using visualization tools.
- Exploratory Data Analysis (EDA): Uses techniques like crosstabulation and heatmaps to uncover patterns and relationships in the data.
- Feature Engineering and Preprocessing: Transforms and prepares data for machine learning models through encoding and scaling.
- Model Implementation: Applies models like SGD Classifier, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Decision Tree, and Random Forest Classifiers. Evaluates their performance using accuracy, precision, and F1 score.
Results and Evaluation:
- Presents classification results, highlighting the Random Forest Classifier as the best performer.
- Evaluates models based on metrics like accuracy, sensitivity, specificity, PPV, NPV, and F1 score.
Conclusion:
- Predictive analytics and machine learning significantly impact direct marketing in banking.
- The study provides a comprehensive analysis and evaluation of different machine learning models.
- Findings guide future strategies for direct marketing in the banking sector.
The study investigates methods for predicting potential long-term deposit investors using historical bank data and machine learning techniques. Banks use various approaches to offer long-term deposits to consumers, but an ineffective approach wastes resources. The research uses machine learning algorithms such as logistic regression, Random Forest, and LightGBM to predict consumer interest based on features like age, job, marital status, and more.
The study highlights the issue of imbalanced data, with a significant disparity between positive and negative class labels. To address this, SMOTE (Synthetic Minority Over-sampling Technique) is applied to balance the dataset, resulting in improved prediction accuracy. The dataset from Kaggle comprises 32,950 entries and 16 features, and data preprocessing includes cleaning and removing irrelevant data.
The study compares the accuracy of different machine learning models after applying SMOTE. LightGBM achieved the highest accuracy at 90.63%, slightly higher than Random Forest at 90.34%, and logistic regression at 88.89%. The results demonstrate that LightGBM, combined with SMOTE for data balancing, provides the most accurate predictions for long-term deposit investments. This method's novelty lies in its higher prediction accuracy compared to previous studies.
Identifying the Best Machine Learning Model for Predicting Bank Term Deposits: An Empirical Study Using Public, Post Financial Crisis Data
The paper investigates various machine learning models to predict bank term deposits, aiming to optimize targeted marketing efforts. The main sections of the paper are as follows:
Introduction
The concept of bank term deposits is introduced as a strategy for banks to secure stable funds for lending and investment. The paper highlights the importance of the net interest margin, which is the difference between interest earned from borrowers and interest paid to depositors. The research question posed is, "Which classification model can accurately predict bank term deposits from the public?"
Methodology
Bias-Variance Tradeoff
This tradeoff is essential in machine learning to minimize overfitting and underfitting, aiming for models that perform well on both training and test data.
Accuracy Paradox
The dataset used is imbalanced, with many more instances of customers not making deposits. This imbalance affects the accuracy and reliability of the models, leading to the need for addressing the accuracy paradox.
Statistical Models
- Binomial Logistic Regression: A popular method for binary classification tasks. It uses explanatory variables to predict the binary target variable, employing the logistic function to generate probabilities.
- Decision Tree Classifier: A supervised learning model that uses a tree-like structure of nodes to make decisions. The model aims for pure leaf nodes, but methods like pruning and cross-validation are used to avoid overfitting.
- Artificial Neural Network (ANN): Inspired by the human brain, ANNs consist of interconnected neurons in layers. The network adjusts weights and biases through training to improve performance. Activation functions like Sigmoid, ReLU, and Leaky ReLU are used to process inputs and generate outputs.
- Support Vector Machine (SVM): A supervised learning model that finds the optimal hyperplane to separate classes. SVMs use kernel functions to transform data into higher-dimensional spaces, improving classification efficiency.
Conclusion
The paper compares these models to determine which best predicts bank term deposits, enhancing the bank's marketing efficiency and potentially increasing the net interest margin. The emphasis is on balancing precision and recall to effectively target potential customers.
Background: Predicting bank deposits is a crucial task for banks to manage their liquidity and make informed decisions. Traditional statistical models have limitations in handling complex data and non-linear relationships, making it challenging to achieve accurate predictions.
Objective: The authors aim to develop an ensemble learning approach to predict bank deposits using a combination of machine learning algorithms.
Methodology
- Data Collection: The authors collected a dataset of 10,000 customer records from a Bangladeshi bank, featuring 14 attributes, including demographic, financial, and behavioral characteristics.
- Data Preprocessing: The dataset was cleaned, transformed, and normalized to prepare it for modeling.
- Feature Selection: A correlation-based feature selection method was applied to select the most relevant features.
- Ensemble Learning: Three base models were trained: Decision Tree, Random Forest, and Gradient Boosting. The authors then combined these models using stacking and voting techniques to create an ensemble model.
Results
The ensemble model outperformed individual base models, achieving a higher accuracy of 92.15% in predicting bank deposits.
The stacking ensemble approach performed better than the voting approach, indicating that the former is more effective in combining the strengths of individual models.
Feature importance analysis revealed that the most significant predictors of bank deposits were average balance, age, and occupation.
Conclusion
The study demonstrates the effectiveness of ensemble learning in predicting bank deposits, highlighting the potential of machine learning techniques in improving the accuracy of deposit predictions. The proposed approach can be useful for banks to optimize their liquidity management and make data-driven decisions.
Background: Bank deposit subscription is a crucial aspect of banking, and predicting customer behavior is essential for banks to design effective marketing strategies. Traditional statistical methods have limitations in handling large datasets and complex relationships, making machine learning a promising approach.
Objective: The authors aim to develop prediction models using machine learning techniques to forecast bank deposit subscription behavior.
Methodology:
- The authors collected a dataset of 10,000 customers from a commercial bank, including demographic, financial, and behavioral features.
- They applied four machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines (SVM), and Gradient Boosting.
- The models were trained and evaluated using a 70:30 split of the dataset for training and testing, respectively.
- The authors used various performance metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), to evaluate the models.
Results:
- The results showed that all four machine learning algorithms outperformed traditional statistical methods in predicting bank deposit subscription behavior.
- The Gradient Boosting algorithm achieved the best performance, with an accuracy of 86.2%, precision of 84.5%, recall of 88.1%, F1-score of 86.3%, and AUC of 0.93.
- The authors found that the most important features contributing to the prediction models were customer age, income, credit score, and deposit history.
Conclusion:
- The study demonstrates the effectiveness of machine learning techniques in predicting bank deposit subscription behavior.
- The authors suggest that banks can use these models to identify high-potential customers, design targeted marketing campaigns, and improve customer retention.
- The study's findings can be generalized to other financial institutions and industries, highlighting the potential of machine learning in predictive analytics.
On another note, I am staying up late today to watch the EURO final, but while waiting for 4am to come - I am transferring posts to my github blog. I hope I get a decent amount in.
That is all for today!
See you tomorrow :)
