To accomplish this task, follow these steps:

1. Data preprocessing:
   Start by exploring and cleaning the dataset. This may involve:

   a. Handling missing values: Impute missing values with appropriate techniques such as mean, median, or mode imputation, or drop rows/columns if there is a high percentage of missing values.

   b. Encoding categorical variables: Convert categorical variables into numerical format using techniques like one-hot encoding or label encoding.

   c. Feature scaling: Standardize or normalize numerical features to bring them to the same scale.

   d. Feature engineering: Create new meaningful features based on the existing ones or combine them in a way that can improve the model's performance.

2. Split the data:
   Divide the cleaned dataset into a training set (70-80% of the data) and a testing set (20-30% of the data) to evaluate the model's performance on unseen data.

3. Model selection:
   Choose a suitable machine learning algorithm for this classification problem. Some popular classification algorithms include Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Gradient Boosting Machines (GBM). You can also consider using deep learning models like Artificial Neural Networks (ANN).

4. Model training:
   Train the selected model(s) on the training dataset. During this process, the model will learn patterns and relationships between the input features and the target variable (loan default status).

5. Model evaluation:
   Evaluate the performance of the trained model(s) on the testing dataset. Use appropriate evaluation metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Compare the performance of different models to select the best one.

6. Hyperparameter tuning:
   Optimize the hyperparameters of the chosen model(s) using techniques like Grid Search, Random Search, or Bayesian optimization to improve their performance.

7. Model validation:
   Once the best model is selected and optimized, validate its performance using cross-validation techniques, such as k-fold cross-validation, to ensure that the model generalizes well to new data.

8. Model deployment:
   Deploy the final model to make predictions on future customers' likelihood of defaulting on a loan. This can be integrated into the bank's loan approval process to assist in making more informed decisions about granting loans.

9. Continuous improvement:
   Regularly update the model with new data to ensure that it remains accurate and relevant. Monitor the model's performance and retrain or adjust it as needed.
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