You are a data scientist working for a bank and you have been tasked with developing an AI model that can accurately predict whether a customer will default on a loan or not. You have been given a dataset containing information about past customers and whether or not they defaulted on their loans. Your goal is to train a machine learning model that can make accurate predictions about future customers' likelihood of defaulting on a loan.
So what do you want me to do?
Sorry let me clarify, I would like for you to describe how you would go about creating an AI model that can accurately predict wether a customer will default on their loan or not. Your POV is a scientist that works for a bank.
I see. Here are the steps I would take:

Exploratory Data Analysis: First, I would perform exploratory data analysis on the dataset to gain insights into the relationships between different variables and the target variable (default or not). I would identify missing values, outliers, and other data quality issues, and take steps to address them.

Feature Engineering: Based on the insights from the exploratory data analysis, I would engineer new features that capture important relationships between variables and the target variable. This could include creating new variables by combining existing ones, transforming variables to better fit a model's assumptions, or adding external data sources.

Model Selection: Next, I would select a machine learning model that is appropriate for the problem at hand. In this case, since we are dealing with a binary classification problem (default or not), I would consider models such as logistic regression, decision trees, random forests, and neural networks.

Model Training: Once the model is selected, I would train it on the dataset using a portion of the data (e.g., 80%) as a training set and the remaining portion (e.g., 20%) as a validation set. During training, I would tune the model's hyperparameters to optimize its performance on the validation set.

Model Evaluation: After training, I would evaluate the model's performance on a separate test dataset that was not used during training. I would use metrics such as accuracy, precision, recall, F1 score, and ROC AUC to measure the model's performance.

Deployment and Monitoring: Finally, I would deploy the model into a production environment and continuously monitor its performance to ensure that it is working as expected. If necessary, I would retrain the model using new data or adjust its parameters to improve its performance over time.

Overall, the goal of this project is to develop an accurate machine learning model that can help the bank identify customers who are at a high risk of defaulting on a loan, allowing the bank to take appropriate actions to minimize its financial risk.