Menu
PREDICTIVE ANALYSIS & MACHINE LEARNING
Loan Default Prediction
Business Problem:
A finance company aims to predict the likelihood of loan default for clients seeking various types of loans, including unsecured personal loans, auto refinancing loans, business loans, medical financing, and investment options.
COMPANY OVERVIEW
The finance company offers personal loans, auto refinancing loans, business loans, medical financing, and some investment options. Prior to 2020, they operated a peer-to-peer lending service that enabled borrowers to obtain unsecured personal loans ranging from $1,000 to $40,000 USD.
THE CHALLENGE
One of the key challenges faced by the company was assessing the risk associated with each loan application. They would like to proactively identify clients who are at a higher risk of defaulting on their loans. This enables them to take necessary measures to minimize potential losses and make informed decisions when granting new loans.
SOLUTION
A predictive model that accurately predicts the likelihood of loan default for clients across various loan types.
DATASET OVERVIEW
Dataset contains information about more than forty-five thousand personal loans lent through the company.
Represents a portion of the loans facilitated by the company.
Covers diverse loan types, including personal loans, auto refinancing loans, business loans, medical financing, and investment options.
Dataset includes comprehensive information about loan clients, their repayment habits, and other relevant features.
Used for developing a predictive model to assess loan default risk.
Dataset served as a basis for exploring loan applicant characteristics and their impact on loan default likelihood.
Utilized logistic regression, a powerful machine learning technique which is particularly well-suited for binary classification tasks, for building the predictive model.
IMPACT
By solving this business problem, the company is able to make informed decisions regarding loan approvals and effectively manage the risk inherent in their lending operations.