Abstract:
"A major risk to the sustainability and profitability of financial institutions are nonperforming loans, or NPLs. This project suggests incorporating a Non-Performing Loan
Prediction System into an existing loan management system of a well-known financial
institute in Sri Lanka.
The project's main goal is to provide the financial institute with a predictive tool for
determining the probability that borrowers will default on their loans when facilities are
being granted. By implementing a machine-learning based prediction model, the
institute hopes to improve loan approval, decision-making procedures and strengthen
its risk management strategies.
The identified problem is that the financial institute does not have a specific model for
predicting non-performing loans. Officers do not now have a systematic method to
determine the probability of a customer's loan would become non-performing over
time. This gap results in the institute's incapability to predict and minimize possible
credit risks within the institute.
The proposed solution involves implementing a non-performing loan prediction system
and integrate it with the existing loan management system. At the point of loan
approval, the system aims to provide a Non-Performing Loan score for every customer
using machine learning algorithms. The research will make use of supervised machine
learning algorithms with a variety of datasets that include customer profiles, historical
loan information, and other relevant features. Algorithms such as Random Forest and
Logistic Regression will be trained and evaluated to identify the most accurate model
for predicting Non-Performing Loans.
In conclusion, the financial institute will receive many significant advantages from the
implementation of the NPL Prediction System. This can result in lesser non-performing
loans (NPLs), better portfolio performance overall, and a stronger financial position for
the organization."
