  In this study, a predictive model using Multi-layer Perceptron of Artificial
Neural Network architecture was developed to predict customer churn in a
financial institution. Previous researches have used supervised machine
learning classifiers such as Logistic Regression, Decision Tree, Support Vector
Machine, K-Nearest Neighbors, and Random Forest. These classifiers require
human effort to perform feature engineering which leads to over-specified and
incomplete feature selection. Therefore, this research developed a model to
eliminate manual feature engineering in data preprocessing stage. Fifty
thousand customers? data were extracted from the database of one of the leading
financial institution in Nigeria for the study. The multi-layer perceptron
model was built with python programming language and used two overfitting
techniques (Dropout and L2 regularization). The implementation done in python
was compared with another model in Neuro solution infinity software. The
results showed that the Artificial Neural Network software development (Python)
had comparable performance with that obtained from the Neuro Solution Infinity
software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating
Characteristic) curve graphs are 0.89 and 0.85 respectively.
