Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258585 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 10 [Article No.:] 481 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-24
Publisher: 
MDPI, Basel
Abstract: 
A constant in the business world is the frequent movement of customers joining or abandoning companies' services and products. The customer is one of the company's most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most efficient way to maintain the customer base, since the replacement of dropouts by new customers costs, on average, 40% more. Aiming to mitigate the churn (customer evasion) phenomenon, this study compared predictive models to discover the most efficient method to identify customers who tend to drop out in the context of a banking organization. A literature review of related works on the subject found the neural network, decision tree, random forest and logistic regression models were the most cited, and thus the models were chosen for this work. Quantitative analyses were carried out on a sample of 200,000 credit operations, with 497 explanatory variables. The statistical treatment of the data and the developments of predictive models of churn were performed using the Orange data mining software. The most expressive results were achieved using the random forest model, with an accuracy of 82%.
Subjects: 
churn
machine learning
predictive model
neural networks
decision tree
random forest
logistic regression
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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