In recent years, banks have observed increased customer departures for various reasons. Churn modeling is essential to formulating effective strategies for customer retention. This study aimed to anticipate whether a customer is likely to leave the bank in the foreseeable future. Churn prediction involves identifying clients likely to discontinue service or terminate their subscription. This prediction holds significant importance for many firms, as acquiring new customers often incurs higher costs than retaining existing ones. The dataset utilized includes diverse parameters such as credit score, age, gender, estimated salary, etc. In addressing this issue, neural networks prove advantageous as they can adeptly learn and model nonlinear and intricate interactions. This capability is vital because many real-life relationships between inputs and outputs exhibit non-linear and complex patterns. Consequently, an Artificial Neural Network with two hidden layers is employed within the Python environment. The study delves into the correlation between various parameters, conducting thorough exploratory data analysis to discern the key factors influencing a customer's decision to discontinue their association with the bank.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Churn Modeling Using Artificial Neural Network


    Additional title:

    Smart Innovation, Systems and Technologies



    Conference:

    Congress on Control, Robotics, and Mechatronics ; 2024 ; Warangal, India February 03, 2024 - February 04, 2024



    Publication date :

    2024-11-14


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    A Survey on Artificial Intelligence in Telecommunication for Churn Prediction

    U, Prakash / A, Anila / C, Swetha et al. | IEEE | 2022


    Customer Churn Prediction using Machine Learning

    Peddarapu, Rama Krishna / Ameena, Sofia / Yashaswini, Surepally et al. | IEEE | 2022


    Modeling Thermal Engine Behavior Using Artificial Neural Network

    Jander, Bojan S. / Baar, Roland | British Library Conference Proceedings | 2017


    Modeling Thermal Engine Behavior Using Artificial Neural Network

    Jander, Bojan S. / Baar, Roland | SAE Technical Papers | 2017


    Advancing Telecom Customer Churn using Deep Learning

    Seethalakshmi, R. / Varsha, B. Swarna | IEEE | 2024