Intellectual Property India Publishes Patent Application for 'A Multi-Domain Customer Churn Prediction System Using Machine Learning For Telecom, Banking, And Insurance Industries' Filed by Dr. Santhosh Kumar Balan; Mr. A. Keshav Reddy; Mr. Ayodhya Krishna Teja; and Ms. Srinithya Jonnada

MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641063511 A) filed by Dr. Santhosh Kumar Balan; Mr. A. Keshav Reddy; Mr. Ayodhya Krishna Teja; and Ms. Srinithya Jonnada, Ibrahimpatnam, Telangana, on May 20, for 'a multi-domain customer churn prediction system using machine learning for telecom, banking, and insurance industries.'

Inventor(s) include Dr. Santhosh Kumar Balan; Mr. A. Keshav Reddy; Mr. Ayodhya Krishna Teja; and Ms. Srinithya Jonnada.

The application for the patent was published on May 29, under issue no. 22/2026.

According to the abstract released by the Intellectual Property India: "The present invention discloses a Multi-Domain Customer Churn Prediction System using machine learning techniques for predicting customer attrition across telecom, banking, and insurance industries. The system collects and processes customer datasets containing demographic, transactional, behavioral, and service usage information to identify customers likely to discontinue services. The invention performs data preprocessing operations including missing value handling, data cleaning, categorical encoding, normalization, and feature transformation to improve data quality and prediction accuracy. The processed data is analyzed using supervised machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors (KNN) for churn classification. The system generates churn probability predictions, identifies high-risk customers, and evaluates model performance using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. The invention further provides analytical visualizations and actionable insights that support proactive customer retention strategies and business decision-making. The proposed invention offers a scalable, automated, and cost-effective predictive framework capable of handling large multi-domain datasets with improved prediction accuracy, operational efficiency, and customer retention performance."

Disclaimer: Curated by HT Syndication.