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		<www.jsetms.com>
		<Title>CREDIT CARD SPENDING PATTERN & CUSTOMER TRANSACTION ANALYTICS DASHBOARD</Title>
		<Author>1 N Ramya Krishna, 2 P Supritha, 3 V Nithin Raj, 4 S Vijay</Author>
		<Volume>03</Volume>
		<Issue>09</Issue>
		<Abstract>The Credit Card Spending Pattern  Customer Transaction Analytics Dashboard is a data analytics and visualization system designed to analyze credit card transaction patterns and customer spending behavior Credit card transactions generate large amounts of information related to purchase amounts transaction categories locations payment channels and transaction dates Analyzing these patterns can help financial organizations understand spending trends and improve business planning The proposed system collects appropriate transaction information such as transaction date transaction amount merchant category transaction type payment channel geographic region customer segment and transaction status The system processes this information and calculates important Key Performance Indicators KPIs such as total transaction value transaction count average transaction amount spending by category monthly spending growth and transaction success rate The dashboard presents the analyzed information using interactive charts graphs tables maps and KPI cards Authorized analysts and managers can compare spending patterns across different merchant categories customer segments regions payment channels and time periods Filters allow users to focus on specific transaction categories or reporting periods The system can identify changes in spending behavior highvolume transaction categories seasonal spending patterns and differences between customer segments It can also help identify unusual aggregate transaction patterns that may require further investigation These insights can support customerservice planning product strategy financial reporting and transaction monitoring Overall the Credit Card Spending Pattern  Customer Transaction Analytics Dashboard provides a centralized platform for transforming transaction data into meaningful business insights It reduces manual reporting efforts and supports datadriven financial analysis Future enhancements can include anomaly detection spending forecasting customer segmentation personalized financial insights realtime transaction monitoring and AIassisted trend analysis</Abstract>
		<permissions>
<copyright-statement>Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved</copyright-statement>
<copyright-year>2026</copyright-year>
</permissions>
		</www.jsetms.com>
		