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    <responseDate>2026-10-12T00:25:40Z</responseDate>
    <request verb="GetRecord" identifier="oai:data.mendeley.com/s83xr4fsv9.1" metadataPrefix="oai_dc">https://data.mendeley.com/oai</request>
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            <header>
                <identifier>oai:data.mendeley.com/s83xr4fsv9.1</identifier>
                <datestamp>2026-08-14T06:17:05Z</datestamp>
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    <dc:creator>fatah, hemn</dc:creator>
    <dc:title>A Deployment-Derived Online Banking Fraud Detection Inference-Log Dataset from a Live Cloud-Based Deep Learning System</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains 56,962 transaction inference logs collected through a live, cloud-deployed online banking fraud detection demonstration system over a 30-day period in January 2026. The data were gathered through a REST API endpoint hosted on a Ubuntu Linux virtual private server, where a trained hybrid CNN-LSTM model processed each incoming transaction in real time. The dataset contains 98 confirmed fraudulent transactions (0.172% fraud rate). Each record includes 30 input features alongside model-generated outputs comprising fraud probability score, risk level, confidence estimate, action recommendation (BLOCK or ALLOW), and response latency in milliseconds. The deployment was a proof-of-concept system operating as a public demonstration interface and not a licensed banking institution.</dc:description>
    <dc:subject>Computer Science</dc:subject>
    <dc:subject>Artificial Intelligence</dc:subject>
    <dc:subject>Information Systems Management</dc:subject>
    <dc:contributor>Rashid, Zryan Najat</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/s83xr4fsv9.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/s83xr4fsv9.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:relation>https://data.mendeley.com/datasets/s83xr4fsv9</dc:relation>
    <dc:date>2026-08-14T06:17:05Z</dc:date>
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