analyze telecom fraud at hadoop scale
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Page 1
Diyotta, Inc. All Rights Reserved
Analyze Telecom Fraud at Hadoop Scale
29th June 2016
Sanjay Vyas
Co-founder & COO, Diyotta
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Diyotta, Inc. All Rights Reserved
Telecom-Relevant Glossary
• CDR – Call Detail Record
• Any phone call generates a CDR
• IPDR – IP Detail Record
• Any internet browsing activity generates an IPDR
• IVR – Interactive Voice Response
• Automated telephone response system usually for typical queries
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Diyotta, Inc. All Rights Reserved
Fraud in Telecom
Global Mobile Industry
$2.2T
Revenue Losses due to
Fraud
$46.3B
Reference: http://www.argyledata.com/files/Telecom-Fraud-101-eBook.pdf
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A Day In Life of Telecom Data (Fraud Use-Case)
Source Systems
Ingestion Pipelines
Target Data Sets
Fraud Analysis
Minataur
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Legacy State for Fraud Analytics
Monolithic Script Based file Ingestion
MinataurFraud Application
• Limited capacity for processing
• Cannot Scale for Volume/Velocity
• Cannot do on-demand real-time Analytics
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Business Challenges
• Business not able to
• analyze IPDR Data due to the sheer
volume
• ingest streaming data from IVR
systems for fraud analysis
• Perform on-demand real-time fraud
analytics for deeper insights
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IT Challenges with Hadoop Adoption
• Skill Gap
• Limited in-house expertise on evolving technologies and keep up the pace
• Enterprise Standards
• Manual coding suffers from quality/maintenance issues and is inconsistent
• Scalability across data and technology
• Real-time, social media, multi-processing engines
• Data Lineage
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Solution Components
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Solution Architecture for Fraud Use-Case
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Diyotta Modern Data Integration Platform
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Diyotta Architecture
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Customer Success Story
Page 13
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Q&A
Sanjay Vyas
Email: sanjay@diyotta.com
Web: http://www.diyotta.com
Trial: www.Diyotta.com/try
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