analyze telecom fraud at hadoop scale

Diyotta, Inc. All Rights Reserved

Analyze Telecom Fraud at Hadoop Scale

29th June 2016

Sanjay Vyas

Co-founder & COO, Diyotta


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


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


A Day In Life of Telecom Data (Fraud Use-Case)

Source Systems

Ingestion Pipelines

Target Data Sets

Fraud Analysis

Minataur


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


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


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


Solution Components


Solution Architecture for Fraud Use-Case


Diyotta Modern Data Integration Platform


Page 11


Diyotta Architecture


Page 12


Customer Success Story

https://vimeo.com/167189384







Q&A

Sanjay Vyas

Email: [email protected]

Web: http://www.diyotta.com

Trial: www.Diyotta.com/try

mailto:[email protected]

http://www.diyotta.com/

http://www.diyotta.com/try

analyze telecom fraud at hadoop scale

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