webinar: how to prevent bank fraud & monitor risk in real time

How to Prevent Bank Fraud & Monitor Risk in Real-TimeMarch 2, 2016Vassili Patrikis, Big Data Lead, AccentureMatthew Stump, Head of Product Marketing, DataStax

2

ANALYTICS LANDSCAPE

IS SHIFTING

Copyright © 2015 Accenture All rights reserved.

3Copyright © 2015 Accenture All rights reserved.

Democratization of data and data discovery

New data sources

Complex data-driven environment with significant opportunities to create business value

Focus on advanced analytics

Big data and hybrid architectures

Changing skills requirements

Market Trends


Issues OutcomesHarness the power of big data

Employ innovative analytical tools

Isolate the signal from the noise

Embed insights into decisions and processes

Data

Analytics

Insights

Actions

Shrinking market share

Pricing pressures

Customer defection

Fragmentation and complexity

Inefficient operations

Aged platforms and systems

Employee engagement

Fraud & non-compliance

Expanding market share

Enhanced cost andcash advantage

Customer loyalty

Speed-to-insights

Operational Excellence

Leading edge platforms

Winning the war for talent

Reduced risk and fraud

Helping our clients use data and analytics to defend, differentiate and disrupt their markets

The Accenture Analytics to High Performance


MANAGEMENT &

MOBILIZATIONMOVEMENT CONSUMPTION

How to move data swiftly from its source to places in the organization where it is needed

How to mobilize as quickly as possible to drive data and analytic driven insights

How to enable users, machines and algorithms to leverage data and analytics to support information in circulation @ scale

Each component can address data movement, management & mobilization, and/or consumption, and each has distinctive technology features.

Challenges


Organizations can choose from many different data technology components to build the architecture needed to support data acceleration

Cache Clusters

Ingestion

In-memory Databases

Big Data Platforms

Complex Event Processing

Appliances

Architectural Components & their technology

features

Distributed computingIn-memoryStreaming

Distributed computingIn-memoryStreaming

Distributed computingIn-memory

Distributed computingIn-memory StreamingOptimized network

In-memory

Distributed computingIn-memoryOptimized networkCustom silicon

Data Supply Chain Components


Technology Components:

Volume, type and velocity of data determines how data management principles are applied.

Ingestion/Integration:

Big Data Platform Deployment Options Performance ClusterCommon Layer Data Management

Dat

a S

trate

gy

Service Interface Layer

Application

Big Data Core

Query Engine

In-Mem Analytics

Dat

a A

rchi

tect

ure

– D

ata

Mod

elin

g, S

truct

ures

, Etc

.

Met

adat

a M

anag

emen

t

Sec

urity

/ P

rivac

y / E

ncry

ptio

n

Mas

ter D

ata

Man

agem

ent

Mas

ter D

ata

Aut

horin

g

Con

solid

atio

nC

o-E

xist

ence

Reg

istry

Tran

sact

ion

Streaming Event Processing

Search

Dat

a C

lean

sing

/ P

rofil

ing

/ Err

or S

ensi

ng Distributed Cache In-Mem DB NoSQL

Bar

e-M

etal

Dire

ct In

stal

lV

irtua

lized

App

lianc

e

Clo

udP

ublic

Virt

ual P

rivat

eM

ulti-

tena

nt

Dat

a G

over

nanc

e

Traditional Relational

Sources

Transfer (Batch, Stream, Interactive)

Transfer Paths Added Processing Interactivity Enabled

Technology Enabled Operations @ Speed of “Now”

Copyright © 2015 Accenture All rights reserved.

• Does my data set require random access at low latency?• Does the business require high volume concurrent writes or reads from the database

layer?• Is there business impact of database downtime?• Does the data need to be replicated to more than a single database?• Does the data size have a significant growth rate over time?• Is there a business case for analytics in near real-time?

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When is DataStax the Right Solution?

© 2015 DataStax, All Rights Reserved. 9

HTAPLambdaTrans-analyticsSystems of intelligenceStreaming analytics


HTAPLambdaTrans-analyticsSystems of intelligenceStreaming analytics

REALTIMEVALU

E


DistributedLinear scaleContinuous availabilityCommodity Hardware


DistributedLinear scaleContinuous availabilityCommodity Hardware

EPICSCAL

E

© 2015 DataStax, All Rights Reserved. © 2015 DataStax, All Rights Reserved. 13

ScaleValue


Cloud Applications


Attributes of Cloud Applications

Continuously AvailableGeographically DistributedOperationally Low LatencyLinearly ScalableImmediately Decisive

Architecture of an Anti-Fraud Application


Web App Queue Ingest Operational

Database




Database

Solr




Database

Hadoop

Solr

Queue

Hive

Search

Batch Processing



Web App Queue Ingest

Spark Streaming

Operational

Database

Hadoop

Solr

Queue

Hive

Search

Batch Processing

IBM Real-time Analytics Reference Architecture

Source: Explore the advanced analytics platform (http://ibm.co/1PLAneB)© 2015 DataStax, All Rights Reserved. 20

Oracle Reference Architecture

Source: Big Data in Financial Services and Banking (http://bit.ly/1GP8mmE)

“70% of Hadoop deployments will not meet cost savings and

revenue generation objectives due to skills and integration

challenges.”

© 2015 DataStax, All Rights Reserved. 24 Source: Hadoop promises not yet paying off (http://tek.io/1MrHtSx)

DSE Real-time Analytics Reference Architecture

HTTP Application Message Queue

Streaming

Analytics

BatchAnalytic

s

Real-time



DataStax Enterprise is the database for cloud applications

Gartner ODBMS MQ 2015


DataStax Use Cases• Customer 360°• Master data management• Customer profile management• Authentication and identity management• Product personalization• Anti-fraud and money laundering• Payments and transactions• Risk reporting/capital adequacy• Market data capture/replay


Accenture DataStax Alliance Overview

29Company Confidential - Copyright © 2016 Accenture All rights reserved.

Accenture Analytics & Big Data• Extensive capabilities – data access

and reporting• Enhanced modeling• Forecasting and sophisticated

statistical analysis

Deep industry experience

BETTER TOGETHER:

Help organizations scale and leverage their big data to become data-driven enterprises

Go to Market Focus:

• NoSQL solutions and online transaction processing

• Rapid deployment of data for analytics

• Personalization

500Global customers in 45 countries

Fast, scalable distributed database technology, delivering Apache Cassandra to innovative enterprises

DataStax is built to be agile, always-on and predictably scalable to any size

Integral part of the Accenture Analytics alliance and vendor ecosystem of big data technology providers

36,000Accenture Digital professionals

1,300+Data Scientists

23Accenture Innovation Centers including 5 Advanced Analytics Innovation Centers globally

Contacts & ResourcesAccenture:• Barbara White, Accenture Alliance Director [email protected]• Vassili Patrikis, Big Data Lead [email protected]

DataStax:• Brian Borbely, DataStax Alliance Director [email protected] • Matthew Stump, Head of Product Marketing [email protected]

Resources: • Accenture Alliance with DataStax • DataStax Alliance Partnership Press Release

© 2015 DataStax, All Rights Reserved. © 2015 DataStax, All Rights Reserved. 30

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webinar: how to prevent bank fraud & monitor risk in real time

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