The Big Data Revolution: The Next Generation of Finance

Finance & Risk Services
The Big Data Revolution:
The Next Generation of Finance
May 2016

Agenda
2Copyright © 2016 Accenture All rights reserved.
Big Data revolution and financial services industry trends
Big Data - opportunity for CFOs to play a greater role
The transformation journey to become a Big Data-driven bank
The value at stake for the CFO

More data More relevant data
Real-time streaming dataDifferent types of data
From… …To
Terabyte
≈ 1,000
Gigabyte
≈ 1,000,000,000,000
Gigabyte
3
The Big Data revolution means access to large volumes of new data:
It’s time for organizations to capture the huge value hidden in this data
Big Data: What does it really mean?
Copyright © 2016 Accenture All rights reserved.
Zettabyte Mountain of data Useful insights
Business/structured data Unstructured data
Monthly-based reporting
Fixed frequency data input Continuous flowing data input
…ToFrom…
Real-time reporting
From… …To …ToFrom…
Interpretable Uninterpretable
Relevant Irrelevant
Information
The 4Vs
model
t+30
Live
update
TXT
Twitter Facebook
Insights
(novel info)
Signal
(relevant info)
TXT

4
According to recent surveys and market evidence, the Financial Services industry
is increasingly embracing the Big Data revolution with the opportunity it presents
to reinvent the space
How Big Data is affecting the Financial Services (FS) industry
71%
of surveyed FS industry firms are
exploring Big Data and predictive analytics1
$6.4 billion
Big Data investment in 2015
by FS firms5
+26%
Increase in Big Data spending
estimated for 2015-20196
70%
of FS industry firms surveyed report that Big
Data is of critical importance to their firms3
54%
of surveyed FS firms have appointed a
Chief Data Officer to their organizations4
+201%
Total investment growth (2014 vs 2013) in
new Technology firms (Fintech)7
$12 billion
Global Fintech financing activity in 20158
60%
of banks worldwide expect to process
the majority of their transactions in
cloud by 20162
How is the market moving?What is the market sentiment?
How much is this bet?How widespread is the use of Big Data?

Agenda

Discovery of new business opportunitiesData-driven decision making
6
Risk and regulatory managementEnhanced productivity and efficiency
The adoption of advanced technologies and capabilities is a key enabler to new
and important opportunities for CFOs to expand their strategic role
The potential value behind Big Data adoption
The timely availability of large amounts and different
types of data allows decision-making processes based
on data rather than intuition.
New technologies allow the automation of
manual business processes and the handling of
large volumes of unstructured data at lower costs.
New solutions to extract valuable insights and facilitate
the discovery of business opportunities, enabling CFOs
to expand their role as trusted advisor to the CEO.
Agile infrastructures and processes able to manage
what is required now, and what is likely to be required
in the future by the Regulator.
Opportunities
for CFOs to
play a larger
strategic role
Cost
Reduction
Revenue
Growth
Insights
Discovery
Data
Monetization
Strategic
Decisions
Investment
Choices
Process
Automation
Low Storage
Costs
High
Scalability
CFO/CRO
Integration
Real-time
Simulations
Regulatory
Reporting

Discovery of new business opportunitiesData-driven decision making
7
The Big Data revolution will help CFOs use real-time and valuable insights to
improve decisions making and support their role as a strategic partner
Data-driven decision making and discovery of new business opportunities
CFOs now have the opportunity to use insights
generated from data to make decisions with the
aim of enhancing revenue growth and driving
cost reductions.
CFOs are now positioned to unlock the power of Big
Data and take on the role of trusted advisor to the
CEO, providing ideas, guidance and perspectives for
developing new business opportunities that create
addition value.
Big Data and advanced analytics help improve data
management processes through the elaboration and
analysis of large amounts of data. They also provide
real-time information and useful insights that allow
CFOs to make faster and better decisions based on
data rather than intuition.
Data Storage Data Elaboration Data Analysis Decision Making
Huge volume
and different
types of data
Generation of
combined
information
Data
visualization
techniques
Real-time data
and insights
discovery
Big Data and digital technology help CFOs shift from
business operator activities (budgeting, forecasting,
performance monitoring) to a C-levels advisor role
(insights for supporting strategic decisions, analysis of
investments choices, new business opportunities).
Big Data Management
CFO Business Role
CFOCapabilities
Business Advisor
Strong industry
knowledge to act
as business leader
Business Operator
Monitoring skills,
reporting activities
Strategic Partner
Analytics
capabilities to
drive decisions
and create new
opportunities

8
Big Data techniques allow CFOs to gain useful information at a lower cost,
particularly in regulatory reporting
Enhanced productivity, efficiency and risk and regulatory management
Copyright © 2016 Accenture All rights reserved. Note: A legend describing the acronyms can be found at the back to the presentation
Risk and regulatory managementEnhanced productivity and efficiency
New architectures, moving from silo solutions to “data
lakes” and using Big Data technologies can help
generate important cost advantages due to a higher
level of scalability and large volumes of data managed
at a lower cost per unit.
Each year, banks are confronted with new regulatory
requirements and challenges. Big Data allows
organizations a simplified and cost effective way
to source their data and convert it into “usable
information for regulatory reporting.”
Data
Sources
2015 2016 2017 2018
LowMediumHigh
Enforcement Date
ExpectedImpact
236
(cap 8)
SSM SREP
RDARRInternet
Payments
EMIR
SRM
Transparency
Bankit
CRS
PRIPs
CCD
Supervisory
Reporting EBA Payment
Account Directive
MAD II
MIFID (Mkts)
MIFID
(Inv. Prot.)
FRTB
AnaCredit
AML IV Reg. EU
Privacy
PSD II
IFRS9
Liikanen
236
CFOs now have the opportunity to reduce costs
related to data elaboration by using automated and
sophisticated analytical tools that store and analyze
large amounts of data faster and more easily.
CFOs are now better positioned to respond quickly to
regulator requests due to the availability of Big Data
and advanced technologies that allow real-time
simulations and scenario analysis.
Big Data
Query
Machine
Learning
Data
Enrichment
In-Memory
Technologies
Fast Access
Database
Real-time Use
(Mobile, Web,..)
Analytical Use
(Report, Dashboards,..)
Data Lake (Big Data Platform)

Agenda

10
The transformation to a Big Data-driven enterprise should address three key
strategic imperatives
The transformation journey to becoming a Big Data-driven bank
A new data operating model based
on the central role of data in
decision-making processes and day-
to-day activities is recommended.
The model will define new roles and
responsibilities related to the data
governance processes.
Advanced technologies and
flexible IT architectures that can
store and analyze huge amounts of
different types of data are
recommended. These help extract
useful insights and generate value
across the enterprise.
A cultural change is encouraged
in terms of innovative mindset,
new business roles and advanced
skills across the organization. This
will help capture and understand
the real business value behind the
adoption of Big Data.
1. Data Operating
Model
2. Leading-Edge
Architecture
3. Cultural
Change
Strategic imperatives to becoming
a Big Data-driven enterprise

The roles within the data operating model include the data officer as a leader
The data operating model
Data Governance Coordinator
• Coordinates and meets regularly with
all governance function players
• Oversees the reporting process and
makes sure involved players use the
appropriate data sources,
methodology and tools
Data Quality Steward
• Writes data quality standards for the
entire data management process and
defines the data dictionary
• Manages the data quality process for
reporting: coordinates, monitors and
addresses the corrective measures
according to the data quality policy
Data Owner (Business)
• Defines the business’s data requirements
for proper and effective reporting
• Defines (with the Data Quality Steward)
the data checkup activities in the
reporting development process and makes
sure the activities are performed correctly
Data Manager
• Makes sure that data required
for reporting purposes (regulatory
and managerial) are always available
and reliable
• Oversees the quality of data used for
reporting purposes and takes action
to solve potential issues
Designs macro data governance processes, assigning roles,
responsibilities and accountable for the reliability of the
data used for regulatory and financial reporting purposes
Data Officer
Data
Officer
11

A Big Data IT architecture based on different data layers offers greater flexibility,
scalability and data exploration
The leading-edge architecture
Presentation
Users view reports, through dashboards, that provide
data as support for business decisions
Management
Data are elaborated, aggregated, enriched through
specific algorithms and engines to provide synthetic
measures and results
Data Lake
Data from legacy systems are collected in order to allow
full flexibility from this point on in terms of data analysis,
data quality and data elaboration
Legacy and Source Systems
Data sources for the banks, contain and manage data
required for core banking activities and represent the
point of origin of the information
Data Discovery and Exploration
Users access, browse, explore and analyze data in all
the different stages and aggregation levels
Legacy 1
Legacy 4
Legacy 2
Legacy 5
Legacy 3
Legacy n
DataGovernanceandDataQuality
DataLayers
Management
Data Lake
Presentation
Data Discovery
Legacy
Systems
• Engines
• Datawarehouse
• Tableau de Bord / Dashboard
• Reporting
• Self Business Intelligence
12

Cultural change in terms of innovative mindset, new business roles and advanced
skills is encouraged throughout the organization
The cultural change
Key Elements
for a Cultural
Change
Change in senior management mindset is encouraged
to promote:
• Innovation diffusion
• Evidence and data-based decisions
• Convergence of data and sharing of information
• Business accountability for data management
New business roles should be established with the
responsibility to:
• Define and execute data strategy
• Identify and manage data and design data quality controls
• Manage the reliability and traceability of business data
• Define data accountability
New skills and capabilities should be acquired, such as:
• Data scientists
• Quantitative analysts
• IT architects and big data specialists
Innovative
Mindset
New
Business
Roles
Advanced
Skills and
Capabilities
13

Agenda

15
Big Data can enable tangible savings and potential intangible up-side
transforming a traditional CFO department into Data-Driven CFO organization
From issue To outcome
Poor data quality
and technology
inefficiency
Today
-5%
-10%
-20%
Non-harmonized
reporting Non-aligned
processes
Alignment of
processes
Harmonized
reporting
Centralized data
sources
Traditional CFO Department The Data-Driven CFO Organization
+30%
+50%
+15%
+5%
Cost of inefficiency
Cost Reductions
-35%
Further Up-side
Data-driven
decision making
-
Discovery of new
business
opportunities
Issue Outcome
The impact of growing business constraints and regulatory
requirements often result in greater inefficiency and costs. Poor
data quality, outdated technology and misaligned data processing
can lead to inconsistent information and greater difficulty in
executing strategy. Based on our experience, such a situation
could lead to significant cost increases (up to 50%).
Our experience indicates that high-performance CFO
organizations can benefit from significant and tangible cost
reductions (up to 35%) and potential intangible up-side. A new
operating model to manage data (based on the alignment of data
processes, high standards in data quality, new technologies,
harmonized reporting and new analytics capabilities) can help
sustain profitability and return on equity.
Tangible Costs Tangible Cost Reductions Intangible Benefits Tomorrow

Accenture Point of View:
– Exploring Next Generation Financial Services: The Big Data Revolution
• https://www.accenture.com/us-en/insight/big-data-revolution-next-generation-financial-
services
For More Perspectives on Big Data and
Financial Services

Legend
• 236: Internal control systems for Italian banks
• 236 (8): Information security for Italian banks
• AML IV: Anti-Money Laundering IV
• AnaCredit: Analytical Credit Datasets project
• CCD: Consumer Credit Directive
• CRS: Common Reporting Standards
• EMIR: European Market Infrastructure Regulation
• FRTB: Fundamental Review of Trading Book
• IFRS 9: International Financial Reporting Standard 9
• Liikanen: Liikanen Report
• MAD II: Market Abuse Directive II
• MIFID: Markets in Financial Instruments Directive
• PRIPS: Packaged Retail Investment Products
• PSD II: Payment Services Directive II
• Reg. EU Privacy: Protection of personal data
• RDARR: Risk Data Aggregation and Risk Reporting
• SREP: Supervisory Review and Evaluation Process
• SRM: Single Resolution Mechanism
• SSM: Single Supervisory Mechanism
• Supervisory Reporting EBA (European Banking
Authority): New technical standards for
regulatory reporting
• Transparency Bankit: Transparency exercise on
RWA composition

References
1. “Just Using Big Data Isn’t Enough Anymore,” Harvard Business Review, February 9, 2016. Access at:
https://hbr.org/2016/02/just-using-big-data-isnt-enough-anymore
2. “Gartner: Majority of Banks Will Turn to Cloud for Processing Transactions by 2016,” Information Week, Bank
Systems & Technology, October 16, 2013. Access at: http://www.banktech.com/management-
strategies/gartner-majority-of-banks-will-turn-to-cloud-for-processing-transactions-by-2016/d/d-id/1296641
3. “Just Using Big Data Isn’t Enough Anymore,” Harvard Business Review, February 9, 2016. Access at:
https://hbr.org/2016/02/just-using-big-data-isnt-enough-anymore
4. Ibid
5. “What is Big Data,” HRBoss Blog, infographic, February 3, 2014. Access at: https://hrboss.com/blog/2014-02-
03/what-big-data-infographic
6. ”Global Big Data IT Spending in Financial Sector – Market Research 2015-2019,” Technavio. Access at:
http://www.technavio.com/report/global-big-data-it-spending-in-financial-sector-market-research-2015-2019
7. “The Future of FinTech and Banking,” Accenture 2015. Access at: https://www.accenture.com/us-en/insight-
future-fintech-banking
8. Ibid

The Big Data Revolution:
The Next Generation of Finance
Disclaimer
This presentation is intended for general informational
purposes only and does not take into account the
reader’s specific circumstances, and may not reflect the
most current developments. Accenture disclaims, to the
fullest extent permitted by applicable law, any and all
liability for the accuracy and completeness of the
information in this presentation and for any acts or
omissions made based on such information.
Accenture does not provide legal, regulatory, audit,
or tax advice. Readers are responsible for obtaining
such advice from their own legal counsel or other
licensed professionals.
About Accenture
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The Big Data Revolution: The Next Generation of Finance

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