Analytics driving innovation in banking through data

Analytics driving innovation and efficiency in Banking
Exceeding performance, mitigating risks and meeting regulations optimizing usage of data
Gianpaolo Zampol | @gzampol
Sao Paulo | March 15th, 2018

2 @gzampol | Analytics driving innovation and efficiency in Banking | March 15th, 2018
Agenda
Point of view on key and emerging topics
Leveraging Analytics to exceed performance and meet regulations
Call to action
Value creation through Data & Analytics with efficiency
Big Data & Analytics in Banking industry
Main trends and challenges in Brazilian market landscape

Agenda
Call to action

Complex inter-related challenges drive competition for
resources into a new business environment
Customers
Savvy, demanding customers
means banks must adapt to new
business models
Competition
New non-traditional
competition for
customers (e.g. retailers,
“GAFA”) migrate profit
pools out of financial
institutions
Complexity and cost
Cost and inefficiencies
hamper profitability,
concentrated on back office
operations, IT infrastructure
and legacy systems
Capital efficiency
Capital remains scarce due to
regulation so risk informed, capital
decisions are a key determinant
Regulation and
governance
Emerging regulations
demand granular and
frequent demonstration of
governance and control,
increasing cost of
compliance
Risk and security
Understand and mitigate
risks and reduce growing
cyber security threats well
remains a challenge
$
Source: Analysis based on The “New Normal” in Retail Banking, BCG, 2012.

Customer-centric outcomes
Operational optimization
Risk / financial management
New business model
Employee collaboration
Other
functional
objectives
Customer-
centric
objectives49%
18%
15%
14%
4%
55%
4%
23%
15%
2% Banking & Financial
Markets
Global
Big Data & Analytics objectives
The majority of efforts are focused on improving customer interactions, followed by better risk
management and counter fraud
Source: The real world use of Big Data, IBM & University of Oxford, 2016; Febraban/Deloitte Research, 2017
47%
of banks are investing in Analytics
24%
started investing in Artificial
Intelligence/Cognitive Computing
96%
grow of customers using Mobile
Banking between 2015 and 2016
Highlights from Brazilian banks

How Brazilian banks are respondingKey insights
▪ Brazilian Tier 1 banks created Chief Data Officer
organizations, establishing data governance to supply
“single sources of true data” to LoBs.
▪ Partnerships with fintechs (e.g. Guia Bolso & Votorantim).
▪ Bank data still sits in internal silos, limiting
competitiveness in the future.
▪ Turn data into insights given regulations as GDPR1,
appealing to ecosystems to access data.
Data as corporate asset,
holistic data governance
and monetize data
▪ Banks created Gestora de Inteligência de Crédito (GIC),
to supply analytics beyond credit bureaus services.
▪ API enabled architectures grow in all banks, supporting
integration with ecosystems and low cost data transfers.
▪ Data expert start ups emerging, giving banks options
to outsource data analysis (e.g. Cardlytics, Experian).
▪ Other banks are sharing their customer data securely
through APIs.
Banks continue to
experiment Data as a
Service (DaaS) models
▪ 75% of local leading banks grew +15% revenue with
advanced analytics2.
▪ CDOs organizations expand their data scientists teams.
▪ RPA migrations projects +60% back office reductions.
▪ Predictive analysis with data science evolve add
value to customers, uncovering behavior patterns.
▪ Advances in RPA can automate and standardize inquiry
of data for precision, reducing error and operating costs.
Analytics advances
continue to help reduce
costs and provide better
customer engagement
Key challenges for Brazilian banks in Big Data & Analytics
▪ Requirements from FRTB/Basel IV, credit risk and better
capital allocation due to local macroeconomic.
▪ Improve AML analytics incorporating unstructured data.
▪ Brazil remain as major market attacked by cybercriminals.
▪ Risk mitigation, capital and regulatory requirements
stay in a high plateau, but continue to drive investments.
▪ Growing digital environment require high focus on
cyber security, fraud detection, KYC, AML.
Increase demands from
risk, security and
regulatory compliance
analytics
▪ Two speed IT in all banks with ‘digital’ departments, but
highly focused on UX, still coexists with old legacy systems.
▪ Open source codes heavily applied (e.g. R, Python),
creating security issues and architecture governance.
▪ Legacy back office and IT infrastructure remains the
largest challenge to transformation. Cloud-based and
API-enabled architectures make viable faster and
cheaper big data exploration and advanced analytics.
Infrastructure technology is
being modernized to
decrease costs and
improve agility
Source: Research and analysis upon Brazilian financial services market; McKinsey articles. 1General Data Protection Regulation (GDPR); 2High Stakes High Rewards, EY, 2017.

Key challenges for Brazilian banks in Big Data & Analytics
Descriptive
Diagnostic
Predictive
Prescriptive
Cognitive
Reports
past events
Assesses
past
outcomes
Identifies
potential
outcomes
Identifies (and
may automate
execution of)
optimal
outcomes
systems
Learning
systems
based on
probabilistic
reasoning
Analytics maturity levels
Source: Analytics: Dawn of the cognitive era, IBM Institute for Business Value, 2016
Increasing analytic maturity is cumulative, incorporating new cognitive capabilities

Predictive combined with Cognitive Analytics
Predictive Analytics Cognitive Analytics
Internal Data Syndicated Data Social Data Device Data
Both capabilities can be woven into every customer interaction to personalize and enhance
the customer experience
Customer
Segmentation
Action
Clustering
Next Best
Action
Personality
Analytics
Sentiment
Analytics
Image
Recognition
Source: Researches on cognitive computing

Agenda
Call to action

Key topics for discussion
Big Data &
Analytics
Importance of Data Lake in
modern architecture in Banking
Risk-Adjusted Performance
Management (RAPM)
Concept, main
components/functions and impacts
on customer service improvement
Fundamental Review of
Trading Book (FRTB)
Definition and impacts in Brazilian
banks capital structure

Big Data &
Analytics
Management (RAPM)
Concept, main
Trading Book (FRTB)

Big Data & Analytics
Data is growing exponentially and became a new ‘natural resource’
Source: IDC DataAge 2015 Study, 2016
Note: 1 petabyte = 1M gigabytes, 1 zetabyte = 1M petabytes
Considerations
▪ ~10% are structured data, mainly
corporate date, stored in traditional
databases.
▪ Despite 90% of unstructured data
are documents, images, movies,
voice recordings, posts, tweets, etc,
most of this percentage are coming
from IoT devices.
▪ Internet giants (Google, Amazon,
Facebook, Apple) are expanding
beyond industry boundaries, with
the power of data (e.g. Apple Pay,
‘Facebook Bank’ in Ireland).
▪ Unlock new insights is imperative
for competitive advantage and
also business continuity.

Big Data & Analytics (cont.)
Regulatory
Pressure
Regulator demand
growing every
year
Data
Management
Ability to manage
data has not kept
pace
Data
Growth
Amount of data
increasing
exponentially
Big Data
Capabilities
Advancements
lower costs and
technical barriers
Business
Pressure
Growth demands
still driving
investment
Impacts and challenges from a ‘data flood’
Data
Storage
Cost to store data
decreased
exponentially,
supported by
cloud computing
Big Data Transformation by managed Data Lake
Source: Researches on Big Data best practices

Three fundamental transformation principles
Manage Big DataGather Big Data Use Big Data
1 2 3
More data is available –
both internal and
external. Technology has
made it easier and cost
effective to gather and
store for business
usage.
Big data strategies
incorporate end to end
data lineage and
governance practices from
source to consumption.
Usage is being
transformed by new data
availability, new analytic
capabilities (e.g.
cognitive, streams) and
organizational priority.
Path to value is
accelerating through new
analytic capabilities and
applications
Technology has
lowered sourcing and
storage barriers
Big Data transformation
programs implement
controls across the data
supply chain

Big Data Transformation Conceptual Architecture
Real Time AnalyticsEnterprise
Data
Analytics
‘At Rest’
Rapid
Ingestion
and
Integration
Managed Data
Lake
Visualization,
Applications and
Traditional
Reporting
Traditional
Repositories
External
Data
Reference &
Master Data

Big Data Transformation Conceptual Architecture
Real Time AnalyticsEnterprise
Data
Analytics
‘At Rest’
Rapid
Ingestion
and
Integration
Managed Data
Lake
Visualization,
Applications and
Traditional
Reporting
Traditional
Repositories
External
Data
Reference &
Master Data
1 - Gather Big Data
2 - Manage Big Data
3 - Use Big Data

Managed Data Lake
Analytics at
Speed
AI
Applica-
tions
Risk and
Compliance
Enabled
New and
Deeper
Insights
Client and
User Expe-
rience
Managed
Data Lake
Cloud
▪ "In broad terms, data lakes are marketed as enterprise wide data
management platforms for analyzing disparate sources of data in
its native format.” (Gartner)
▪ "The idea is simple: instead of placing data in a purpose-built
data store, you move it into a data lake in its original format.
This eliminates the upfront costs of data ingestion, like
transformation. Once data is placed into the lake, it's available
for analysis by everyone in the organization.” (Gartner)
▪ A data lake is a large storage repository and processing engine.
They provide "massive storage for any kind of data, enormous
processing power and the ability to handle virtually limitless
concurrent tasks or jobs”. (Wikipedia)
Features of a
Managed
Data Lake
Definitions
▪ Controlled and managed environment at the heart of modern
Data Transformations.
▪ Enables operating model cost reduction across the data supply
chain: sourcing, modeling, provisioning, analytics.
▪ Speeds analytic insight.
▪ Supports regulatory requirements across data supply chain.
▪ Reduce/Reuse/Recycle/Innovate Model.
Source: Gartner IT Glossary, Wikipedia, researches on Big Data best practices

How a Data Lake drives a better client experience
Profile/Descriptive data
▪ Products and Policies
▪ Goals
▪ Characteristics
▪ Demographics
▪ Self-declared info
Attitudinal data
▪ Opinions
▪ Feedback
▪ Preferences
▪ Aspirations
▪ Expressed / Inferred needs
Behavioral data
▪ Transactions
▪ Payments
▪ Inquiries
▪ Feature Usage
▪ Issues
Interaction data
▪ Browsing / Clickstream
▪ Contact center
▪ In-person dialogue
▪ E-Mail / chat transcripts
▪ Third-parties / Alliances
Accessible
Timely &
Kept Fresh
High-Quality &
Curated
Easily
Integrated
Real-Time +
Enrichment
Simple Sets /
Patterns
Complex
Analytics /
Models
Data Discovery,
Test & Learn
Data Staging
We don’t need to know everything... just the right things for the target experience...
the up-to-the-moment, contextually relevant, actionable “view” of the client
Source: Researches on Big Data and Analytics best practices

▪ Goals
▪ Characteristics
▪ Demographics
Attitudinal data
▪ Opinions
▪ Feedback
▪ Preferences
▪ Aspirations
Behavioral data
▪ Transactions
▪ Payments
▪ Inquiries
▪ Feature Usage
▪ Issues
Interaction data
▪ Contact center
Accessible
Timely &
Kept Fresh
High-Quality &
Curated
Easily
Integrated
Real-Time +
Enrichment
Simple Sets /
Patterns
Complex
Analytics /
Models
Data Discovery,
Test & Learn
Data Staging
Pattern: Real-Time Offers

▪ Goals
▪ Characteristics
▪ Demographics
Attitudinal data
▪ Opinions
▪ Feedback
▪ Preferences
▪ Aspirations
Behavioral data
▪ Transactions
▪ Payments
▪ Inquiries
▪ Feature Usage
▪ Issues
Interaction data
▪ Contact center
Accessible
Timely &
Kept Fresh
High-Quality &
Curated
Easily
Integrated
Real-Time +
Enrichment
Simple Sets /
Patterns
Complex
Analytics /
Models
Data Discovery,
Test & Learn
Data Staging
Pattern: Financial Planning

Big Data &
Analytics
Management (RAPM)
Concept, main
Trading Book (FRTB)

Fundamental Review of Trading Book - FRTB
What is FTRB for?
Why is there a need?
Who is impacted?
Main objectives
▪ Fundamental Review of Trading Book outlines the new minimum
capital requirement for market risk.
▪ FRTB regulation of the banking industry represents the biggest
changes to market risk capital requirements in over a decade.
▪ Mitigate the shortcomings of existing Market Risk capital
requirement regime such as permeable trading book vs. banking
book boundary.
▪ More consistency between approaches and better risk coverage.
▪ All banks with trading book: Tier 1, Tier 2, Tier 3.
▪ Different jurisdictions will implement at different times and “flavors”.
▪ According to QIS*5 (Oct’15), is expected 4.2x increase on regulatory
capital for market risk (current vs. future Standardised Approach).
▪ Main assets and risks: GIRR (General Interest Rate Risk), CSR
(Credit Spread), EQ (Equity), CO (Commodities), FX (Foreign
Exchange), DR (Default Risk).
Source: Bank of International Settlements; Bringing Basel IV into focus, McKinsey, 2018
*QIS – Quantitative Impact Study

Fundamental Review of Trading Book - FRTB (cont.)
Summary of approaches
Standardised Measure Approach (SMA)
A credible fall-back and basis for IMA floor setting
Internal Model Approach (IMA)
Capital relief for risk management sophistication at a cost vs SMA
Basic calculations (+,-,x..) and pricing on a
limited number of stress scenarios
Complex model calculation (Calculation over a
large number of scenario)
Less accurate. Tends to over-estimate risk More accurate: allow netting, diversification...
Higher capital charge Lower capital charge
Relatively easy to implement More difficult to implement
All banks will have to implement Larger/Sophisticated banks
Source: Bank of International Settlements

Implementation timeline
Rules making
Jan 2011 Jan 2016 Mar 2018 Jan 2019 Dec 2019
Monitoring and
recalibration
National rule making
Monitoring and approval process*
Institutional implementation
Finalized
FRTB
standards
Final
national
standards
Latest
first
reporting date
Relevant papers:
▪ Jan 2011 – Messages from academic literature.
▪ Mar 2012 – First consultative paper
▪ Jan 2013 – RCAP, Market RWA
▪ Oct 2013 – Second consultative paper
▪ Dec 2014 – Third consultative paper
Quantitative Impact Studies (QIS):
▪ Apr 2014 – QIS1
▪ Jul 2014 – QIS2
▪ 2015 – QIS3, 4 and 5
Source: Bank of International Settlements
*Following local regulators timeline
Basel IV…

Potential effects on Brazilian banks (Tier 1 and selected Tier 2)
19,5% 19,1%
17,6%
15,2%
16,2%
17,3%
15,3%
14,6%
16,9%
19,2%
18,9%
17,5%
15,1%
15,4%
14,0%
15,0%
14,4%
15,2%
Itau
BB
Bradesco
Caixa
Santander
BTG
Safra
Votorantim
Citibank
Basel Ratio (Current) Basel Ratio (Adjusted)
65%
88%
88%
49%
83%
87%
91%
89%
89%
12%
9%
8%
4%
8%
11%
8%
8%
9%
23%
3%
4%
46%
9%
2%
2%
2%
3%
Citibank
Votorantim
Safra
BTG
Santander
Caixa
Bradesco
BB
Itau
Credit Operational Market
RWA distribution by risk type
Sep’2017
Simulated impact on Base Ratio
Sep’2017, 50% incremental on Market Risk RWA portion
Source: Analysis on Central Bank of Brazil, IF.data, September 2017; QIS 5, BIS, October 2015

The high demands of FRTB requires a fundamental rethink of the risk infrastructure of banks
User Experience
Risk Data Management
Risk Analytics
Trade Data Historical Risk
Factor Data
Current Risk
Factor Data
Portfolio
Sensitivities /
IMM Eligibility
Credit
Hierarchies
Results Data
Internal Model Standardized
Approach
Default Risk
Charge
CVA Risk
Framework
FRTB SA-CVA
Standard Initial
Margin Model
Risk Reporting & Aggregation
Model
Approval
Compliance Risk Factor
Analysis
Configuration Drill-down /
Day-
over-Day
Interactive
What-If
Model
Verification
Front
Office
Strategic
Planning
Middle
Office
Efficient
On demand
Source: Research and Analytics on FRTB and Risk infrastructure, PwC, IBM, McKinsey studies

Big Data &
Analytics
Management (RAPM)
Concept, main
Trading Book (FRTB)

Risk-Adjusted Performance Management – RAPM
Source: Value and Capital Management: A Handbook for the Finance and Risk Functions of Financial Institutions, Thomas C. Wilson, Willey, 2015; EPM best practices.
A best practice Enterprise Performance Management (EPM) planning and allocation process
Risk measurement and capital
attribution
Setting risk appetite
Risk-adjusted
performance measurement
Capital budgeting and
monitoring
Available
capital
100
40
25
35
0%
5%
10%
15%
20%
25%
30%
35%
0% 100%
35
Current
capital requirements
Capital
buffer
Risk
appetite
Performance
measurement
New business
allocation
Corporate lending
Financial markets
Investment banking
Retail
Fin. Mkts
Asset Mgt
Pensions
Pension Funds
…
Share of capital
ROE
Corporate lending
Financial markets
Investment banking
Retail
Other business
…
▪ How much capital is required to cover all risks?
▪ How capital can be distributed to LoBs?
▪ What buffer is needed to protect against
changes in economic conditions?
▪ Is the business plan aligned with risk appetite?
▪ How can the bank understand where it will be
more profitable to allocate capital?
▪ How can the bank can measure returns from an
efficient capital allocation?

Risk-Adjusted Performance Management – RAPM (cont.)
Source: Enterprise Risk Management – Towards shareholder value creation, Oliver Wyman, 2016.
Economic measures of value are designed to align with shareholder interests

Uniquevaluerealized
Regulatory
Compliance1
Dynamic Capital
Analytics2
Strategic Capital
Planning3
Reduce Cost &
Complexity of
responding to new
regulations for capital
adequacy
Measure bank policies and
strategies over a range of
possible changes in
portfolios and key risk
drivers
Maximize Risk Adjusted
Return on Capital (RaRoC)
through forecasting and
optimized capital allocation
Assess capital actions
Reverse stress testing
Recovery planning
ALM & Gap Management
Liquidity
Profitability
Losses
Risk-Adjusted Returns
Capital Optimization
Market Capitalization
Capital Ratios
Aligning regulatory compliance with incremental high value on risk management activities
Source: Research on Enterprise Performance Management and Enterprise Risk Management best practices.

Main componentes in a Finance function reference architecture
Consolidations
Transaction
Sources
Transaction
Systems
Customer
Accounting
Systems
Manual
Input
Financial
Systems
Reporting &
Analysis
Regulatory
Reporting
Financial
Reporting
Management
Reporting
End User
Analytics
Decision
Support
FormattingXML
PublishingPORTAL
XML
Transaction
Services
(ETL)
Data
Acquisition
Data
Enrichment
Edits &
Validation
Error
Reprocess
Controls &
Recon
Aggregation
Transactions
Management Reporting
General Ledger
Financial Ledger
Multi-
Currency
Average
Balances
Close
Multi-
Language
Management Data
EPMFTP
EPMRWC
EPMCost
Transfers
Budgeting
&Planning
Capital
Allocation
Allocations
Thin
Thick
Common Business Data (e.g. Hierarchies, Attributes)
Metadata (e.g. Rules, Validation Tables)
Reconciliation Point

Main componentes in a Finance function reference architecture
Consolidations
Transaction
Sources
Transaction
Systems
Customer
Accounting
Systems
Manual
Input
Financial
Systems
Reporting &
Analysis
Regulatory
Reporting
Financial
Reporting
Management
Reporting
End User
Analytics
Decision
Support
FormattingXML
PublishingPORTAL
XML
Transaction
Services
(ETL)
Data
Acquisition
Data
Enrichment
Edits &
Validation
Error
Reprocess
Controls &
Recon
Aggregation
Transactions
Management Reporting
General Ledger
Financial Ledger
Multi-
Currency
Average
Balances
Close
Multi-
Language
Management Data
EPMFTP
EPMRWC
EPMCost
Transfers
Budgeting
&Planning
Capital
Allocation
Allocations
Thin
Thick
Common Business Data (e.g. Hierarchies, Attributes)
Metadata (e.g. Rules, Validation Tables)
Reconciliation Point
RAPM
Fund Transfer
Pricing
Performance
Management
Performance
Management

Dimensions and
Hierarchies
Samples:
▪ Product
▪ Channel
▪ Segment
▪ Client
▪ Organizational
Unit
(Bank, Region,
Company, Legal
Entity, Division)
Metrics Samples:
▪ Balance (EOP, Avg)
▪ Revenue, Interest/Non Interest Income,
Risk Adjusted Income
▪ Interest/Non Interest Expenses,
Charges, Provisions for Losses
▪ Capital Allocation (Credit, Market..)
▪ Risk Weighted Avg Balance
▪ RAROC
▪ Economic Profit
Key Capabilities
Time Samples:
▪ Monthly
▪ Yearly
▪ Scenarios for
Back testing
▪ Granular calculation and allocation of profitability and
risk adjusted metrics.
▪ Provide multi-dimensional modeling and profitability
analysis.
▪ Views of trail of audit.
▪ Support multicurrency allocations and fund transfer
pricing.
▪ Alignment with Regulatory Capital.
Calculation Engines
Reporting
Modelling
Key components of a RAPM solution
Source: Research on Risk-Adjusted Performance Management Solutions;
Oracle Financial Services Profitability Management (OFSPM) User Guide, 2013

Agenda
Call to action

Approaches for a data-driven transformation
I. Big Data Strategy
II. Perform POC and Define Value Case
IV. Enable Change Management
Program
III. Roadmap and Execution Plan
VI. Define and Launch Organization
V. Implement Governance
Framework
IX. Manage Change and Ongoing
Communications
VII. Establish Environments
X. Mobilize Usage Patterns
XI. Deliver with Agility
Activity Legend:
Baseline Change Management Mobilize
VIII. Business Enablement
Big Data Transformation: Creating a Managed Data Lake Journey
Source: Data Transformation best practices, researches and experience on Big Data and Analytics projects

Approaches for a data-driven transformation (cont.)
Develop Actionable
Use Case & Roadmap
Design the
Solution
Proof the Solution
Assess the
Business
Value
Solutions Deployment
Identify a specific
Business Problem
Client
Success &
Expansion
Briefing
& Vision
Approach
▪ Drive value creation each step.
▪ Accelerate time to value through agile sprints.
▪ Understand and work on financial institution data and
advanced analytics journey.
▪ Focus on solutions based on business problems.
▪ Mitigate risk for cloud-first solution design and
implementation.
▪ Establish business case for making an investment to
advance data-driven transformation.
Uncovering and designing a journey walking through bank needs
Source: Data Transformation best practices; approach developed for previous clients along Data Transformation projects

Thank you
Gianpaolo Zampol
Senior Managing & IT Consultant
Financial Services Sector
gianpaolozampol
@gzampol
gzampol

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