finance’s evolving role: the importance of transformation
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This material contains information that is proprietary and confidential to American Express. It cannot be shared with third parties without American Express’ written consent. AMERICAN EXPRESS PROPRIETARY & CONFIDENTIAL. DO NOT COPY. DO NOT DISTRIBUTE.
Finance’s Evolving Role: The Importance of
Transformation and Digitization to Finance
Scott A. Nitschke; Vice President, Finance Transformation
Karl R. Johnson; Director, Finance Transformation
October 22, 2019
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Agenda
AXP Finance Overview and Background
Impetus for Change
Building a Strategy and Roadmap
Executing the Strategy
Learnings To Date
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Organizational Overview
BU CFO/Decision Support
Corporate Planning
Controllership
Global Tax
Global Treasury
Corporate Development
LOB 1 LOB 2 LOB 3 LOB 4 LOB 5 LOB 6
FINANCE ORGANIZATION LINES OF BUSINESS
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Historical Technology Investment
Accounting & Reporting
Journal Entries
Trial Balance
Balance Sheet
Income Statement
Regulatory Reporting
Focused on the Finance ‘basics’:
• Accounting
• Financial Reporting
• Regulatory ReportingFinancial &
Management
Reporting
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Gaps In Other Areas
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Feature by Feature Comparison
Lack of Process
Standardization and
Automation
Limited Knowledge of &
Availability of Data
Limited Analytics &
Reporting Tools Usage
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Lack of Agility Across Finance
Product Owner
Release Planningvs
Waterfall AgileRequirements
Management
Team
Engagement
Stakeholder
Management
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Reset. Rebound. Return.
2016 • Wind-down of our relationship with
Costco
• Industry-wide pressure on cobrand
economics and merchant fees
• Persistent macroeconomic
headwinds
Accelerate Revenue Growth
Significantly Reduce Expense
Base
Optimize Investments
AXP’s Game Plan
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Introducing . . . Finance Forward!!!
Process Centralization (COEs)
Process Standardization and Automation
Reporting and Metrics Simplification
Data and Technology Roadmap
Become Agile in Mindset and Practice
OBJECTIVES
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Roadmap DevelopmentCommencing in late May/early June 2016, a 12 week effort to: Assess Current Situation, Define
Future State and Develop a Deployment Timeline
Decision Support Center
Org Design
•Which functions can be consolidated & centralized?
•Which functions need to be more closely aligned to the business?
Planning and Forecasting
Process
•Define a standard process for entire enterprise.
•Reduce manual efforts and bespoke BU specific processes.
Investment Optimization
Process
•Define a standard end to end process for entire enterprise.
•Clear up role and responsibility confusion between Finance and other groups.
•Reduce manual efforts.
Metrics & Reporting
•Simplify the reporting and dashboarding.
•Improve the dimensionality.
•Standardize definitions across BU’s.
Application & Technology
Strategy
•Select applications to support standard Planning and Investment Optimization processes.
•Determine the right analytics & dashboarding tools for the process.
Data Strategy
•Define a holistic view of the data required by Finance.
•Leverage existing assets, if possible.
•Design a single source of truth that supports Finance and the enterprise.
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Application/Technology and Data Assessment
We owned most of the tools
we needed, but . . .
• Needed to rethink current
application deployments to fit
standardized processes.
• Need to apply new data sets to
achieve the required
dimensionality.
We needed to educate Finance
on existing capabilities . . .
• Leveraging existing Management
reporting tools to produce better
results
• Data visualization tools exist, we
weren’t using them to the fullest
extent.
AXP’s big data solution presented
Finance with opportunities, but. . .
• How do we take advantage of
big data?
• What expertise does Finance
need in order to do so?
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Data AssessmentAll Finance processes in scope required basically the same core data set in a single view:
The problem? This view didn’t exist! But, we knew what we needed to do
to create it!
Card Member
• Account Tenure• Location Country• Demographic Information• Spend/Non Spend
Product
• Product Type• Issuing Business• Issuing Country
Client
• Name• Segment• Demographic Information• Client Tenure • Client Revenue
Merchant• Name• Industry• Ownership Hierarchy• Demographic Information• Merchant Tenure • Location Country
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Introducing the Finance Organized Data LayerFODL . . . An analytical environment FOR Finance, designed BY Finance to drive enterprise value through
improved data insights.
A unified global view/hierarchy of all AXP cards with data sourced from three main types of platforms:
a) Merchant Submitted Transactions
b) Card Member Billed Transactions
c) Global Clearing Hub Transactions
Leverage the power of big data solution to create algorithms to match the same transaction across all three platform
• Improved profitability analytics with greater dimensionality and accuracy.
• Foundation for machine learning models to predict spend, non-spend trends
• Data source for revamping Management Reporting
• Market specific spend/revenue/expense by product/client/merchant
Uses for the Improved Data in the FODL
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Organizational Design and Process Standardization
Org Design & Operating Model Process Standardization
❖ Established Decision Support
Center of Excellence (DSCOE)
❖ Eliminated multiple handoffs,
duplication of deliverables across
teams
❖ Optimized global locations to
ensure continuity and alignment
with BU CFO’s
❖ Enterprise wide streamlined Planning
processes & methodologies minimizes
redundancies & iterations
❖ Enterprise wide streamlined
Investment Management processes
including integration with Planning
and Performance Management
❖ Implemented enterprise-level
application for P&F and IM processes.
Shifted off of Outlook and Excel
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Robotics and Automation
2016 2017 2018 2019+
2019 FocusPan-Finance
2016 – 2018 FocusControllership
Basic Robotics Process Automation
Cognitive Automation – NLG / NLP / OCR✓ Capability expansion to transform voluminous data and perform complex calculations
✓ 42 projects completed
Enhanced Process Automation ✓ Automation COE inception within Controllership
✓ Focus on automating simple, repetitive manual activities
✓ 12 projects completed
✓ Accelerate expansion to Finance & formalize engagement model
✓ Expand cognitive automation capabilities
✓ Cognitive automation tools launched
✓ Legacy system decommission
✓ 68 projects completed
~300 K Hrs. (Cumulative) ~50 K Hrs.Annual
Efficiencies in hours
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Data Architecture
Deployment of Finance-owned big data
solution providing transparency from
enterprise level aggregations to transactional
details.
Additional Data Dimensions sourced with
greater accuracy
Significantly improved customer and
merchant profitability analytics, based upon
actual customer and merchant behavior
Process integration of Analytical layer and
Management Reporting layer provides results
consistency and transparency
New enterprise view of reference data created
where needed (e.g. global card products).
Leverage existing Business owned reference
data where possible (e.g. Merchant Industry)
Data foundation for machine learning
models to predict spend, non-spend
trends; future investment returns
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Decision Science and Machine Learning
❖ Established data science role across
BU CFO and Corporate Planning orgs
▪ Customer spend and non spend
predictions & forecasting
▪ Data anomaly and error detection
❖ In early stages, role evolving as we
build our expertise and identify
additional opportunities.
Data Scientists Machine Learning
❖ Upskilling Decision Scientists and
others on:
▪ Python
▪ PySpark
▪ ML Studio
▪ ML Methodologies
❖ Leveraging Enterprise ML platform,
with Finance-dedicated space
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Changing Roles and New Responsibilities
To realize the vision and execute on the original strategy, Finance needed non-traditional roles
to represent our needs and partner with Technologies:
• Finance Data Architects
• Finance Data Analysts and Engineers
• Finance Application Developers
• RPA Tool Experts
• Data/Decision Scientists
Additionally, to continue to evolve the organization and realize faster time to market for
Finance solutions, Finance adopted Agile-related roles to continue to partner with Technologies
while taking control of our own destiny:
• Product Owners
• Product Managers
• Dashboard and Visualization Developers
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Uncovering the UnknownsWhen moving from disparate data sources and localized data transformation processes, you can
expect to uncover many unknowns when trying to centralize the data.
The longer a process has been around, the more likely there is to be ‘hidden’ transformations and
modifications within the process.
Leveraging some of the Agile principles will help guide you through this discovery process:
• Deliver working versions of the data as early as possible without trying to solution every nuance in initial delivery.
• Avoid spending excessive time trying to gather requirements to uncover the unknowns. These will come out much more
quickly through validation of real data.
• Ensure leaders understand the iterative approach and don’t expect perfect results in initial versions.
• Encourage interaction between the developers and data experts during data validation to drive learning on both sides.
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Things to Keep in Mind When Undergoing a
Transformation Journey❖ A truly transformational initiative is a journey of self-discovery. You will encounter processes which nobody
understands why they were designed the way they were. Take the time to figure out the ‘why’ before you
determine the ‘how’ to transform.
❖ Agile is a MINDSET, not a methodology. Sometimes you just need to do what makes sense . . .
For the problem
For the process
For your people
❖ Hire smart people with a transformational mindset, a willingness to learn and make decisions. Sometimes
those people will have a Technical background, sometimes a Finance background, sometimes a different
background. You’ll need them all!
❖ Don’t strive for perfection immediately . . . create a minimum viable product and then iterate, iterate, iterate
❖ Remember, the problem you’re trying to solve wasn’t created overnight . . . and it won’t be fixed overnight
either!
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