Finance’s Evolving Role: The Importance of Transformation ...

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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


Agenda

AXP Finance Overview and Background

Impetus for Change

Building a Strategy and Roadmap

Executing the Strategy

Learnings To Date

Overview and Background


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


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


Gaps In Other Areas

Feature

Pro

du

ct

Produ

ct

Pro

du

ct

Produ

ct

Pro

du

ct

Produ

ct

Pro

du

ct

Produ

ct

Produ

ct

Pro

du

ct

Feature

Feature

Feature

Feature

Feature

Feature

Feature

Feature

Feature

Feature by Feature Comparison

Lack of Process

Standardization and

Automation

Limited Knowledge of &

Availability of Data

Limited Analytics &

Reporting Tools Usage


Lack of Agility Across Finance

Product Owner

Release Planningvs

Waterfall AgileRequirements

Management

Team

Engagement

Stakeholder

Management

Impetus for Change


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


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

Building the Strategy and Roadmap


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.


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?


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


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

Executing the Strategy


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


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


✓ Accelerate expansion to Finance & formalize engagement model

✓ Expand cognitive automation capabilities

✓ Cognitive automation tools launched

✓ Legacy system decommission


~300 K Hrs. (Cumulative) ~50 K Hrs.Annual

Efficiencies in hours


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


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


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

Learnings to Date


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.


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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