The Data-Driven Credit Union: Powering Transformation with

Advanced Analytics April 20, 2018

Adoption and execution

Workflow integration

Modeling insights

Dataecosystem

Sourceof value

AdvantEdge AnalyticsAreas of Differentiation

BusinessConsultingServices

Insights Activation

Reporting andPerformanceManagement

PredictiveAnalyticsSolutions

DataManagement

Data Transformation Strategy

Integrated AdvantEdge Analytics E2E Solutions and Services

Areas of differentiation for most analytics and tech vendors

Our Value Proposition and Differentiation

1

Growing Team + Endorsements

Key Endorsements

Growing Team130 + AdvantEdge

employees

25 + Data Scientists20+ Data Engineers

20 + Data Translators

+ 115 clients from Acquisition of

Sequan

Growing Client List

Growing Clients, Team and Endorsements

2

Before we begin – a quick poll

3

Spectrum of Data and Analytics

Advanced Analytics

• Prescriptive AnalyticsWhat is the best next action for us to take?

• Predictive AnalysisWhat will happen next?

10

9

Guided Analytics

• ForecastWhat if these trends continue?

• Statistical AnalysisWhy is it happening?

8

7

Reporting/Self Service

• AlertsWhat are best actions?

• Selective drill downWhere is the problem?

• Ad Hoc Queries How many, how often, where?

• Standard Reports What has happened?

6

5

4

3

Data• Clean Data

• Raw Data21

4

5

30%30%

50%50%

90%90%

What % of all data in the world was created in the last 2 years?What % of all data in the world was created in the last 2 years?

Data and Analytics is a key priority for Credit Unions…

Common business priorities

Grow member base

Deliver a best in-class digital member experiences

Optimize risk and manage losses

Grow number of products per member and wallet share

73%of credit unions see analytics as a way to significantly transform the way they do business

SOURCE: CUNA 2016 credit union member survey on data and analytics 6

…but most credit unions have been unable to drive business value

Have business-driven analytic initiatives26%

Have a comprehensive front-line adoption approach9%

72% Indicate most of their member data is NOT easily accessible

SOURCE: CUNA 2016 credit union member survey on data and analytics 7

The case for change

Driving analytics led transformation

8

Contents

2018 ≠ 1980Costs of data storageand processing

Dataavailability

Math

20181980 1950’s 1980’s 2010’s

DeepLearning A branch of ML

Machine LearningA major approach to realize AI

Artificial Intelligence The science of making intelligent machines

Basic demo-graphic data (e.g.,city, income)

Trans-actions data (e.g., ATMs, mobile-apps)

Gov. agencies (e.g., tax payment report, updated demo-graphic data)

Regular survey / satisfaction data

Callcenter(e.g., customer interaction notes)

Inputs from RMs(e.g., sales logs)

Telcos (e.g., top-up patterns, monthly bill payments)

Wholesalers(e.g., paymenthistory for SMEs)

Utilities (e.g., payment record)

Website navigation data

Video analysis of customer footage

Comments on company’s page / website

Social media sentiment

SOURCE: Dave Evans (April 2011) "The Internet of Things: How the Next Evolution of the Internet Is Changing Everything” 9

Where are the opportunities?Three ways advanced analytics is driving value creation

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Boosting traditional P&L levers• Accelerating growth• Enhancing productivity• Improving risk control Finding new

sources of growth

Delivering the digital bank

Not just FinTechs – the best incumbent credit unions and banks are on the move…

Leading banks are investing

17-20% of their EBIT to support large-scale digital and analytics transformations Redesigning organizations and operating models to

achieve agility and innovation

Building an arsenal of data and analytics capabilities

Radically reshaping key customer journeys

Modernizing their technology infrastructure

11

… focused on driving next generation ofretail banking

SOURCE: McKinsey 12

Call centers

• 50% efficiency gains over next four years

• Powerful source of customer insight for better selling and advice

Bank branches

• 40% efficiency gains over next four years

• Powerful source of customer insight for better selling and advice

Mobile banking

• 70% interactions on mobile over the next four years

Automation

• 70% efficiency gains in the next four years

• Far fewer errors than outsourced workforce

The case for change

Driving analytics led transformation

13

Contents

14

Data gatheringData gathering

Model developmentModel development

Workflow integrationWorkflow integration

Where do most data analytics engagements fail?

Where do most data analytics engagements fail?

Deployment and adoptionDeployment and adoption

Delivering impact requires more than just data and models

“Analytical impact at scale is

• 10% analytics

• 90% end user adoption

Most companies fall short on the latter”

15

Vision and strategy

Organization & Talent

Data

Agile Culture

Modeling tools & techniques

Value assurance

Building a world-class data analytics organization requires focus on 6 key elements

16

Prioritization exercise to assess most impactful analytics use cases and customer journeys and build a roadmap

High

LowLow

Imp

ac

t

Feasibility High

20Prioritized use cases

21

1

26

22719

6

108 2312

3

4 5

7

9

10

11

1314

17

18

19

22

24

25

28 29

Use case prioritization

Feasibility and risk considerations

BusinessWhat economic benefits can be realized (and when)?

Impact considerations

StrategyWhat is the alignment with the business aims and aspirations?

CustomerWhat improvements can be made to customer perception?

DataWhat is the quality of data available and how complex will it be to scale-up data collection and delivery?

CapabilitiesWhere is the organization today relative to aspirations?

Obstacles to changeHow will internal politics/change management perspectives and external environment help or hinder change?

VISION AND STRATEGY

17

Completely decentralised Completely centralisedWith CDO

CDO unit

Data owner units

Several organizational archetypes for analytics

SOURCE: McKinsey Analytics

ORGANIZATION AND TALENT

18

A

B

C

D

Data ScientistData Scientist

Data EngineerData Engineer

Business TranslatorBusiness Translator

What is the most difficult analytics role to hire?

What is the most difficult analytics role to hire?

Workflow IntegratorsWorkflow Integrators

ORGANIZATION AND TALENT

19

Technology skills

Analytics skills

Business skills

DeliveryManagers

B.

Businesstranslator

C.

VisualisationAnalyst

D.

WorkflowIntegrator

E.

Datascientist

F.

Data engineer

G.

Data architect

H.

Business leaders

A.

The most critical talent to find are ‘translators’ who can bridge different functional areas

SOURCE: McKinsey Analytics

ORGANIZATION AND TALENT

20

Customer data

Collateral data

PricingC/C …

BANKING EXAMPLEA strong data foundation is a key enabler

SOURCE: McKinsey Analytics

Governance systems

Legacy systems Customer

dataCollateral

dataPricingC/C …

Data users

Data Warehouse/lake

Risk Accounting …

To parallel modern architectureFrom “spaghetti” architecture

DATA

21

Risk Accounting …

Value leakage in the “last mile” is usually driven by inability to activate the insights appropriately

10

100

Taking Action

Interpreting Insight

Value atStake

Making Decision

OutcomeIncorporating feedback

Monitoring outcome

Value leakage in the “last mile” ($M)

VALUE ASSURANCE

22

An Agile way of working key to bringing multiple skillsets together

SOURCE: McKinsey Analytics

AGILE CULTURE

23

Data Scientist

DataEngineer

DeliveryManager

WorkflowIntegrator

VisualisationAnalyst

Business Translator

DataArchitect

The Data-Driven Credit Union: Powering Transformation with

Advanced Analytics April 20, 2017