© 2019 Jack Henry & Associates, Inc.®1 © 2019 Jack Henry & Associates, Inc.®

Data AnalyticsWhat’s With All the Hype?Jennifer Geis, Sr. Strategic Initiatives AnalystJack Henry & Associates

© 2019 Jack Henry & Associates, Inc.®2

Points of Discussion

01. Who is forming modern analytics solutions and how did we arrive where we are today

WHO AND HOW

02.

03.

Which modern financial service technologies rely on data and where are they being utilized

WHICH AND WHERE

Why financial institutions should establish a data strategy and what to do now

WHY AND WHAT

© 2019 Jack Henry & Associates, Inc.®3

Source: Data Never Sleeps 6.0 Report, Domo, 2018

3,877,140 searches

4,333,560 YouTube videos

$68,493 P2P transactions

3,138,420 GB of internet data

12,986,111 texts

2,083,333 snaps

© 2019 Jack Henry & Associates, Inc.®4

© 2019 Jack Henry & Associates, Inc.®5

Data Builds Amazon’s Payment Ecosystem

SOURCE: AI for an Improved CX, Digital Banking Report, February 2019

© 2019 Jack Henry & Associates, Inc.®6

Factory in China has replaced 90% of its human workers with robotsJune 21, 2019

Robots may replace 800 million workers by 2030December 5, 2018

Breaking News

© 2019 Jack Henry & Associates, Inc.®7

Traditional Data Collection is Insufficient

Manual just-in-time approach:

Consumer-led data harvesting:

Bank-generated sensing/awareness:

Consumer data is added manually

Consumers are passive

Consumer data is harvested from digital footprint

Consumer accepts sharing private data

Harvested data is used to predict what the consumer needs, when they will want it.

Consumer provides prior consent—no formal engagement

© 2019 Jack Henry & Associates, Inc.®8

Modern Methods of Data Collection Emerge

SOURCE: Swimming in Data: Data Lakes in Banking, Celent, June 2019

© 2019 Jack Henry & Associates, Inc.®9

SOURCE: The Power of Advanced Analytics, The Financial Brand, December 2018

© 2019 Jack Henry & Associates, Inc.®10

Innovative Data Collection Needs a Data Lake

a consolidated store of both structured and unstructured data. Provides the flexibility to use the data stored, whatever its format

a common data repository, that breaks down siloes of knowledge throughout any organization

noun

© 2019 Jack Henry & Associates, Inc.®11

Data Lake Data Warehouse

Storage is relatively easy since the data is not pre-processed, stored in its native format.

Accepts structured and unstructured data. Typically a flat data model, but hierarchical

solutions exist. Offers flexibility until the time of use. Multi-tenant capabilities.

Storage is more difficult. User must define schema.

Data must be structured. Data model is complex to design. Purpose must be fully designed prior to

construction. Single tenant only.

© 2019 Jack Henry & Associates, Inc.®12

Points of Discussion

01. Who is forming modern analytics solutions and how did we arrive where we are today

WHO AND HOW

02.

03.

Which modern financial service technologies rely on data and where are they being utilized

WHICH AND WHERE

Why financial institutions should establish a data strategy and what to do now

WHY AND WHAT

© 2019 Jack Henry & Associates, Inc.®13

AI: Broad and Often Misused Term in Financial Services

© 2019 Jack Henry & Associates, Inc.®14

AI Is Increasingly Included in Banking Strategy

SOURCE: AI in Banking, BI Intelligence, June 2019SOURCE: Accenture, Digital Banking Report, September 2018

© 2019 Jack Henry & Associates, Inc.®15

AI Leads the 2019 Top Ten Retail Banking Trends

Source: Retail Banking Trends and Predictions, The Digital Banking Report, December 2018

© 2019 Jack Henry & Associates, Inc.®16

Tangible AI Use Cases Are Being Utilized

SOURCE: AI in Banking, BI Intelligence, June 2019

$447B

Estimated U.S. banking industry cost savings, projected 20% reduction in operating expenses by 2030

Source: Capgemini Financial Services Analysis, 2018

© 2019 Jack Henry & Associates, Inc.®17

SOURCE: AI in Banking, BI Intelligence, June 2019

Significant Cost Savings Can Be Achieved

© 2019 Jack Henry & Associates, Inc.®18

Use Cases in Data Analytics

Churn reductionNext best action

Deepening relationships

Marketing and Sales

Credit risk managementLoan optimization

Credit Risk and Underwriting

ChatbotsPersonalized insights

Mobile optimization

Customer Experience

Transaction monitoringIdentity validation

AML/KYC

Fraud Prevention

© 2019 Jack Henry & Associates, Inc.®19

Fraud Prevention Is the Dominant Use of AI in Payments

Fraud Means: Disruptive customer experience Loss of reputation Incurred fines and loss of revenue

SOURCE: Feedzai Acquirers Playbook, April 2018

© 2019 Jack Henry & Associates, Inc.®20

SOURCE: AI in Banking, BI Intelligence, June 2019

Compliance and Anti-Money Laundering Lead as Most Disruptive

© 2019 Jack Henry & Associates, Inc.®21

RTP Require Real-Time Fraud Prevention

Step one: identity check Step two:

eligibility and fraud risk

Step three: transaction monitoring

SOURCE: Feedzai Acquirer Playbook, 70+ Processes Banks have Already Improved Using AI, Mercator Advisory Group, June 2019

Data prevention/protection

AI in payments fraud: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®22

Financial Regulations Benefit Greatly from AI

SOURCE: HSBC: Fighting Financial Crime with Big Data Analysis, Celent Model Bank Case Study, April 2019

Initiative:Deploy a platform to deliver improved and contextual intelligence to better informfinancial crime investigations.

Solution:• Detection of

potential illicit activity.

• Reduced investigation time.

• Efficiency gains.

• Higher confidence in risk profile.

Global Social Network Analytics (GSNA) Platform

AI in payments fraud: EXAMPLE

AML and KYC

© 2019 Jack Henry & Associates, Inc.®23

Use Cases in Data Analytics

Churn reductionNext best action

Deepening relationships

Marketing and Sales

Credit risk managementLoan optimization

Credit Risk and Underwriting

ChatbotsPersonalized insights

Mobile optimization

Customer Experience

Transaction monitoringIdentity validation

AML/KYC

Fraud Prevention

© 2019 Jack Henry & Associates, Inc.®24

Data Insights in UX Are Difficult to See

SOURCE: The Role Of AI In Customer Experience, Pointillist, June 2019

© 2019 Jack Henry & Associates, Inc.®25

Many Customer Experience Strategies Neglect the End Customer

SOURCE: Digital Banking Transformation Strategies Neglect The Customer Experience, The Financial Brand, May 2019

28%

70%of FI customers say they spend more with banks or credit unions that offer effortless digital experiencesof digital

transformation initiatives start with customer needs as the priority

© 2019 Jack Henry & Associates, Inc.®26

Customer Experience Is the Root of Digital TransformationChatbots and voice assistants

AI in CX: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®27

© 2019 Jack Henry & Associates, Inc.®28

AI Can Generate Insights, Anticipate NeedsPersonalized insights

AI in CX: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®29

AI Is Used to Enhance Customer ExperiencesMobile optimization

AI in CX: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®30

Use Cases in Data Analytics

Churn reductionNext best action

Deepening relationships

Marketing and Sales

Credit risk managementLoan optimization

Credit Risk and Underwriting

ChatbotsPersonalized insights

Mobile optimization

Customer Experience

Transaction monitoringIdentity validation

AML/KYC

Fraud Prevention

© 2019 Jack Henry & Associates, Inc.®31

Confirmed Business Value of Real-time Analytics

Reported significant increases in growth and revenue generation.

Realized a deeper understanding of customer journeys.

Reported significant increases in customer retention & loyalty.

55%54%

74%

SOURCE: Harvard Business Review Analytic Services, February 2018

© 2019 Jack Henry & Associates, Inc.®32

SOURCE: AI for an Improved CX, Digital Banking Report, February 2019

AI in sales and marketing: EXAMPLE

The banking organization of the future will interact in similar ways to Amazon® and Google™, gaining insight and getting smarter with each interaction. ”

”

© 2019 Jack Henry & Associates, Inc.®33

Churn reduction Channel segmentationAI in sales and marketing: EXAMPLE

• Reasons for churn• Competitive positioning• Anticipate and manage

future risk

• Channel underutilization or overutilization

• Best channel routing• Channel agnostics

© 2019 Jack Henry & Associates, Inc.®34

Next best action marketing

Deepening relationships

AI in sales and marketing: EXAMPLE

• Lifestyle habits• Lifecycle changes• Purchases• Deposits

• Product personalization• Reward/loyalty eligibility• Life event prediction• Niche segmentation

© 2019 Jack Henry & Associates, Inc.®35

Use Cases in Data Analytics

Churn reductionNext best action

Deepening relationships

Marketing and Sales

Credit risk managementLoan optimization

Credit Risk and Underwriting

ChatbotsPersonalized insights

Mobile optimization

Customer Experience

Transaction monitoringIdentity validation

AML/KYC

Fraud Prevention

© 2019 Jack Henry & Associates, Inc.®36

Financial Institutions Manage and Comply with Multiple Types of Risk

SOURCE: Robotic Process Automation in Risk and Compliance, Celent, August 2018

© 2019 Jack Henry & Associates, Inc.®37

AI Can Significantly Improve Credit Risk

Traditional problems:

• Small amount of data

• Only structured data

• Slow to determine results

Modern solutions:

• Credit model based on 26B data points

• Any data type

• Extremely fastand reliable

Credit risk management

AI in credit risk and underwriting: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®38

AI Analyzes Commercial Loan Agreements

Contract Intelligence (COiN)

JPMorgan software does in seconds what took lawyers 360,000 hours.

Reduced loan-servicing mistakes often attributable to human error in interpreting 12,000 new contracts/year.

Loan automation

AI in credit risk and underwriting: EXAMPLE

© 2019 Jack Henry & Associates, Inc.®39

Points of Discussion

01. Who is forming modern analytics solutions and how did we arrive where we are today

WHO AND HOW

02.

03.

Which modern financial service technologies rely on data and where are they being utilized

WHICH AND WHERE

Why financial institutions should establish a data strategy and what to do now

WHY AND WHAT

© 2019 Jack Henry & Associates, Inc.®40

Considerations for financial institutions developing an AI strategy

© 2019 Jack Henry & Associates, Inc.®41

Building Blocks for Successful Analytics/AI Implementation

SOURCE: AI in the UI: Adoption, Use Cases, and Business Cases, Celent, May 2019

© 2019 Jack Henry & Associates, Inc.®42

Steps to Establish a Data Strategy

1. Look at what industry leaders are doing with analytics to better understand potential capabilities and opportunities

2. Determine one or two best use cases suited for your FI that match your organization’s business objectives

3. Secure the right talent, build a team with executive support

© 2019 Jack Henry & Associates, Inc.®43

Steps to Establish a Data Strategy

4. Research FinTech partners that can help leverage and customize analytics for your particular needs

5. Access internal processes and infrastructure to enable easy access to, and utilization of, cross-business raw data

6. Ensure that a well-defined strategy and a clear roadmap are in place

Questions?

jgeis@jackhenry.com @JenGeis

https://www.linkedin.com/in/jennifer-geis-5a24a2b/

Read my blogs on JHA’s Strategically Speaking:http://discover.jackhenry.com/strategicallyspeaking/author/jennifer-geis