Jennifer Geis, Sr. Strategic Initiatives Analyst Jack Henry & … · 2019. 9. 6. · 1© 2019 Jack...
Transcript of Jennifer Geis, Sr. Strategic Initiatives Analyst Jack Henry & … · 2019. 9. 6. · 1© 2019 Jack...
© 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
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Data Builds Amazon’s Payment Ecosystem
SOURCE: AI for an Improved CX, Digital Banking Report, February 2019
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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
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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
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Modern Methods of Data Collection Emerge
SOURCE: Swimming in Data: Data Lakes in Banking, Celent, June 2019
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SOURCE: The Power of Advanced Analytics, The Financial Brand, December 2018
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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
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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.
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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
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AI Is Increasingly Included in Banking Strategy
SOURCE: AI in Banking, BI Intelligence, June 2019SOURCE: Accenture, Digital Banking Report, September 2018
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AI Leads the 2019 Top Ten Retail Banking Trends
Source: Retail Banking Trends and Predictions, The Digital Banking Report, December 2018
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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
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SOURCE: AI in Banking, BI Intelligence, June 2019
Significant Cost Savings Can Be Achieved
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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
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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
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SOURCE: AI in Banking, BI Intelligence, June 2019
Compliance and Anti-Money Laundering Lead as Most Disruptive
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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
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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
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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
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Data Insights in UX Are Difficult to See
SOURCE: The Role Of AI In Customer Experience, Pointillist, June 2019
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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
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Customer Experience Is the Root of Digital TransformationChatbots and voice assistants
AI in CX: EXAMPLE
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AI Can Generate Insights, Anticipate NeedsPersonalized insights
AI in CX: EXAMPLE
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AI Is Used to Enhance Customer ExperiencesMobile optimization
AI in CX: EXAMPLE
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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
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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
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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. ”
”
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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
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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
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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
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Financial Institutions Manage and Comply with Multiple Types of Risk
SOURCE: Robotic Process Automation in Risk and Compliance, Celent, August 2018
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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
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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
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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
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Building Blocks for Successful Analytics/AI Implementation
SOURCE: AI in the UI: Adoption, Use Cases, and Business Cases, Celent, May 2019
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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
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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?
[email protected] @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