AI, RPA, Analytics & Banks - Shawn Stewart Partner, Advisory Grant Thornton Cost Effective and Practical uses for - Western Bankers Association
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AI, RPA, Analytics & Banks
Cost Effective and Practical uses for
Transformational Technologies in Community/Regional Banks
Shawn Stewart
Partner, Advisory
Grant Thornton
Presenting Today
Grant Thornton
Financial Services
Los Angles, California
Shawn Stewart
Partner, Advisory Shawn.Stewart@us.gt.com
Grant Thornton 310.266.6502
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 2
Agenda
Learning Objective
• Discuss trends and transformational technologies that are
positioned to impact our industry
• Identify and understand the relative market positions and state
of leading products/solutions
• Explore practical and cost effective opportunities where your
bank can utilize these advances in technology to better support
your operations and business
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 3
"Technology is anything that wasn’t
around when you were born."
- Alan Kay (Computer Scientist) -
Trends & Transformational Technologies
The Banking industry is changing…
Traditional Banks Banks of the Future
Baby boomers Millennials
Branches Mobile & Social
Face-to-face Digital
Product focus Customer centric
Common (products) Personalized (solutions)
Process driven Agile
Change averse Fast evolving customer needs and Innovative
technology capabilities are forcing banks to
Silos Collaborative
transform the way they do business
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 5
…and the Digital imperative is at the core of most of the disruption
The Four Pillars of Digital Transformation in FS The branch of the future
1. Reinvent the consumer journey
2. Leverage the power of data
3. Redefine the operating model
4. Build a digital driven organization
Digital disruption in Financial Services
Digital will
Digital is a high Most transactions are now made through On Line Banking,
fundamentally change
priority, but banking Customers go to a branch to seek for advice and expertise,
the economics and
providers are moving
competitive landscape usually in relation with a project,
at different speeds
in corporate banking
The mission, number, location, technology of the branches
86% 10%
need to evolve,
75% 80% 85% 90% 95% 100% The evolution of the branch personnel’s skillset is also a
major issue for Retail Banks,
Strongly agree Neutral Disagree or strongly disagree
Digital also leads to the disintermediation of a number of
banking activities and is challenging future revenue streams.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 6
Data Analytics
Data analytics is the quantitative and qualitative science of
drawing insights from raw information sources.
Many of the techniques and processes of data analytics have been automated
into mechanical processes and algorithms that work over raw data for human
consumption. Data analytics techniques can reveal trends and metrics that
would otherwise be lost in the mass of information. This information can
then be used to optimize processes to increase the overall efficiency of a
business or system.
• Descriptive Analytics: What has happened over a period of time
• Predictive Analytics: What is likely to happen
• Prescriptive Analytics: What should happen (suggesting a course of action)
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 7
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) – software robot that
is programmed to do basic tasks across applications
just as human workers do; performs repetitive tasks more
quickly, accurately, and consistently than a human can.
Designed to reduce the burden of repetitive, high volume
simple tasks on employees.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 8
AI
Machine Learning (ML) &
Artificial Intelligence (AI) ML
Artificial Intelligence (AI) and Machine Learning (ML) –
computer programs that have the ability to learn and adapt
to new data without human interference; these computer
programs in more advanced AI states have the ability to
rationalize and take actions to achieve a specific goal or
outcome.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 9
Blockchain
At its most basic level, blockchain is literally just a chain of blocks, but not in
the traditional sense… digital information (the “block”) stored in a public
database (the “chain”). Blocks on the blockchain are made up of digital pieces
of information. Specifically, they have three parts:
1. Blocks store information about transactions, say the date, time, and dollar amount of
your most recent purchase
2. Blocks store information about who is participating in transactions. Instead of using
your actual name, your purchase is recorded without any identifying information
using a unique “digital signature,” sort of like a username.
3. Blocks store information that distinguishes them from other blocks. Much like you
and I have names to distinguish us from one another, each block stores a unique
code called a “hash” that allows us to tell it apart from every other block.
Blocks are also encrypted and transactions are verified and stored in multiple locations
Source: Investopedia
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 10Blockchain
A Simplified View
Each block has a unique
hash and once entered into
the chain it also contains
the hash of the record
before it.
Each block can store up to
1 MB of info and several
transactions.
identical copies of the
blockchain are stored on up
to millions of computers
Facilitated by private and
public keys
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 11
Source: Financial TimesFinancial Technology (FinTech)
Historically referred to technology employed in the back-end systems of
established financial institutions (old definition)
New technology that seeks to improve and automate the delivery and use of
financial services and banking. At its core, FinTech is utilized to help companies,
business owners and consumers better manage their financial operations,
processes and lives by utilizing specialized software and algorithms that are
used on computers and mobile devices.
Can apply to any innovation in how people transact business, from the invention
of digital money to consuming financial services over your smart phone.
May also refer to industry of companies who produce innovative and disruptive
technologies for financial services.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 12What are some of the major disruptive
technologies that we will focus on today?
• Data Analytics / Prescriptive Analytics – Mathematical or computational
analytics. Think of this as "most of what is out there now"
• Robotic Process Automation (RPA, or "Bots") – Think of them as
"macros" for your desktop with a bit more logic built in
• Machine Learning (ML) & Artificial Intelligence (AI) – The ability of
machines to create associations where none existed. Think of this as
"teaching a computer to identify patterns, after giving it some basic
instructions" (ML) so that the system can rationalize and take action (AI).
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 13What they are not (at least not yet):
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 14Example: Prescriptive analytics
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 15Example: Robotic Process Automation
Process Candidate Brief Description of Process to be Process Value/Outcomes (% Process Candidate Brief Description of Process to be Process Value/Outcomes
# (Name) Automated How Automatable (%) savings) Pri.
# (Name) Automated How Automatable (%) (% savings) Pri.
1
1
2
2
3
4 3
5 4
6 5
7 6
8
9
10
Activity 1: Fill in
7
8 Activity 2: Edit
9
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12 these columns 10 this field
13 11
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Activity 3:
Prepare this Table
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 16Example: Artificial Intelligence
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 17“You never change things by fighting
the existing reality. To change
something, build a new model that
makes the existing model obsolete.”
- Buckminster Fuller (Inventor & Futurist) -
Relative Market Positions & State of
Leading Products/SolutionsDisruptive Technologies
Our Perspective
“Digitized
Start of the “serious” Cloud Infrastructure and Enterprise”
Applications
disruption window Robotic
(size = impact potential) Processing
Chatbots
Pay attention, start planning Natural Language Processing
Cyber
defense
Machine Learning
Artificial
Our discussion today Intelligence
Virtual
Blockchain Reality
"Big Data" Augmented Reality
Predictive &
Prescriptive Analytics
“Manual
Enterprise"
2017 18 19 20 21 22 23 24 2025
© 2017 Grant Thornton LLP. All rights reserved.
4Business Value & Analytic Maturity
Help move your institution from beginners to leaders / disruptors
Level 5
Disruptors
Level 4 - Drives new business
models
Leaders - Aggressively
Level 3 - Sophisticated data
embraces disruptive
Doers science and analytics
potential of analytics,
digital, and emerging
- Specific analytics tool team technologies
Level 2
Maturity
- Dedicated team - Streamlined data and - Disruptive
Thinkers - Integrated quality data defined analytics technologies
Level 1 - Desire to explore & do
- Regular usage of - Continuous monitoring effectively integrated
Beginners more
analytics processes into the strategy of the
- Automation for key - Automation used as a bank and driving a
- Ad-hoc analytics - Excel-based analytics
areas common solution to strategic advantage
- No dedicated team - 1- 3 member team (often
advance operations
- Disparate data wearing multiple hats)
and manage the
- No to very low insights - Low quality data
business
- Limited coverage
Undisciplined Disciplined Dynamic
Business Value © 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 20Cloud
Infrastructure
as a Service
Agility & Reduced Cost
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 21Data Integration
Tools
Efficiencies
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 22Analytics &
Business
Intelligence
Platforms
Actionable Insights
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 23Data Science &
Machine Learning
Platforms
Enhanced Capabilities
Beyond Human Cognition
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 24Robotic Process
Automation (RPA)
• Rapid & Accurate Processing
• Cost Savings
• Quality
• Ability to reassign human
resources to higher use tasks
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 25We are stuck with technology when what
we really want is just stuff that works.”
- Douglas Adams (Author) -
Explore Practical & Cost Effective OpportunitiesPrimary
StrategyValue Drivers
& Execution
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 27Primary Value Drivers
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 28Phased Approach
Explore & Define Collect, Prepare & Model & Evaluate Realize Value,
Transform Data Consume &
Use Case Ideas Generate Insights
Prioritized Use Cases
Profile Use Cases
Assess Impact
Prioritize
Evaluated Use Cases
Planned Projects
Delivered Projects
Capabilities throughout the Engagement:
• Strategy and visioning
• Analytic expertise
• Change management
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 17"Technology presumes there’s just one
right way to do things and there never is."
- Robert M. Pirsig (Writer) -
Use Cases:
Areas for SuccessData Analytics – Why it Matters
Data holds insight, but it is people—not data—who ensure that analytics
generates value for the company.
• Advances in technology are raising expectations for leadership,
creating new needs, and transforming the way we do business
• Analytics is becoming a central focus of leadership agendas
because of its potential to improve profitability, mitigate risk, and
ensure a sustainable organization
• 92% of leaders understand the value of integrating enterprise-
wide data analytics; however
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 31Accounts Payable
Accounts Payable
Analytics and Recovery
Grant Thornton can directly impact the bottom line
of our clients by recovering funds (usually in the
form of vendor credits) for erroneous payment. This
service also is one of the few that can be performed
for contingency fee with appropriate management
approvals.
We use analytics tools that risk rank vendors and
transactions related to invoices and payments. We
also identify outlier transactions for further
investigation to identify unusual, duplicate or
suspicious activity and vendors, and determine cost
recovery potential for duplicate payments. Upon
investigation and confirmation that payments may
be erroneous, we help clients recover those funds
through ongoing communications.
32
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International LtdSpend Analysis
Spending Analytics
Grant Thornton is able to help find recoveries
($$) for our client, clean up their vendor
master, provide insightful analytics, and help
our clients avoid making overpayments to
vendors.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 33Machine Learning Use Cases
1 Fraud Detection 7 Lifetime Value Prediction
2 Risk Modeling 8 Managing Customer Data
3 Controls Automation 9 Customer Segmentation
4 Compliance Monitoring 10 Personalized Marketing
5 Internal Audit Test Automation 11 Real-time & Predictive Analytics
6 SOD & Security Monitoring 12 Recommendation engines
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 34HAVE YOU HEARD THE NEWS?
AI adoption is exploding…
By 2020: – 85% of CIOs will be piloting AI programs through a To realize business value, AI technologies must be deployed
combination of buy, build and outsource efforts.1 to deliver specific, measureable business outcomes for
– AI technologies will be virtually pervasive in almost targeted use cases.1
every new software product and service.1
By 2021: – The term AI will no longer be considered a
AI will enable you to reduce costs. But its greater impact will
be in answering questions such as, "How do I change the
differentiator in market tech provider solutions.1
nature of the customer experience?" or "How can I initiate
– The dominant source of AI business value will
AI-driven insights to alter all levels of decision-making?"1
be new revenue.1
By 2022: – 40% of customer-facing employees and AI and the CEO: Why Every Company Must Become an AI Company3
government workers will consult an AI-powered
virtual agent every day for decision-making or
process-related support.1
Today: – Tech Giants Are Paying Huge Salaries for Scarce AI Talent:
Nearly all big tech companies have an AI project, and they are willing
to pay experts millions of dollars to help get it done.2
1 Gartner | 2 The New York Times | 3 Forbes Article
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 35Compliance Confidence
Recent reports underscore the opportunity for innovation and analytics within the compliance function
Only
27% of Legal and Compliance executives are confident in
their programs’ ability to manage risk
Top risk areas for legal and compliance executives: By 2020, 85% of CIOs will be piloting artificial
intelligence (AI) programs
Legal and compliance leaders can act to help the organization make smart
decisions by:
• Evaluating controls in newly automated areas to ensure risks are
appropriately managed
• Creating experiences to ensure that legal and compliance staff are exposed
to AI and automation
• Tracking developing regulations that impact emerging technologies.
• Updating existing legal and compliance risk assessments and sensing
mechanisms
• Coordinating with other assurance functions to gauge the adequacy of policies,
communication, and training, and make ongoing improvements
Source: Gartner, Legal and Compliance Hot Spots Report (2018) Source: Gartner Data & Analytics Summit press release (February, 2018)
© 2017 Grant Thornton LLP. All rights reserved.
2Fraud Monitoring
Fraud Monitoring
We help organizations prepare fraud risk analytics to support SAS 99
fraud testing and compliance. This is a shift to a risk-based audit
planning and testing approach, and fraud risk analytics is an essential
component. We use fraud risk analytics to identify:
>> Transactions with risk characteristics present related to financial
reporting fraud, restatement and material weakness in controls
>> Journal entries with descriptions that match potential fraud
keywords - and client or industry specific keywords
>> High risk account pairings and account combinations with a
material impact on financials
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 37The Core Compliance Problem: Compliance is
too expensive and provides little business value
Wasted Time
For every hour an auditor spends, the client spends 8 to 12 hours for
that same audit
High Overhead
SOX adds up to 15% overhead to finance and IT staff for those
companies that wish to be compliant
Poor Value
Clients perceive very little value in testing, yet it still consumes more
than 50% of an auditors time
Lack of Staff Career Trajectory
Our staff quickly realize that testing performed is not valued and that
newer technologies can address this work more efficiently
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 38Changing the nature of risk management
from risk mitigation to value protection
Time savings of 95%+ per control
With Manual Assessment With Automatic Monitoring
8 hours per control Automated control monitoring using advanceMachine Learning
Use Case
Banks are obliged to collect, analyze, and store massive
amounts of data. But rather than viewing this as just a
compliance exercise, machine learning and data science tools
can transform this into a possibility to learn more about their
clients to drive new revenue opportunities.
Nowadays, digital banking is becoming more popular and widely
used. This creates terabytes of customer data, thus the first step
of data scientists team is to isolate truly relevant data. After that,
Managing being armed with information about customer behaviors,
customer interactions, and preferences, data specialists with the help of
data accurate machine learning models can unlock new revenue
opportunities for banks by isolating and processing only this most
relevant clients’ information to improve business decision-
making.
Source; ActiveWizards
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 40Machine Learning
Use Case
The key to success in marketing is to make a
customized offer that suits the particular client’s needs
and preferences. Data analytics enables personalized
marketing that offers the right product to the right
person at the right time on the right device. Data mining
is widely used for target selection to identify the
potential customers for a new product.
Personalized Data scientists utilize the behavioral, demographic, and
marketing historical purchase data to build a model that predicts
the probability of a customer’s response to a promotion
or an offer. This helps banks make efficient,
personalized outreach and improve relationships with
customers.
Source; ActiveWizards
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 41Robotic Process Automation Use Cases
1 Data Replication 6 Customer Service
2 KYC / BSA / AML 7 IT Services
3 Lending 8 Contract Management
4 Payments 9 Compliance
5 Account Closure 10 User Access
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 42Data Replication
Auto Lending Mortgage Origination
Mobile Banking
Core Banking Mortgage Servicing
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 43Robotic Process Automation
Use Case
The pre-RPA implementation commercial lending process is 75 percent
manual, performed by a commercial loan processor in the back office—
each of these steps can be performed by a bot:
• Receive the final commercial loan onboarding package from
Underwriting and other supporting documents from the loan officer via
email
• Open the loan processing system to ensure all the information from the
loan officer and underwriting is in the loan processing system and
complete
• Reconcile, copy, and paste all the missing data from the loan officer’s
Lending email into the loan processing system
• Copy and paste all missing data from underwriting into the loan
processing system
• Prepare the loan file for closing by checking the Secretary of State
website for confirmation of loan applicant business status.
• Sends a standard template email to loan operations for loan booking
once all the paperwork is in good order
The above process takes 45 minutes per loan for a person to complete
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 44Robotic Process Automation
Use Case
Banks receive several requests to close the accounts on a
monthly basis. Sometimes, the accounts can also be closed if
the client does not furnish the proofs required for operating
the account. Considering the high volume of data handled by
the bank every month and the checklist they need to adhere
to, the scope for human error also increases.
With RPA, banks can send automated reminders to the
customers asking them to furnish the required proofs. It can
Account also process the account closure requests in the queue based
Closure on set rules in a short duration with 100% accuracy. RPA is
programmed to cover exceptional scenarios as well such as
closing an account due to failure in KYC compliance. So, this
makes it easier for the bank to focus on other functions that
are less monotonous and require more human intelligence.
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 45Navigating New Technologies
Examples of fintech solutions in place today
• BNY Mellon - Streamline trade settlement procedures – including clearing trades, conducting research on orders and resolving
discrepancies (e.g., reconciling trades) - Normally, human staff take between 5 and 10 minutes to reconcile a failed trade. In comparison,
a bot can perform the same procedure in “a quarter of a second"
• SunTrust has implemented Pega Robotic Desktop Automation in payment operations areas such as Consumer Bank Cards, Wires, and
ACH. Among the results delivered by robotics, the bank noted that average transaction speed improved 3.8x, average training time
improved 4x and the average error rate was reduced by 65 percent
• Deutsche Bank – "We are modernizing our IT and pursuing the digitalization of our business. Today, our private clients can open an
account online in a matter of minutes – and not seven days as before…We have launched robo-advisers (WISE) in the asset
management business and in the Private & Commercial Bank (ROBIN). WISE and ROBIN use algorithms to compile a suitable
portfolio for our clients. In our other businesses, too, we are utilizing robotics and artificial intelligence to automate what were previously
manual processes – this will minimize errors and lower costs.” Annual Report 2017
• Increase speed to process auto loans by validating customer data on government websites, such as the DMV or tax sites
• Reduced time to process consumer loans by eliminating the need to copy and paste data from one banking system to another
• Increase speed and accuracy of new bank account opening requests – eliminate data transfer errors from new account opening
request emails to core banking systems
• Customer service – bots can resolve lower priority inquiries and free up human customer service personnel to handle more complex
inquiries
• Credit card processing – bots are used to gather customer documents, perform credit checks and background checks, and make a
credit decisions based upon set parameters
• KYC process – bots are used to collect customer data, validate it, and perform screening
• Many banks also use chat bots to respond to customer inquiries
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 46Risks & Rewards
The speed and consistency of FinTech are great benefits if
working properly, but the risk exposure if not working properly
can spread across or multiply within the Bank extremely quickly.
Examples include:
• Faulty algorithm
• Incorrect and/or incomplete data accessed
• Process is hacked
• Business and/or economic conditions change but the
technology does not changes with them
© 2017 Grant Thornton LLP | All rights reserved | U.S. member firm of Grant Thornton International Ltd 47Thank-you
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