Impact of Human Centric AI on Our World of Audit - 20.12.2019.pdf

2019 © NextGen Knowledge Solutions Private Ltd. 1
Digital Accounting & Assurance Board of ICAI
National Conference on Information Technology
Hotel Hyatt Regency, Ahmedabad – December 20, 2019
Impact of Human Centric AI in Our World of Audit
Presented by:
Vinod Kashyap
NextGen Knowledge Solutions Private Ltd.

Agenda
o Preamble
o Artificial Intelligence
o Artificial Intelligence in Audit
o Intelligent Process Automation in Audit
o Ethical Implications
o Q & A

Preamble

“We stand on the brink of
of a technological revolution that will
fundamentally alter the way we live,
work and relate to one another. In its
scale, scope and complexity, the
transformation will be unlike anything
humankind has experienced before.”
Klaus Schwab on The Fourth Industrial Revolution
World Economic Forum

The 4th Industrial Revolution
1st Industrial Revolution
18th Century
2nd Industrial Revolution
19th – 20th Century
3rd Industrial Revolution
(1st Information Revolution)
Late 20th Century
4th Industrial Revolution
(2nd Information Revolution)
Early 21st Century
Steam-based
Machines
Electrical
Energy-based
Mass Production
Computer and
Internet-based
Knowledge
Artificial Intelligence
Information Technology
Information
Big Data
IoT
Cloud
Intelligence
AI SW
Source : World Bank

Are your skills ready and relevant for Career 4.0 ?
1st Industrial Revolution
18th Century
2nd Industrial Revolution
19th – 20th Century
3rd Industrial Revolution
(1st Information Revolution)
Late 20th Century
4th Industrial Revolution
(2nd Information Revolution)
Early 21st Century
90 % of US Work Force
are farmers
Source : https://www.agclassroom.org/gan/timeline/farmers_land.htm
are farmers
4.6 % of US Work Force
are farmers
are farmers but are
feeding more than 10 X
people then in 1700
1784 1870 1969 Today

“During the fourth
industrial revolution, we
will experience endless
waves of disruption, and
complacency can be
kiss of death.”
Miki Tsusaka, CMO, Senior Partner & Managing
Director, The Boston Consulting Group

In the digital age, the largest corporations now need to rapidly innovate not only to
stay ahead but simply to survive
Corporates in the digital age
1 Digital Vortex : How digital disruption is redefining industries (Global center for Digital Business Transformation
2 Darwinian Digital Disruption : Survival of the fittest, 2015 (Capgemini Consulting)
3 Based on A T Kearney analysis of Fortune 500 data
4 Lifetime expectancy has been derived from the five year moving average of the turnover rate of the year (for example
a 10% turnover rate implies a 10 year-expectancy)
5 Don’t get cozy, Fortune 500 : It’s a Do-or-Die time for digital disruption (Upstart Business Journal)
Rapid digital disruption will displace 40% of incumbent companies across 12
industries in 5 years (1)
Since 2000, 52% of the companies in Fortune 500 have either gone bankrupt,
been acquired or ceased to exist (2)
Lifetime expectancy of the companies in Fortune 500 list has fallen from 23
years in 1965 to 15 years in 2014 (3)
The average age of the company in S & P 500 was 60 years in 1960; it will be
12 years by 2020 (4)
Source : A T Kearney analysis

Emerging Technologies : Challenges and Opportunities
Artificial Intelligence (AI) Robotic Process Automation (RPA) Blockchain Big Data

Is it being very cultured ?
- Being a renaissance person
Is it being able to solve problems no one solved before ?
Is it being human ?
Is it contingent on the moment on time ?
Is it satisfying functionalities ?
What is Intelligence ?
Source : Miklos Vasarhelyei

Intelligence has been defined in many ways: the capacity for logic, understanding, self-
awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem
solving. More generally, it can be described as the ability to perceive or infer information, and to retain it
as knowledge to be applied towards adaptive behaviours within an environment or context.
Intelligence is most often studied in humans but has also been observed in both non-human animals and
in plants. Intelligence in machines is called artificial intelligence, which is commonly implemented
in computer systems using programs and, sometimes, specialized hardware.
What is Intelligence Cont.…
Source : Wikipedia

“Artificial Intelligence (AI, also Machine
Intelligence, MI) is Intelligence displayed by
machines, in contrast with the Natural
Intelligence (NI) shown by the human
beings.”
- Wikipedia
“AI is about making Computers that can
help us that can do the things that humans
can do but our current computers can’t.”
- Yoshua Bengio

Examples of Artificial Intelligence (AI)
Spotify
is a digital music service
that gives you access to
millions of songs.
is an intelligent personal
assistant developed by
Amazon.
is an intelligent personal
assistant, part of Apple’s
iOS, watchOS, macOS &
tvOS systems.
Is a virtual assistant
created by Microsoft for
Windows 10, Windows
10 Mobile, Windows
Phone 8.1
Alexa Siri Cortana

Artificial
Intelligence
Machine
Learning
Deep
Learning
AI is the study of devices that perceive their environment and
define a course of action that will maximize its chances of
achieving a given goal.
Machine Learning
is the subset of study of artificial intelligence, in which machines
learn how to complete a certain task without being explicitly
programmed to do so.
Deep Learning
is a subset of machine learning in which the tasks are broken
down and distributed into machine learning algorithms that are
organized in consecutive layers. Each layer builds up from the
output of the previous layer. Together the layers constitute an
artificial neural network that mimics the distributed approach to
problem solving carried out by neurons in a human brain.
Artificial Intelligence (AI)

Artificial Intelligence is becoming ubiquitous intelligence with the ability to see,
hear, speak, smell, feel, understand gestures, interface with your brain, and dream
AI that can See AI that can Speak AI that can Hear
AI that can Feel AI that can Smell AI that can Move
Source : PWC

Artificial Intelligence : Technology Landscape
REAL TIME EMOTION ANALYSIS
CHAT BOTS
NATURAL LANGUAGE PROCESSING
PATTERN RECOGNITION
NEURAL NETWORKS
AUTONOMOUS SYSTEMS
VIRTUAL COMPANIONS DEEP LEARNING
MACHINE LEARNING
REAL TIME UNIVERSAL
TRANSACTIONS
THOUGHT CONTROLLED
GAMING

Where Industries will put AI to work ?
Source : PWC
Ranking of AI impact by its potential to free up time, enhance quality and enhance personalization
o Supporting diagnosis by detecting variations in patients data
o Early identification of potential pandemics
o Imaging diagnosis
Health Care
1
Ranking Industry High Potential use cases
2
3
4
Automotive
o Autonomous fleets for ride sharing
o Semi-autonomous features such as driver assist
o Engine monitoring and predictive, autonomous maintenance
Financial Services
o Personalized financial services
o Fraud detection and anti money laundering
o Automation of customer operation
Transportation
and Logistics
o Autonomous trucking and delivery
o Traffic control and reduced congestion
o Enhanced security

Where Industries will put AI to work ?
Source : PWC
Ranking of AI impact by its potential to free up time, enhance quality and enhance personalization
o Media archiving, search and recommendations
o Customized content creation
o Personalized marketing and advertising
Technology, media
and
telecommunication
5
Ranking Industry High Potential use cases
6
7
8
Retail and
consumer
o Autonomous fleets for ride sharing
o Semi-autonomous features such as driver assist
o Engine monitoring and predictive, autonomous maintenance
Energy
o Smart metering
o More efficient Grid operation and storage
o Predictive infrastructure maintenance
Manufacturing
o Enhanced monitoring and auto-correction of processes
o Supply chain and production optimization
o On-demand production

o AI is going to replace all jobs.
o Only low-skilled and manual workers will be replaced by AI and automation
o Super-intelligent computers will become better than humans doing anything that we can do
o Artificial intelligence will quickly overtake and outpace human intelligence
o AI will lead to the destruction of enslavement of the human race by superior robotic means
Five Myths About Artificial Intelligence (AI)

Source : PWC
24% are using Artificial Intelligence
In a survey of CEO…
28% are evaluating Artificial Intelligence
27% are aware of Artificial Intelligence
21% say Artificial Intelligence is not a
priority
$15 T of global GDP impacted by Artificial Intelligence in 2030

Artificial Intelligence: How is it working?
Real world data is
continuously fed into AI
Data is used to identify
patterns and develop
models for predictive tasks
Throughout its use, AI
updates is models based on
new data it’s exposed to

Source : Forbes
The Rise of Artificial Intelligence

AI and The Singularity
The Singularity in Artificial Intelligence (AI) is the inflection point where machine advance beyond human
intelligence and thus become self sufficient. Jon Von Neumann first defined the term in the 1950s and it has
been further advanced by well known futurist and technologist Ray Kurzweil.

Jack Ma and Elon Musk : Debate on AI
Source : World AI Conference in Shanghai
Elon Musk : Computers will surpass humans in every single way, remember AlphaGo which won against the World
Champion of Go. The biggest mistake smart people are making is assuming that they are smart…they are not.
Jack Ma : Computers are clever (knowledge driven), but humans are smart (experience driven). We invented the
computer, but we have never seen a computer inventing a human.

Artificial Intelligence in Audit

Artificial Intelligence in Accounting & Auditing : Now for Real

How will AI affect the
Auditing Profession?

How will AI affect the Auditing Profession?
• Companies and organizations are generating and collecting large amounts of data on a continuous
basis, from points of sale, to shipment tracking information, as well as inventory counts in real
time. In addition, information from exogenous sources, in the form of social media and news
feeds to name a few, is readily accessible and available for analysis. It is, in fact, the application of
AI to this type of Big Data that is expected to take the auditing profession a step forward.
• AI application in auditing can significantly improve audit efficiency. Rather than manually
examining a sample of transactions, auditors can take advantage of AI methodologies to examine
complete populations of transactions in a much shorter time.
• The increasing maturity of AI technologies, more specifically deep learning technology, such as
visual recognition, textual analysis, natural language processing, and audio processing, provides
unlimited potential and inspiration for its application to auditing.

Deep Learning in Audit
Deep Learning in Audit
Deep Learning
in Speech
Recognition
Deep Learning
in Image
Recognition
Deep Learning
in Language
Analysis
Deep Learning
in Natural
Language
Classification
Sensor data, visual recognition
techniques are capable of
recognizing object categories
e.g.: product images, analyse
inventory images captured by
automated drones to replace
physical inventory checks
Analyse text, including both
HTML/text documents and
webpages “ text mining”.
e.g.: extract attributes from
conference call transcripts,
management discussion and
analysis sections of 10-Ks, and
other text data for audit analytics
streamed or recorded audio like
phone calls, interview
conversations, and management
presentations.
e.g.: voice interactivity can
transform speech into searchable
and analysable audio data
Classify earnings conference call
transcripts into two groups:
‘‘fraudulent’’ and ‘‘non-fraudulent.’’
e.g: language classiﬁcation model
to analyse forum posts, comments,
conversations from social media to
obtain supplemental audit evidence.

AI Enabled Automated Audit Process
Pre-Planning
• AI collects and analyze Big Data
• Data related to the client’s
organizational structure,
operational methods, and
accounting & financial system
fed into AI System.
Contracting
• AI uses the estimate of the
risk level from phase 1 and
calculate the audit fees and
the number of hours
• AI analyzes the database of
contracts and prepares the
contract
• Auditor and client sign the
contract.
• Feed flow chart, questionnaire
answers and narratives into the AI
system and use image recognition
and text mining to analyze them
• Use visualization & pattern
recognition to identify risk factors
• AI aggregates all the data to identify
fraud & illegal-acts risk factors.
Internal Controls & Risk Factors
• Continuous control monitoring
systems examine controls
continuously
• AI runs process mining to verify
proper Internal Controls
implementation
• Logs are automatically
generated to ensure their
integrity
Control Risk Assessment
Substantive Tests
• Continuous Data Quality Assurance
ensures quality of data and assurance
• AI examines data provenance
• Continuous Test on details of
transactions on 100% of the
population
• Continuous Test on details of balance
• Continuous Pattern recognition,
outlier detection and benchmarks
Audit Report
• AI uses a Predictive Model to
estimate the various risks
identified
• Audit Report can be continuous
(grade 1-100 for example)
rather than categorical (clean,
qualified, adverse etc.

Current AI Implementations
All Big Four Accounting Firms are reporting the use of these AI Blocks
Machine
Learning
Big Data /
Smart Analytics
Natural Language
Processing
Machine Vision Intelligent RPA
Speech
Recognition
• Classifier (Tax
Transaction,
Contract)
• Fraud Detection
• Review full
population of
outliers
• Large scale data
analysis
• Discover facts and
relations that are
difficult for the
human mind
• Synthesis of text
e.g. Review of
contracts, vendor
invoices, emails,
transcribed
conversation
• OCR + Machine
Learning to
extract data from
images
• Drones + IoT to
perform inventory
inspection
• Test of
transactions
• Document
workpapers
• GL review
• Bank confirmation
• Decode
conversation
• Chat Bots
• Digital assistants
Source : Ivy Munoko

Current AI Implementations Cont.…
Small and Medium Size Accounting Firms are using AI SaaS Source : Ivy Munoko
Ensemble AI Combines Machine Learning with
domain expertise / business rules and statistical
methods to :
• Gain actionable insights into large population of
data
• Review 100% of data
• Sample from outliers
• Perform risk assessment

Intelligent Process Automation in Audit

Intelligent Process Automation (IPA)
IPA is a constellation of technologies that expands RPA by augmenting it with AI,
cognitive technologies, and other technologies to make the process automation
scalable, flexible, and intelligent.
Intelligent Process Automation
Machine
Learning
Computer
Vision
Virtual
Agent
NLP/
NLG
Others
RPA
Cognitive
Computing
Other technologies
Data
Analytics
Drones
Smart
contracts
IoT Others
Source : Chanyuan (Abigail) Zhang

The “Sense-Think-Act” Loop of IPA
AI and
cognitive
technologies
Human
Think
Human is in the loop
RPA to execute workflows, APIs,
computer command scripts,
mechanical robotics
Act
Adapted from UiPath (2017)
Computer vision, natural
language processing, drones,
IoT, etc.
Sense

Types of Audit Tasks
Type Examples
Structured
a. Go to the Morningstar website, search for a particular fund name, copy paste the fund
returns to Excel, compare with the return provided by the client
b. Go to the third-party website, input user name and password, then download the
confirmation report
c. Reconciliations, recalculations, confirmations in substantive testing
Semi-structured
a. Read the supporting documents and choose “yes” or “no” on audit planning work paper
b. Go through the pension contract and extract the key term such as eligibility, and then execute
the corresponding testing queries.
Unstructured
a. Fair value estimation
b. Internal control risk assessment
c. Judgements on certain exceptions identified

Steps in Implementing IPA in Audit
Is the task structured,
semi-structured or
unstructured?
Is the audit task
automatable?
If it is automatable, can
or will it be
automated?
If the task will be
automated, what
potential automation
tools that can be used
for this task?
Is there any need to
redesign the process
for more efficient
automation?
Prototyping and
implementing

The “Auditor-in-Loop” IPA Eco-system

2019 © NextGen Knowledge Solutions Private Ltd.
Information
collection
Initial analysis
Audit plan
formation
Planning the internal
control testing based on
risk
Conducting the tests
Reconciliation
Confirmation
Analytical
procedures
Concluding
Reporting
Planning Test of Controls
Substantive Testing Concluding and Reporting
Audit Phase Example Tasks Type of Tasks RPA IPA
Planning
Establish planning materiality based on
pre-defined rules
Structured √ √
Fill in the planning worksheet per pre-
defined criteria based on structured or
unstructured information
Semi-
structured × √
Read and summarize internal and external
information about client’s business and
industry.
Unstructured × √
Test of
Controls
Test controls per pre-defined rules Structured √ √
Process the notable items and send the
auditors the most risky items to investigate (if
full population are tested)
Semi-
structured × √
Assess likelihood of misstatements based on
pre-defined rules or based on cognitive
reasoning
Unstructured × √
Substantive
Testing
Standardize the testing data if needed
Test key items per pre-defined rules
Structured √ √
Process the notable items and send the
auditors the most risky items to investigate
(if full population are tested)
Semi-
structured × √
Assess likelihood of misstatements based
on pre-defined rules or based on cognitive
reasoning
Unstructured × √
Concluding
and Reporting
Complete disclosure checklist on
formatted templates
Structured √ √
Conclude and issue appropriate type of
auditor's report
Semi-
structured × √
IPA in Major Phase of Audit

Audit Production Line Driven by IPA

Facilitate
continuous
audit
Impacts on
audit quality
Cost benefit
considerations
Reliability of
IPA solution
Stakeholders’
concerns
Discussions and Consideration

Ethical Implications

Why consider Ethics in use of Artificial Intelligence ?
The increased use of automation and artificial
intelligence (AI) in auditing will result in a shift in the
auditor’s role and level of involvement in the audit,
but not the auditor’s responsibility.
“30% of Corporate Audits will be performed by AI by 2025.”
There are benefits:
AI promises the capability to review unstructured data on a real time basis and provide a concise
analysis of numerical, textual and visual data

Examples of “what could go wrong” with AI Systems ?
Data collection and usage of AI leads
to Privacy / Security violations
Data inaccuracy ?
Therefore, Algorithm inaccuracy ?
Biased Data / Data that doesn’t represent
the true population result in unfair /
incorrect decisions made by Algorithms
Complex AI Models resulting in
unexplainable and therefore
undefendable decisions
Users of AI relying wholly on the
system, and not performing
reasonable tests of AI output
Regulations violated by AI use ?
e.g. HIPPA

Ethical Responsibility: Is it auditor’s responsibility ?
Is the fulfillment of the ethics, responsibility of the auditor to the shareholders and public
impacted by the use of Artificial Intelligence ?
Auditor’s mindset and behavioural expectation
Independence /
Confidentiality
Competence and
Due Care
Objectivity
Auditors are relying on emerging technology
Characteristics of emerging technology: radical novelty, fast adoption, high impact,
Uncertainty since not yet mature

Ethical Framework : A Futuristic Approach

How Ethical Issues are identified ?

Ethical Issues Identified

Proposed Responsibility Mapping

“No matter how complex things are,
basically everything is simple.”
Questions

THANK
YOU

Vinod Kashyap
Email : vinod.Kashyap@nextgenknowledge.com
Tel # : +91-96544-55392

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