SYSTEMS APPROACH FOR MANAGEMENT OF ARTIFICIAL INTELLIGENCE

IN SELECT FINANCIAL SERVICES

Synopsis

Submitted in the partial fulfilment for the requirements of

Doctor of Philosophy

In

Management

Submitted by

Bharti Kumari

Under the Supervision of

DR. JASPREET KAUR PROF. SANJEEV SWAMI

Supervisor Co-Supervisor

Assistant Professor Professor and Head

Department of Management Department of Management

Faculty of Social Sciences Faculty of Social Sciences

DEPARTMENT OF MANAGEMENT, FACULTY OF SOCIAL SCIENCES

DAYALBAGH EDUCATIONAL INSTITUTE (DEEMED TO BE UNIVERSITY)

DAYALBAGH, AGRA – 282005

2020
SECTION 1: INTRODUCTION

Artificial Intelligence is the ability of a machine to perform cognitive functions which we generally

associate with human minds, such as perceiving, reasoning, learning, and problem solving

(McKinsey, 2018). It is also a branch of computer science that concerns the development of machines

able to simulate human intelligence and actions. AI systems can also improve their performances

while learning from their experiences. Artificial Intelligence consists of data driven technologies, like

artificial neural network, machine learning, genetic algorithms, fuzzy logic, and robotics.

Just as much other technological advancement and result of Industry 4.0, Artificial Intelligence came

to our lives and changed it in drastic way (Omar et al., 2017). It offers solutions superior to those of

traditional methods of doing and human cognitive capacity. Consciously or unconsciously, Artificial

Intelligence has become an integral part of our day to day lives. Even we do not know in how many

ways, AI is involved in our daily routine. It is everywhere from Siri or Google Assistant in our phone

to Netflix or Youtube recommendations or from smart LED lights to Alexa powered by Amazon.

Being an emerging technology, it has been applied in a variety of industries to serve functions which

humans have often previously performed. Artificial Intelligence has a profound impact on just about

every industry, and the financial services sector is no exception. The applications of artificial

intelligence in financial services sector are many, from improving relationships with employees and

customers to finding patterns in extreme data volume to perform repetitive tasks. There is strong

agreement among IT decision makers of companies, providing financial services that AI is very much

needed in this segment to dramatically improve operational efficiency and fundamentally transform

this sector’s core financial processes (NASSCOM, 2018). However, this can only happen if there is

mass adoption of AI. The clear objective that stands out in this segment for using AI is to offer a more

proactive and personal customer experience at a lower cost, followed by management of back-end

business processes to reduce human error and to improve the turn-around-time for manual processes.

AI will have huge impact on various aspects of financial services operations, whether it is banking,

financial services or insurance. As better and more advanced AI systems are being created, financial

1
 services sector is becoming more curious to adopt such techniques in their operations, but there are

many challenges that a firm has to face before in managing it and it comes with many complexities at

individual, organisational, economical, national and international level which in turn, are not resulting

in increased operational efficiency. Before an organization takes the initiative to adopt AI, the

environment should be scanned and an adequate research should be conducted in the suitability of AI

applications (Omar et al., 2017) and even after adoption proper management of this disruptive

technology is crucial to align it with our goals. Considering the non-linearity and complexities of this

sector, the proper management of this disruptive technology is crucial. Therefore, the present study

focuses on addressing this issue.

The following research questions have been proposed in this concern:

i. What is the present status of AI technology in financial services and how it is managed at

present?

ii. What factors are pushing the better management of AI and what factors are acting as barriers of

management of AI in financial services?

iii. What will be optimal strategy for management of AI for value maximization of financial services

sector?

Thus, the thesis will provide a relevant outlook on the enablers and challenges in management of

artificial intelligence and its further impact on various dynamics considering value maximization of

Indian finance sector. To the best of our knowledge, this is one of the early studies in this area in the

Indian financial services sector.

SECTION 2: THEORETICAL BACKGROUND

2.1 Artificial Intelligence

The father of Artificial Intelligence, McCarthy (1956) defines AI as, “Artificial Intelligence is the

science and engineering of making intelligent machines, especially intelligent computer programs”.

2
The Oxford English Dictionary defines artificial intelligence as “the theory and development of

computer systems able to perform tasks normally requiring human intelligence”. The Accenture report

also says the same (Accenture, 2018). The technology – which enables machines to simulate and

augment human intelligence – has finally come of age.

Artificial Intelligence (AI) is intelligence exhibited by machines. In computer science the field of AI

defines itself as the study of “intelligent agents” (Mauersberger, 2017). Artificial intelligence (AI)

refers to the use of digital technology to create systems that are capable of performing tasks

commonly thought to require human intelligence (Banwo, 2018).

Generally, the term “AI” is used when a machine simulate functions that human’s associate with other

human minds such as learning and problem solving (Gupta, 2017). The technology –which enables

machines to simulate and augment human intelligence – has finally come of age (Accenture, 2018).

Based on capabilities, there are three types of Artificial Intelligence, namely Artificial Narrow

Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI).

Artificial Narrow Intelligence is the technology which imitates the human intelligence in a specific

or some narrowly defined task. It is the ability of a machine to perform. ANI is also known as weak

AI (Rouse, 2010).

Artificial General Intelligence is the stage of evolution of AI when machines will be able to do all

the intellectual tasks like human beings. AGI is also known as strong AI (Rouse, 2010). Strong AI is

considered as a threat to existence of human beings. According to Hawking, “The development of full

artificial intelligence could spell the end of the human race. It would take off on its own, and re-

design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution,

couldn’t compete and would be superseded”.

Artificial Super Intelligence is the stage of artificial intelligence when the capabilities of machines

will surpass the human intelligence. It outperforms humans Intelligence by self-improvement that

3
 lacks human's genetic limitations. It is currently a hypothetical situation. It is also known as

‘Conscious AI’ (Rouse, 2010).

2.2 Applications of Artificial Intelligence in Financial Services

a) Fraud Prevention: Fraud has long been a major issue for financial services institutions and as global

transactions have increased, the danger has too. Fortunately, artificial intelligence has enormous

potential to reduce financial fraud. To win the war against financial fraud, the segment providing

financial services must abandon out-dated approaches. Identifying and preventing fraudulent

transactions requires sophisticated solution, i.e. ML that can analyse high-volume data. By decoding

patterns and using predictive analytics, machine learning algorithms can block fraudulent transactions.

Fraud detection systems analyse clients’ behavior, location, and buying habits and trigger a security

mechanism when something seems out of order and contradicts the established spending pattern.

b) Risk Management: Artificial Intelligence is a game changer for risk management in finance as it

provides banks and credit unions with tools and AI solutions to identify potential risks and fraud.

2017 and 2018 saw financial institutions adopting AI solutions for financial risk management.

Traditional software applications predict creditworthiness based on static information from loan

applications and financial reports. AI enriched with ML technology can go further and also identify

unstructured data that can affect a client’s ability to pay.

Of course, risk management also extends to preventing financial crime and financial crisis prediction.

Machine learning in financial services provides solutions to these and many other risk concerns.

c) Investment Predictions: In recent years, hedge funds have increasingly moved away from traditional

analysis methods. Instead, they have adopted machine learning algorithms for predicting fund trends.

Using machine learning, fund managers can identify market changes earlier than is possible with

traditional investment models.

d) Customer Service: Customer service is the bread for every business. The simple truth is that without

satisfied customers, you’ll soon be out of business. Artificial Intelligence is currently being deployed

4
 in customer service in the form of chatbots and virtual agents with the primary goals of improving the

customer experience and reducing human customer service costs. While the technology is not yet able

to perform all the tasks a human customer service representative could, many consumer requests are

very simple ask that sometimes be handled by current AI technologies without human input.

Whether customers are speaking with a human, or using a virtual assistant, customers want accurate

information and fast solutions to their problems.AI technology has empowered customer support

allowing customers to interact with their banks from anywhere and anytime. Machine learning puts

a new spin on virtual assistants by enabling them to learn, rather than simply following a prescribed

set of instructions.ML-based chatbots adapts their approach based on the behavior of each customer.

The result is a chatbot that acts and feels more human for an improved customer experience (North,

2019).

e) Cyber Security: Data security is at the top of the list whenever financial institutions are asked about

their concerns and it must be as data of financial services segment is the data of savings and

investments of millions and billions of people.

The challenge to identify modern sophisticated cyber-attacks cannot be relegated to yesterday’s

security software. To meet the security threats financial institutions now face requires advanced

technology.AI enabled security solutions are uniquely capable of securing the world’s financial data.

The power of intelligent pattern analysis, combined with big data capabilities, gives ML security

technology an edge over traditional, non-AI tools.

f) Underwriting: The insurance sector is also coming up with a storm as they are moving towards

congruent automation (Vijai, 2019). A growing number of insurance companies have turned to AI to

help identify risks and to help set premiums. Since machine learning makes predictions based on

historical patterns and current trends, it is the perfect vehicle for insurance companies to improve

profitability. AI enabled fitness and vehicle tracking system in both health and auto insurance sector

give rise to the dynamic, intelligent underwriting algorithms that cleverly control the way premium is

dictated. Using Artificial Intelligence and Machine Learning, insurers can save a lot of time and

5
 resources involved in the underwriting process and tedious questions and surveys, and automate the

process. The same advantages apply to the banking sector. Financial institutions that offer insurance

products to their clients yield the same benefits from ML as insurance companies.

g) Algorithmic Trading: Algorithmic trading automates the trading process by executing trades

according to predefined criteria set by the trader or fund manager. In its simplest form, an “algo” trade

can automatically buy (or sell) a quantity of stock when the price-per reaches a specific level.

AI technology offers a new and diverse suite of tools to make algorithmic trading intelligent.ML

algorithms are designed to analyse historical market behavior, determine an optimal market strategy,

to make trade predictions, and more.

h) Money-Laundering Prevention: An estimated 2%-5% of the global GDP is laundered annually.

Unfortunately, FIs have not been winning the battle. AI technology can be utilised by FIs in anti-

money laundering (AML) by analysing the large amounts of data, to flag false alerts and identify

complex criminal conduct. It can identify connections and patterns that are too complex to be picked

up by straightforward, rule-based monitoring or the human eye.

i) Personalised Financial Services: Customers are ever-more demanding, and want fast, high quality

and above all personalized service from the organizations that they deal with. Tools of AI in financial

services segment will become the primary method of interaction between customers and banks. It will

automate a large number of banking processes and result in a better understanding of customer

behavior and expectations. Consequently, banks will be able to provide more personalized and

customer-centric services (Gauvrit, 2017; Jackson, 2017).

j) Wealth Management: With growing per capita income, people are looking for advanced and secured

ways to manage their financial health. Therefore, many financial institutions and banks are using AI

to guide their clients with investments. Advanced methods of AI are efficient in creating effective

solutions and provide lucrative returns for both clients and companies (North, 2019).

6
 2.3 Benefits of AI for Finance Sector

AI has the potential to transform just about every existing industry, yet, in the banking, financial

services and insurance sector it can truly unleash its transformative potential. Below, is a list of some

tangible benefits that AI applications can bring to the table in finance sector.

a) More Accuracy and Predictability: The primary goal of AI is to increase accuracy and

predictability of outcomes and reduce human errors in business processes, particularly manpower

intensive ones. Data driven accurate decisions at lower cost lead to a different style of management

which can help leaders and subordinates to take better decisions.

b) Availability: AI based solutions can work 24/7 without taking breaks or getting tired.

c) Scalability: AI tools and techniques are scalable because they use deep learning technology to

continuously improve themselves through self-learning.

d) Digital Fraud Detection and Prevention: Anomaly detection can be used to increase the accuracy of

credit card fraud detection and anti-money laundering (Vijai, 2019). UPI transactions and digital

payments are growing month-on-month, it will lead to an equally big spike in digital frauds. AI

technologies can monitor huge volumes of digital transactions to identify and prevent digital frauds.

Suspicious behaviour, logs analysis, and spurious emails can be tracked down to prevent and possibly

predict security breaches.

e) Understand Customer Behaviour: As customers go online, AI will be required to analyse customer

behaviour by tracking the websites which consumer frequently visits and the frequently used method

of payment and improve their experience.

f) Maintain Regulatory Compliance: Negligence in security measures could lead to financial losses.AI

can help companies to analyse data to detect any regulatory deviations to stay on the right side of the

law.

7
g) Digitization and automation in back-office processing: Capturing documents data using OCR and

then using machine learning/AI to generate insights from the text data can greatly cut down back-

office processing times.

h) Customer Retention: Tailored products can be offered to clients by looking at historical data to

retain the customer for longer period.

2.4 Present Status of AI in Indian Finance Sector:

Research institutions and universities have been working with various AI technologies for decades,

and especially in the area of social transformation. With enabling technologies becoming a lot more

accessible and inexpensive, AI is now becoming main stream, with large enterprises and start-ups. A

research showed that the adoption of AI has the potential to add nearly $1 trillion to the Indian

economy in 2035 (Accenture, 2017). India is now global hub for financial services, which account for

more than half of India’s IT services and Business Process Management (BPM) exports (NASSCOM,

2018).

Indian banks like SBI, HDFC, ICICI, HSBC and Axis banks in India have turned towards AI in a

significant way. Below we provide brief details of some of these initiatives:

Table 1 : Present Applications of AI in Finance Sector

Organizations AI applications
State Bank of India (SBI) 1. Conducted “Code for Bank" hackathon.
2. Launched beta testing SIA- SBI Intelligent Assistant.
Canara Bank 1. Installed a robot named Mitra at its Chennai Branch.
2. Launched its first digital banking branch ‘CANDI’.
HDFC Bank 1. Chatbot- ‘Electronic Virtual Assistant’, or EVA.
2. Humanoid- Intelligent Robotic Assistant or IRA.
HDFC Life Insurance AI based email bot, named SPOK.
ICICI Bank 1. Chatbot- iPal.
2. Deployed robotic software.
Axis Bank 1. Virtual Assistant- Axis AHA.
2. Cheque authentication by AI.
ICICI Lombard 1. Chatbot- MyRA.
2. Robotic Process Automation to automate mundane
tasks.
Fullerton India Credit Company Ltd. Chatbot- ASHA
Policy Bazaar Chatbot- PBee
Kotak Mahindra Bank Chatbot- Keya
Punjab National Bank (PNB) Using AI in account reconciliation.
PNB Metlife Chatbot- Dr. Jeevan
National Stock Exchange Working on artificial intelligence to strengthen its
surveillance operations to prevent any manipulative activities.

8
 Bombay Stock Exchange Uses data analytics based system solution.

SECTION 3: REVIEW OF LITERATURE AND IDENTIFICATION OF RESEARCH GAP

3.1 Review of Literature

The review of literature for the proposed research is performed to understand the basic concepts and

insights of AI and financial services sector of India as well as abroad.

Classification of Literature Review: A broad classification of the previous papers is given as

follows:

1. Focus of the paper

o Banking

o Financial Services

o Insurance

o Artificial Intelligence

2. Context of the paper

o Indian

o Foreign

3. Type of Data for analysis

o Qualitative

o Quantitative

4. Research Design

o Exploratory

o Descriptive

o Analytical

9
 Table 2: Synthesis Matrix of Literature Review

Summary of Literature Review

Focus Issue Context Data Type Methodology

Financial Services

Market/Portfolio
Category

Management/

Quantitative

Descriptive
Qualitative

Exploratory
Title

Analytical
Intelligence
Insurance

Artificial
Banking

Foreign
NBFC)
Author(s) (Year)

Indian
(Stock
Artificial Intelligence, Expert Systems, and Financial
Planning Research
✔ ✔ ✔ ✔ ✔
Jay T. Brandi (1988) Paper

Artificial Intelligence and Financial Services Research

✔ ✔ ✔ ✔ ✔
L.F. Pau (1991) Article
Artificial Intelligence and Expert Systems in
Accounting Databases: Survey and Extensions Review
✔ ✔ ✔ ✔
Daniel E. O'Leary (1991) Paper

Artificial Neural Network and the Financial Markets:

A Survey
Research
Amitava Chatterjee, O. Felix Ayadi and Bryan E. ✔ ✔ ✔ ✔
Artcle
Boone (2000)

Artificial Intelligence in Portfolio Management

Man-Chung Chan, Chi-Cheong Wong, W.F. Tse,
Research
Bernard K.-S. Cheung and Gordon Y.-N. Tang ✔ ✔ ✔ ✔ ✔
Article
(2002)

Application of neural networks to stock prediction in

“pool” companies
Research
Oscar Sapena , Vincente Botti and Estefania Argente ✔ ✔ ✔ ✔
Article
(2003)

10
 Summary of Literature Review (Contd.)
Focus Issue Context Data Type Methodology

Artificial Intelligence
Management/NBFC)
Financial Services

Market/Portfolio
Category

Quantitative

Descriptive
Qualitative

Exploratory

Analytical
Title

Insurance
Banking

Foreign
Indian
(Stock
Author(s) (Year)

Opportunities for Artificial Intelligence Development

in the Accounting Domain: The Case for Auditing
Review
Amelia A. Baldwin, Carol E. Brown And Brad S. ✔ ✔ ✔ ✔
Paper
Trinkle (2006)

Assessing Bank Efficiency and Performance with

Operational Research and Artificial Intelligence
Research
Techniques: A Survey ✔ ✔ ✔ ✔ ✔
Article
Meryem Duygun Fethi, Fotios Pasiouras (2009)

Stock Price Prediction using Neural Network with

Hybridized Market Indicators
Research
Adebiyi Ayodele, K. Ayo Charles, O. Adebiyi Marion ✔ ✔ ✔ ✔
Article
, and O. Otokiti Sunday (2012)

The Role of Artificial Intelligence in the

Development of Accounting Systems: A Review Review
✔ ✔ ✔ ✔
Syed Moudud – Ul - Huq (2014) Paper

Reducing Risk in KYC (Know Your Customer) for

large Indian banks using Big Data Analytics Research
✔ ✔ ✔ ✔
Anuraj Soni, Reena Duggal (2014) Article

Enablers and restrictors of mobile banking app use:

A fuzzy set qualitative comparative analysis (fsQCA)
Jose Manuel Cristovao Verissimo (2016) Research
✔ ✔ ✔ ✔ ✔
Article

11
 Summary of Literature Review (Contd.)
Focus Issue Context Data Type Methodology

Artificial Intelligence
Management/NBFC)
Financial Services

Market/Portfolio
Category

Quantitative

Descriptive
Qualitative

Exploratory

Analytical
Insurance
Banking

Foreign
Indian
(Stock
Title
Author(s) (Year)

Stock Market Index Prediction using Artificial

Neural Network
Research
Amin Hedayati Moghaddam, Moein Hedayati ✔ ✔ ✔ ✔
Article
Moghaddam and Morteza Esfandyari (2016)

The Effects of Artificial Intelligence and Robotics on

Business and Employment: Evidence from a survey
Discussion
on Japanese firms ✔ ✔ ✔ ✔
Paper
Morikawa Masayuki (2016)

Adoption of Robots, Artificial Intelligence and Service

Automation by Travel, Tourism and Hospitality
Conference
Companies – A Cost-Benefit Analysis ✔ ✔ ✔ ✔
Proceedings
Stanislav Ivanov, Craig Webster (2017)

Investigating An Innovative Service with Hospitality

Robots
Research
Chun-Min Kuo, Li-Cheng Chen, Chin-Yao Tseng ✔ ✔ ✔ ✔
Article
(2017)

A Literature survey on Artificial Intelligence

Conference
Nishika Gupta (2017) ✔ ✔ ✔ ✔
Proceedings
The Diffusion of Artificial Intelligence in Governance
of Public Listed Companies in Malaysia
Research
Siti Aisyah Omar, Farhana Hasbolah and Ulfah ✔ ✔ ✔ ✔
Article
Mansurah Zainudin (2017)

12
 Summary of Literature Review (Contd.)
Focus Issue Context Data Type Methodology

Artificial Intelligence
Management/NBFC)
Financial Services

Market/Portfolio
Category

Quantitative

Descriptive
Qualitative

Exploratory

Analytical
Insurance
Banking

Foreign
Indian
(Stock
Title
Author(s) (Year)

Artificial Intelligence in Finance: Forecasting Stock

Market Returns using Artificial Neural Networks
Thesis ✔ ✔ ✔ ✔ ✔
Alexandra Zavadskaya (2017)

Artificial intelligence and machine learning in

financial services: Market developments and
financial stability implications Report ✔ ✔ ✔ ✔
Financial Stability Board (2017)

Economic Policy for Artificial Intelligence Working

✔ ✔ ✔
Ajay Agrawal, Joshua Gans, Avi Goldfarb (2018) Paper
Artificial Intelligence in Corporate Banking: A
Closer Look at the Potential Impact on E-Business
Processes
Dissertation ✔ ✔ ✔ ✔ ✔
Jonas Uyttendaele, Supervisor- Prof. Dr. Steve
Muylle (2018)

Artificial Intelligence in Finance The Road Ahead

Paul Dravis (2018) Report ✔ ✔ ✔ ✔ ✔

AI in Banking and Finance

Saman Goudarzi, Elonnai Hickok, Amber Sinha
Report ✔ ✔ ✔ ✔
(2018)

ANN Model to Predict Stock Prices at Stock

Exchange Markets Research
✔ ✔ ✔ ✔
Barack Wamkaya Wanjawa, Lawrence Muchemi Paper

13
 Summary of Literature Review (Contd.)
Focus Issue Context Data Type Methodology

Artificial Intelligence
Management/NBFC)
Financial Services

Market/Portfolio
Category

Quantitative

Descriptive
Qualitative

Exploratory

Analytical
Insurance
Banking

Foreign
Indian
(Stock
Title
Author(s) (Year)

The Impact of Artificial Intelligence (AI) on

Financial Job Market
David He, Michael Guo, Jerry Zhou and Venessa Report ✔ ✔
Guo (2018)

National Strategy for Artificial Intelligence

#AIFORALL Discussion
✔ ✔ ✔ ✔
Anna Roy, Amitabh Kant (2018) Paper

Analysis of the Impact of Artificial Intelligence

Application on the Development of Accounting
Research
Industry ✔ ✔ ✔ ✔
Article
Jiaxin Luo, Qingjun Meng, Yan Cai (2018)

Artificial intelligence and financial services:

Regulatory tracking and change management Review
✔ ✔ ✔ ✔ ✔
Adedayo Banwo (2018) Paper

Artificial Intelligence for Banking, Financial

Services and Insurance Sector
Report ✔ ✔ ✔ ✔ ✔
NASSCOM and CMR (2018)

The economics of artificial intelligence:

Implications for the future of work
Research
Ekkehard Ernst, Rossana Merola, Daniel Samaan ✔ ✔ ✔ ✔
Paper
(2018)

Variable Selection for Artificial Neural Networks

Research
with Applications for Stock Price Prediction ✔ ✔ ✔ ✔
Article
Gang-Hoo Kim and Sung-Ho Kim (2018)

14
 Summary of Literature Review (Contd.)
Focus Issue Context Data Type Methodology

Artificial Intelligence
Management/NBFC)
Financial Services

Market/Portfolio
Category

Quantitative

Descriptive
Qualitative

Exploratory

Analytical
Insurance
Banking

Foreign
Indian
(Stock
Title
Author(s) (Year)

Using AI to Make Predictions on Stock Market Research

✔ ✔ ✔ ✔ ✔
Alice Zheng and Jack Jin (2018) Paper
Using artificial intelligence to create value in
insurance
Research
Mikko Riikkinen, Hannu Saarijärvi, Peter Sarlin, ✔ ✔ ✔ ✔ ✔
Article
Ilkka Lähteenmäki (2018)

Artificial Neural Network: Validity of Technical

Analysis Indicators for predicting Stock Prices Research
✔ ✔ ✔ ✔
Navita Nathani, Jaspreet Kaur, Divya Khator (2019) Article

Indian shopper motivation to use artificial

intelligence: Generating Vroom’s expectancy theory
Research
of motivation using grounded theory approach ✔ ✔ ✔ ✔
Article
Komal Chopra (2019)

Artificial Intelligence in Indian Banking Sector:

Challenges and Opportunities Research
✔ ✔ ✔ ✔ ✔
Dr. C. Vijai (2019) Article

Evolution and control of artificial super intelligence

(ASI): a management perspective
Research
Karan Narain, Agam Swami, Anoop Srivastava, ✔ ✔ ✔ ✔
Article
Sanjeev Swami (2019)

15
3.2 Research Gap

Although previous researchers have provided significant contributions in the field of AI (Agarwal et

al., 2018; Ivanov, Webster, and Berezina, 2017; Kannabiran and Dharmalingam, 2012; Yu, Beam ,

and Kohane , 2018; Narain et al., 2019; Kuo et al., 2017; Masayuki, 2016; Omar et al., 2017), such

research works have not primarily focused on the financial services sector, particularly in the Indian

context. A very few studies have been conducted in relation to AI in whole financial services sector.

Individually, there are some studies in banking sector (Uyttendaele and Muylle, 2018; Ince and Aktan,

2009; Goudarzi et al., 2018; Celik and Karatepe, 2007) and in financial services (Chan et al., 2002;

Chatterjee et al., 2000; Moghaddam et al., 2016; Moudud-Ul-Huq, 2014), but not specifically in the

Indian context. Yet, two studies have reviewed AI in banking and Finance respectively (Goudarzi et

al., 2018; Vijai, 2019).

There is only one practice-oriented study regarding AI in whole financial services segment, i.e. a

report by NASSCOM on AI for Banking, Financial Services and Insurance Sector (NASSCOM,

2018). Only one study has been found which has examined the management perspective of AI (Narain

et al., 2019) which is not considering the financial services.

From methodological point of view, none of the study has attempted for applying systems approach in

the management of this disruptive technology in financial services sector.

This study is an attempt to fill the gap of use of systems approach to understand the management of

AI in financial services sector and provide bird eye view. Therefore, this study will provide a strategic

framework is required in the light of existing enablers and challenges of management of AI so that the

value of financial services sector can be maximized.

SECTION 4: CONCEPTUAL FRAMEWORK

The researcher has proposed conceptual framework which investigates the enabling factors to the

management of artificial intelligence in financial services. Furthermore, it includes he identification of

challenges to financial services sector for AI management. The model also tries to understand the

16
relationship with value maximization in financial services sector, with the efficient management of

Artificial Intelligence.

Enablers

Management of Artificial Value

Intelligence in select Maximization
financial services

Challenges

Figure 1: Conceptual Framework (Source: Researcher’s own construct)

4.1 Enablers of AI Management:

For any kind of management, there are some enabling or driving factors that are pushing the efficient

management. For example, Robust and rapid processing needs, advent of mobile technology, data

availability, and proliferation of open-source software offer management of AI a huge scope in the

financial services sector. Companies those who instead view incorporating AI as building a new long-

term capability have the best chance to transform their organizations for competitive advantage

(Sinclair et al., 2018). In fact, according to an Industrial Development Corporation study, over half of

best-run midsize businesses view AI, machine learning, and digital assistants as critical enablers and

necessities for staying competitive, compared to approximately 13% of digital laggards.

Several studies have demonstrated that technology adoption has a positive relationship with perceived

usefulness (Schierz et al., 2010; Talukder, 2012). In the case of AI, usefulness can be defined in the

following considerations: robots, chatbots and self-service kiosks can operate 24/7, much more than

human employees. Furthermore, chatbots can serve numerous customers simultaneously, which is not

the norm with human employees. Machines can perform the same task thousands of times without

17
complaints. AI could enhance perceived service quality, create value for customers, save employees’

time (Ivanov et al., 2017).

The demand for efficient management of AI is also due to regulatory compliance. New regulations

have pushed banks to automate and adopt new analytical tools that can include use of AI and machine

learning (Adobe, 2019; Financial Stability Board, 2017). One of the primary objectives using AI is to

monitor all processes and data for regulatory compliance and security (Rupali, 2018). In an era of

cyber-crime and stringent regulatory requirements, a highly fraud-resistant system for protecting and

authenticating almost any kind of transaction could have a revolutionary impact on the financial

services industry (PwC, 2016).

Several studies (Goudarzi et al., 2018; NASSCOM, 2018; Roy and Kant, 2018; Rupali, 2018) found

that factors such as technical support, personal innovativeness, incentives, image and enjoyment with

innovation have stronger influence on management of innovation. Availability of computing power

owing to faster processor speeds, lower hardware costs, and better access to computing power via

cloud services, cheaper storage, parsing, and analysis of data through the availability of targeted

databases, software, and algorithms, trust and corporation of employees on management’s decision to

implement AI tools and techniques are also a enabling factors for management of AI in an

organization. The present research will try to determine such more enablers for management of

Artificial Intelligence in financial services sector.

4.2 Challenges in Management of Artificial Intelligence:

Any change cannot be implemented without facing any challenge, so is the case with management of

AI adoption. There are a number of key challenges faced by companies those want to move towards

automation and AI. A magnificent well – informed AI system will be exceptionally satisfying at

performing its goals but we do have to align those goals with ours (Gupta, 2017). As per the

discussion paper, National Strategy for Artificial Intelligence #AIFORALL (2018), the challenges that

any company can face regarding AI in India are concentrated across common themes of:

18
 1) Lack of enabling data ecosystems

2) Low intensity of AI research

a) Core research in fundamental technologies

b) Transforming core research into market applications

3) Inadequate availability of AI expertise, manpower and skilling opportunities

4) High resource cost and low awareness for adopting AI in business processes

5) Unclear privacy, security and ethical regulations

6) Unattractive Intellectual Property regime to incentivise research and management of AI.

For AI enablers in finance such as Kasisto, Voyager Labs, IBM Watson Cyber security, and

Amazon’s Alexa platform, the underserved market presents vast opportunities for engagement, but

only if they can creatively overcome the industry’s daunting challenges. Other firms cited “siloed data

sets, regulatory compliance, fear of failure and unclear internal ownership of emerging technologies”

act as main thwarting factors for innovation (Zhou, 2017). A similar study by PWC (2017)

showed that two in every three financial services firms in the US were hindered in AI adoption by

“operations, regulations, and limitations in budget or resources”. Some challenges have been briefly

described below:

a) Resistance to change- Change is always inevitable so is resistance to change. Resistance to change is

one of the biggest obstacles to any change program. It is basic human nature of people to try and keep

their customs, methods and way of doing routine tasks constant. Adopting the new technology would

require reengineering of the processes within the company, rewriting the service operations manuals

and training staff to use the new technology, thus putting employees outside their comfort zones. They

may be afraid of the change and consider the technology as a threat for their jobs (Ivanov et al., 2017).

The resistance of change is a barrier in effective management of artificial intelligence in this sector

because this sector is already based on so many risks and returns.

b) AI management is Expensive- The use of robots, self-service kiosks, chatbots or any other AI-

enabled software or machine is not free and require financial resources in several directions, i.e.

19
 Acquisition cost, installation cost, maintenance cost, software update cost, cost for hiring specialists,

cost for staff training, etc. (Ivanov et al., 2017). Currently, AI is still in its infancy stages. That,

coupled with its high price tag, makes it a tool that is out of reach for the average individual investors

(Chatterjee et al., 2000).

c) Customers’ readiness and willingness to be served by machines- Customers may resist being

served by a robot or serve themselves via a kiosk, and prefer a human employee. In such situation

automating the service delivery process might not yield the expected positive results for the company

(Ivanov et al., 2017).

d) Lack of Credible and Quality Data- Artificial intelligence thrives with data. The more data you

have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore.

Artificial intelligence supports decision by analysing enormous information (Chan et al., 2002). You

also need high-quality data (Rijmenam, 2019), or in the words of Peter Norvig, you need better data:

“We don't have better algorithms, we just have more data. More data beats clever algorithm, but better

data beats more data." - Peter Norvig - Director of Research, Google.

e) Lack of required skills and knowledge- AI is generating the demand for new skill sets in workplace.

There is a wait for right talent pool to deploy it properly. The lack of well-trained professionals who

can build and direct a company’s AI and digital transformation journeys noticeably hinders progress

and continues to be a major hurdle for businesses (NASSCOM, 2018).

f) Others- There are a lot of other challenges also that financial services sector is facing in management

of AI. Some of them are: Identifying the right AI vendor, Immaturity of industry standards around AI

based systems, Training the deep learning systems to work efficiently with less data, Structuring the

data according to AI based system, Getting the AI system to interpret questions correctly, High cost of

investment, Convincing top management to deploy AI, Addressing security and privacy issues, etc.

This research will try to determine such more challenges faced by financial services sector in

management of AI.

20
4.3 Value Deliverables: Every business entity be it of financial services sector or any other,

needs the maximization of the value deliverables to remain in the competition. The concept of value

in organisations is measured in terms of risks and returns. The following measures would be used for

measuring value of financial services sector to examine the value delivered through efficient

management of AI:

Table 3: Value Deliverables

Risks Volatility of shares/ market

Returns Profitability Indicators

 Return on Investment
 Return on Assets
 Earnings Per Share

Soundness Indicators
 Non-Performing Loans to Total Loans
Ratio
 Loans to Deposits Ratio
 Capital to Risk Adjusted Ratio

It is indicated in the past researches that the technology intervention improves the productivity of

organizations. The productivity-improved results will be checked with the help of given indicators

(Table 3). These indicators are not only affected by AI. The researcher intends to check the

influence of management of AI on these factors.

SECTION 5: COMPLEXITY OF THE PROBLEM

Dealing with complexity requires shifting our focus so as to look at not just the parts to a system but

also the overall macro system as a whole. A complex system is one that has many diverse components

that are highly interconnected and interdependent and the same can be said of finance sector. The

finance sector is too complex and so many transactions take place in it like. Finance systems allow

funds to be stored and moved between economic actors; they enable individuals and organizations to

share and exchange ownership with the associated risks and returns. Being in a same system, each and

every factor acts as a sub-system and somehow these sub systems affects every other sub-system and

other sub-systems affect them as well. Each element of a system is composed of smaller subsystems,

21
which may contain sub-subsystems. Each element of an open system also connects to a larger

environment. This shows a non-linear complex scenario. If visualized, all these factors are connected

to each other in a complex web like structure as dependent variables and drivers (variables that drive

other variables). The same is applied in finance sector. All complex systems involved emergence and

the formation of qualitatively different levels, from the micro to the macro. FINANCIAL SERVICES

systems also operate at all levels from personal finance, to corporate finance, to national, to the

global financial system indicating micro to macro.

The proper management of AI is very critical in this complex system. The management of technology

has certain entry-level enabling factors with possible implementation and maintenance related

challenges acting as obstacles to achieve the value like increase in financial performance, accuracy in

decision making, customer retention and engagement, etc.

The proposed problem is complex because we cannot take AI as any other change. It is single biggest

revolution of our times. It can be either best revolution or the worst revolution. All these depend on its

management. There is ambiguity among the enablers of management of AI and inter-relationships

between enablers and challenges are unknown. It is uncertain that value deliverables are the result of

efficient management of AI or other factors are contributing in it. The management of AI is also

unstructured as there are no fixed standards for AI management in organizations. The finance sector is

not the linear one, as it interacts with many other sectors also. There are abundant complications when

trying to create an intelligent system (Gupta, 2017). As finance sector is an important element of our

economy, any technology adoption decision taken by this- segment will highly influence it by

affecting job roles of our nation. Questions regarding national security and cyber security might be

raised. Legal and ethical issues after adoption of automation generate major complexity. Regarding

this, the first effect on the business side will also be on the hiring or buying new robots which most

probably have an artificial intelligence comparing with its first movers.

Thus a strategic framework is required in order to make successful adoption and management of AI

techniques which can deliver valuable output to the finance sector in presence of this complex system.

22
SECTION 6: SYSTEMS APPROACH TO PROBLEM ANALYSIS

This section provides a brief overview of the systems approach, which will be used to address the

current research problem which is management of AI in finance sector.

Introduction to Systems Approach

The system is a regularly interacting or interdependent group of subsystems forming a unified whole.

The systems approach is an old concept and the term has appeared frequently in management and

systems literature recently. The systems approach has been developed as a response with the

increasing complexity of the socio-technical and managerial systems.

The systems has various inputs, which go through certain processes to produce outputs, which

together, accomplish the overall desired goal for the system. System is usually made up of many

smaller systems, or subsystems. For example, an organization is made up of various administrative

departments, managerial functions, products, services and individuals. The approach concentrates on

the holistic view of the system where the role of each component is studied and analysed to

understand the activity of whole system.

System Dynamics Modelling- System dynamics is a conceptual approach that enables to represent

the structure and behavior of complex systems over time, providing a method for systems description

as well as a useful computational support for simulation (Sterman 2000). It is concerned with using

systems ideas to increase understanding of various types of phenomena and to aid in the decision-

making which determines action upon the processes involved (Keys, 1990). The main elements of SD

are causal loop diagram, stock flow model and dynamic simulation.

Flexible Systems Approach- Flexible systems approach is also termed as soft systems approach or

applied systems approach. Soft systems approach is a framework which does not force or lead the

systems analyst to a particular ‘solution’, rather to an understanding (Hirschheim et al., 1995).

This approach proposes that a flexible policy framework is needed to strategically meet the dynamics

of the new era (Lie et al., 2018) where non-linear and pluralistic complex problems (a problem

23
situation in which participants do not share same values and beliefs, where debate, disagreement, even

conflict, can take place) are prevailing due to the presence of several interacting sub-systems in a

system. Some of the main techniques in the soft systems approach for solving complex problems and

strategy formulation are Brainstorming, Idea Engineering, Nominal Group Technique (NGT),

Interpretive Structural Modelling (ISM), Cross Impact Matrix Multiplication Applied to Classification

(MICMAC), Options Field/Profile Methodology, Fuzzy Consideration, Scenario Building

Methodology, Delphic Hierarchic Process.

The present study will use flexible systems approach because non-linear and pluralistic complex

problems are prevailing due to the presence of several interacting sub-systems in a system.

SECTION 7: OVERVIEW OF PROPOSED STUDY

7.1 Need of the study

Artificial Intelligence is an emerging technology and is catalyst for Industry 4.0. Thus, every industry

is adopting AI techniques to reduce human efforts and give accurate and faster results but this

disruptive technology also arises questions on human existence. To make this technology, a helping

hand and not to become its slave an efficient management of this disruptive technology is necessary.

Hence, artificial intelligence is attractive for financial services segment too, because it eliminates the

human errors, excessive work load leading to free up employees for focusing in more meaningful

work, and enable organisations to provide personalised financial services to retain customers. The

management of AI in this sector is very necessary as it deals with wealth of individuals, corporates

and nations.

There is a lot of hype about the adoption of artificial intelligence. It is becoming challenging for

organisations to manage the same. Finance sector has some enablers and challenges to the

management of AI, yet how they are related to value maximization is unexplained. The possible

impacts range from positive and goal oriented to the worst, and might be impossible to be avoided.

24
Thus, companies need to monitor the incoming changes and adopt the best strategy for companies’

sustainability (Omar et al., 2017).

To make the artificial intelligence in finance sector to realize its potential, there has to be clear

understanding of the varied problems concerned and effective management of AI should be carried

out at personal, corporate, national and international levels because it is the matter of savings and

investments of people.

The proposed research would help to identify and understand the various challenging and enabling

factors for the better management of AI with consideration of the complexity created by these factors.

Also, strategic framework would be developed with the help of hierarchical model under system

methodology to resolve the complexity. This research has been proposed by considering the aim of

the fourth industrial revolution and ‘National strategy for AI’, a strategy document proposed by NITI

Aayog. Thus the main purpose is to develop a strategic framework for maximizing the value

deliverables of the financial services segment through managing AI.

7.2 Objectives of the Study

The proposed research is aimed at accomplishing the following objectives:

Objective 1: To study the present status of adoption and management of artificial intelligence in

select financial services.

Objective 2: To identify the enablers and challenges in management of artificial intelligence in

financial services.

Objective 3: To develop a hierarchical structural model under relevant contextual relations of the

identified enablers and challenges for management of artificial intelligence in select financial services.

Objective 4: To determine the priority of different enablers and challenges for value maximization in

financial services.

Objective 5: To examine the inter-relationships in challenges and enablers to management of

artificial intelligence in financial services.

25
Objective 6: To examine the relationship between management of AI and value maximization in

financial services sector.

7.3 Research Methodology

7.3.1 Scope of the Study: The proposed study will be focused on management of artificial

intelligence techniques in Indian finance- sector.

7.3.2 Data collection

Primary Data: The proposed study is based on the application of the systems approach. The primary

data in this case will be collected through series of system science workshops with the panel of 15-20

domain experts.

Experts Profile: Since the proposed research is related to finance sector, various domain experts from

this sector would be invited for the multi-stage workshops. The profiles of the domain experts would

be academicians of finance field, artificial intelligence and systems approach, managers/employees of

established banks, financial services providing companies and insurance companies and AI experts.

The brief classification of experts for the proposed study is as follows:

Table 4: Sample Plan for Experts

Domain Experts
Academicians
Workshops Mutual Stock
IT (AI) Banks Insurance
Finance Systems Funds Market
Enablers and
Challenges 5 2 2 2 2 2 2
Identification
ISM 5 2 2 2 2 2 2
AHP 5 2 2 2 2 2 2
DEMATEL 5 2 2 2 2 2 2

The number of experts may vary and overlap in different workshops of systems sciences.

Secondary Data: Annual reports of various companies of finance sector and other researches

highlighting the artificial intelligence adoption and management status in Indian financial services

sector.

26
7.3.3 Research Design: The proposed research is both analytical and descriptive since it uses both

systems approach and survey technique, respectively.

7.3.4 Sampling Technique: Judgmental Sampling will be used for both, ethnography study and

experts for workshops.

7.3.5 Tools of Data Analysis

Following table reveals the specific methodology which will be used to fulfill the objectives:

S. No. Objectives of the Study Proposed Tools

Secondary data analysis and

To study the present status of adoption and management
1 personal interview with domain
of artificial intelligence in select financial services.
experts

To identify the enablers and challenges in management of

NGT Workshops and literature
2
artificial intelligence in financial services.
survey

To develop a hierarchical structural model under relevant

contextual relations of the identified enablers and

Interpretive Structural Modeling
3
challenges for management of artificial intelligence in
(ISM)
select financial services.

To determine the priority of different enablers and

Analytic Hierarchy Process
4
challenges for value maximization in financial services.
(AHP)

To examine the inter-relationships in challenges and

Decision Making Trial and
enablers to management of artificial intelligence in
5 Evaluation Laboratory
financial services.
(DEMATEL)

To examine the relationship between management of AI

Survey through Structured
6 and value maximization in financial services sector.
Questionnaire

27
 7.3.6 Proposed Tools and Techniques to be used: The proposed research will use the following

tools and techniques-

 Ethnography: Ethnography is a tool for primary data collection. Ethnography is a study in which

researchers directly observe and/or interact with a study’s participants in their real-life/natural

environment. This research provides an in-depth insight into the user’s views. Ethnography methods

include direct observation, video recordings, photography and artifact analysis such as devices that a

person uses throughout the day. For this, various banks, financial services providing institutions and

insurance companies will be visited personally to know the status of AI adoption and performance of

sector after adoption resulting in addressing the management of AI.

 In-Depth Interview: The managers, technical heads and users of the technology will be interviewed

about issues in management of AI.

 Nominal group technique (NGT): The nominal group technique is an efficient means for making

complex decisions. NGT was developed based on research that indicated that people came up with

more and better ideas working silently in a group than when they worked alone. In NGT, a systematic

and an organized group meeting held among the participants to facilitate decision making by properly

identifying the problems and generating the solutions thereof. Ideally, 10-20 participants give their

ideas by writing on slips. There is no interaction among them which provides an equal opportunity to

everyone to express their views. The objective of nominal group technique is to resolve the opinion

conflicts among the group members and to avoid domination by some individuals as the ideas are

generated independently by the participants. Firstly, a facilitator explains the issue to the experts and

then each one has to jot down the points. The ideas are then recorded, displayed in front of everyone

and discussed. Once all the participants have written their ideas, a clear list of all ideas is prepared. In

last participants vote for prioritizing the ideas.

 Delphi Method: Delphi method is a systematic method used to gather opinions of panel experts on

the problem being encountered, through a series of iterative questionnaires, with a goal of coming to a

group consensus. All the anonymous opinions received through first round are presented to the same

28
 experts for review purpose and the experts are required to give justification for the answers given in

the first questionnaire and on the basis of it, the revised questionnaire is prepared and is again sent to

the same group of experts. The objective of a Delphi technique is to reach to the most accurate answer

by decreasing the number of solutions each time the questionnaire is sent to the group of experts, and

this process continues until the issues are narrowed, responses are focused, and the consensus is

reached. Delphi method will be used when face-to-face interaction is either not possible or not

desirable.

 Interpretive Structural Modeling (ISM): ISM is a well-established methodology for identifying

relationships among specific items, which define a problem or an issue. The ISM process transforms

unclear, poorly articulated, complex mental models of systems into visible well defined simpler

models. It starts with identification of variables, which are relevant to the problem or issue. Then a

contextual relation is chosen and a structural self-interaction matrix (SSIM) is developed based on

pair wise comparison of variables. The experts’ responses help in preparing the SSIM. Based on the

various matrices data, a graphical depiction (digraph) is derived which provides information about

hierarchy between the sub-elements in an element. It provides a clearer picture and an understanding

of contextual relationships among elements.

 Analytic Hierarchy Process (AHP): The Analytic Hierarchy Process (AHP) is also an effective tool

for dealing with complex decision making, and may aid the decision maker to set priorities and make

the best decision. By reducing complex decisions to a series of pairwise comparisons, and then

synthesizing the results, the AHP helps to capture both subjective and objective aspects of a decision.

In addition, the AHP incorporates a useful technique for checking the consistency of the decision

maker’s evaluations, thus reducing the bias in the decision making process.

 Decision Making Trial and Evaluation Laboratory (DEMATEL): DEMATEL is considered as an

effective method for the identification of cause-effect chain components of a complex system. It deals

with evaluating interdependent relationships among factors and finding the critical ones through a

visual structural model.

29
 Fuzzy Consideration: The classical or basic set theory either wholly includes or wholly excludes any

given element. It allows the membership of elements in the set in binary terms, a bivalent condition -

an item either belongs or does not belongs to the set. However, in real life situations, certain sets have

imprecise boundaries. In fuzzy set theory, elements have degree of membership. It permits gradual

assessment of membership of elements in a set describe with the aid of described with the aid of a

membership function valued in the real unit interval [0.1]. Fuzzy considerations are incorporated in

the techniques of systems analysis as well. In addition to the consideration of interaction between

elements and sub elements (binary relationships), it also considers various levels of the possibilities of

interaction. This is done for ISM, AHP and DEMATEL, and then it is referred to as Fuzzy ISM,

Fuzzy AHP and Fuzzy DEMATEL.

7.3.7 Phases of Proposed Study

Literature Review and Ethnographic Study

Phase I

Enabler and Barriers Identified

Workshops with
NGT, ISM, AHP and Domain Experts
DEMATEL

Phase II

ISM DEMATEL AHP

Digraph Plot Output

Phase III Interpretation and Comparison of Results

Causal Loop Diagram and System Dynamic

Phase IV Modelling

Figure 2: Different Phases of Research Work (Resource: Researcher's own construct

30
Phase I: Study of the Present Status of AI in Financial Services

It will include the field survey of companies providing financial services which have adopted or

planning to adopt AI to the present status of AI technology in this sector and depth interview of

managers, employees and users to know their management strategies for AI and its enablers and

challenges what they face in managing AI.

Phase II: Using System Sciences to Find Optimal Solution of the Problem

 In this NGT workshop will be conducted to identify the enablers and challenges of AI.

 Various tools like ISM, AHP and DEMATEL will be applied in the series of workshops on the

factors identified in NGT.

 The technique of fuzzy satisfaction criterion for different strategies may be used to remove the

uncertainty and finding optimal strategy.

Phase III: Interpretation and Comparison of Results will be done on the statistically analysed data

derived from different workshops.

Phase IV: Developing Dynamic Optimal Strategy

A causal loop diagram would be developed on the basis of the discussion or workshop session with

domain experts. System Dynamic Modelling will be used for simulation.

In addition to the above, the researcher may explore the use of related techniques like system dynamic

modeling, Structural Equation Modelling (SEM), Artificial Neural Network (ANN), etc. If

appropriate, Regression Analysis can also be adopted.

SECTION 8: PROPOSED CHAPTERIZATION

The proposed research will contain the following chapters:

 Chapter 1: Introduction

 Chapter 2: Review of literature

 Chapter 3: Research design and methodology

 Chapter 4: Data collection and analysis

 Chapter 5: Results and interpretations

 Chapter 6: Conclusion, Managerial implications, and direction for future research

31
  Bibliography and references

 Appendix

References

1. Accenture (2017). Rewire for Growth: Accelerating India’s Economic Growth with Artificial

Intelligence. Retreived September 1, 2019 from Accenture:

https://www.accenture.com/t20171220t030619z__w__/in-en/_acnmedia/pdf-68/accenture-rewire-

for-growth-pov-19-12-final.pdf%20-%20zoom=50

2. Accenture (2018). Redefine Banking with Artificial Intelligence. Accenture.

3. Adobe (2019. Digital Trends: Financial Services in Focus. Adobe Systems Incorporated.

4. Agarwal, A., Gans, J., and Goldfarb, A. (2018). Economic Policy for Artificial Intelligence.

National Bureau of Economic Research, 139-159, Toranto: University of Chicago Press.

5. Angerhofer, B., and Angelides, M. (2000). System dynamics modelling in supply chain

management: research review. WSC '00 Proceedings of the 32nd conference on Winter

simulation, 342-351, Florida: The Society for Modelling and Simulation International.

6. Attri, R., Dev, N., and Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: An

Overview. Research Journal of Management Sciences, 2 (2), 3-8.

7. Banwo, A. (2018). Artificial intelligence and financial services: Regulatory tracking and change

management. Journal of Securities Operations and Custody, 10 (4), 354-365.

8. Baruah, A. (2019). AI Applications in the Top 4 Indian Banks. Retrieved August 18,2019 from

Emerj: https://emerj.com/ai-sector-overviews/ai-applications-in-the-top-4-indian-banks/

9. Celik, A. E., and Karatepe, Y. (2007). Evaluating and forecasting banking crises through neural

network models: An application for Turkish banking sector. Expert Systems with Applications,

33 (4), 809-815.

32
10. Chan, M.-C., Wong, C.-C., Tse, W., Cheung , B.-S., and Gordon , Y.-N. (2002). Artificial

Intelligence in Portfolio Management. 403-409, Verlag Berlin Heidelberg : Springer.

11. Chatterjee, A., Ayadi, O., and Boone, B. (2000). Artificial Neural Network and the Financial

Markets: A Survey. Managerial Finance, 26 (12), 32-45.

12. Dhanrajani, S. (2019). How artificial intelligence is changing the face of banking in India. Your

Story Media Pvt. Ltd.

13. Dravis, P. (2018). Artificial Intelligence in Finance The Road Ahead. San Fransusco, CA: Future

Perfect Machine.

14. Fethi, M. D., and Pasiouras, F. (2010). Assesing bank efficieny and performance with operational

research and artificial intelligence techniques: A survey. European Journal of Operational

Research, 189-198.

15. Financial Stability Board. (2017). Artificial intelligence and machine learning in financial

services Market developments and financial stability implications. Financial Stability Board.

16. Gauvrit, P. (2017). How AI can deliver a personalized and predictive customer experience.

Retrieved August 1A 9, 2019, from Digital CX Conversations by Eptica:

https://www.eptica.com/blog/how-ai-can-deliver-personalized-and-predictive-customer-

experience

17. Goudarzi, S., Hickok, E., and Sinha, A. (2018). AI in Banking and Finance. The Centre for

Internet and Society, India.

18. Gupta, N. (2017). A Literature Survey on Artificial Intelligence. International Journal of

Engineering Research and Technology (IJERT), 5, 1-5.

19. He, D., Guo, M., Zhou, J., and Guo, V. (2018). The Impact of Artificial Intelligence (AI) on

Financial Job Market. The Boston Consulting Group. China: China Development Research

Foundation.

33
20. Ince, H., and Aktan, B. (2009). A comparison of data mining techniques for credit scoring in

banking: A managerial perspective. Journal of Business Economics and management, 10 (3),

233-240.

21. Ivanov, S. H., Webster, C., and Berezina, K. (2017). Adoption of Robots and Service

Automation by Tourism and Hospitality Companies. Revista Turismo and Desenvolvimento,

27/28, 1501-1517.

22. Jackson, M. (2017). How artificial intelligence can deliver a personalised banking experience.

Retrieved August 19, 2019, from Finextra: https://www.finextra.com/blogposting/14617/how-

artificial-intelligence-can-deliver-a-personalised-banking-experience

23. Kannabiran, G., and Dharmalingam, P. (2012). Enablers and inhibitors of advanced information

technologies adoption by SMEs. Journal of Enterprise Information Management, 25, 186-209.

24. Keys, P. (1990). System dynamics as a systems-based problem-solving methodology. Systems

Practice, 3 (5), 479-493.

25. Kuo, C.-M., Chen, L.-C., and Tseng, C.-Y. (2017). Investigating an innovative service with

hospitality robots. International Journal of Contemporary Hospitality Management, 19 (5),

1305-1321.

26. Masayuki, M. (2016). The Effects of Artificial Intelligence and Robotics on Business and

Employment: Evidence from a survey on Japanese firms. RIETI Discussion Paper Series 16-E-

066. Japan.

27. Moghaddam, A. H., Moghaddam, M. H., and Esfandyari, M. (2016). Stock Market Index

Prediction using Artificial Neural Network. Journal of Economics, Finance and Administrative

Science, 21, 89-93.

28. Moudud-Ul-Huq, S. (2014). The Role of Artificial Intelligence in the Development of

Accounting Systems: A Review. IUP.

34
29. Narain, K., Swami, A., Srivastava, A., and Swami, S. (2019). Evolution and control of artificial

superintelligence (ASI): a management perspective. Journal of Advances in Management

Research .

30. Narrative Science (2015, June 11). 80 Percent of Business and Tech Leaders Say Artificial

Intelligence Creates Jobs, Increases Productivity. Retrieved August 21, 2019, from Business

Wire: https://www.businesswire.com/news/home/20150611005252/en/80-Percent-Business-

Tech-Leaders-Artificial-Intelligence

31. NASSCOM. (2018). Artificial Intelligence for Banking, Finanacial Services and Insurance

Sector. NASSCOM-CMR.

32. North, R. (2019). How AI Helps Banks and Financial Service in Improving Their ROI. Retrieved

August 18, 2019, from Enterprise Edges: https://www.enterpriseedges.com/ai-helps-banks-

financial-service-improving-roi

33. O'LEARY, D. E. (1991). Artificial Intelligence and Expert Systems in Accounting Databases:

Survey and Extensions. Expert Systems With Applications, 3, 143-152.

34. Omar, S. A., Hasbolah, F., and Zainudin, U. M. (2017). The Diffusion of Artificial Intelligence

in Governance of Public Listed Companies in Malaysia. International Journal of Business,

Economics and Law, 14( 2), 1-9.

35. Patel, M. (2018). How AI-powered Mobile Banking App Enhances Customer Experience.

Customer Think.

36. Perwej, Y., and Perwej, A. (2012). Prediction of the Bombay Stock Exchange (BSE) Market

Returns Using Artificial Neural Network and Genetic Algorithm. Journal of Intelligent Learning

Systems and Applications, 4, 108-119.

37. PwC. (2016). Financial Services Technology 2020 and Beyond:Embracing disruption, PwC.

35
38. Rijmenam, M. v. (2019). How Artificial Intelligence Will Disrupt the Financial Sector. Retrieved

August 17, 2019, from Datafloq: https://vanrijmenam.nl/artificial-intelligence-disrupt-financial-

sector/

39. Rouse M. (2010). AI in IT tools promises better, faster, stronger ops. Retreived August 17, 2019,

from TechTarget: https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence

40. Roy, A., and Kant, A. (2018). National Strategy for Artificial Intelligence #AIFORALL. India:

NITI AYOG.

41. Rupali. (2018). AI for Banking, Insurance, and Financial Services. Retrieved August 23, 2019,

from DZone: https://dzone.com/articles/artificial-intelligence-for-banking-insurance-and

42. Sapena, O., Botti, V., and Argente, E. (2003). Application of neural networks to stock prediction

in “pool” companies. Applied Artificial Intelligence,17(7), 661-673.

43. Schierz, P., Schilke, O., and Wirtz, B. (2010). Understanding consumer acceptance of mobile

payment services. Electronic Commerce Research and Applications, 9 (3), 209-216.

44. Schultebraucks, L. (2017). A Short History of Artificial Intelligence. Retreived from

https://dev.to/lschultebraucks/a-short-history-of-artificial-intelligence-7hm

45. Sharma, A. (2017). Punjab National Bank To Use AI To Detect Frauds. India: Analytics India

Magazine.

46. Shiau, S., Huang, C.-Y., Yang, C.-L., and Juang, J.-N. (2018). A Derivation of Factors

Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education.

Sustainability,10, 1-29.

47. Sinclair, A., Brashear, J., and Shacklad, J. (2018). AI: The Momentum Mindset. Accenture.

48. Sushil. (2018). Flexible Systems Methodology: A Mixed-method/Multi-method Research

Approach. Global Journal of Flexible Systems Management, 19(2), 109-110.

36
49. Talukder, M. (2012). Factors affecting the adoption of technological innovation byindividual

employees: An Australian study. Procedia - Social and Behavioral Sciences, 40, 52-57.

50. Towill, D. R. (1993). System dynamics—background, methodology, and applications. Part 1:

Background and methodology. Computing and Control Engineering Journal. 4(5), 201-208.

51. Uyttendaele , J., and Muylle, S. (2018). Artificial Intelligence in Corporate Banking: A Closer

Look at the Potential Impact on E-Business Processes. Belgium: Gent University.

52. Venkatesh, V., and Davis, F. (2000). A Theoretical Extension of the Technology Acceptance

Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.

53. Vijai, C. (2019). Artificial Intelligence in Indian Banking Sector: Challenges and Opportunities.

International Journal of Advanced Research (IJAR), 7(5), 1581-1587.

54. Yeoh, P. (2019). Artificial intelligence: accelerator or panacea for financial crime?. Journal of

Financial Crime, 26(2), 634-646.

55. Yu, K.-H., Beam , A., and Kohane , I. (2018). Artificial intelligence in healthcare. Nature

Biomedical Engineering, 2, 719-731.

56. Zhou, A. (2017). Banks Eager For Artificial Intelligence, But Slow To Adopt. Retrieved August

01, 2019, from Forbes: https://www.forbes.com/sites/adelynzhou/2017/06/30/financial-services-

industry-banks-artificial-intelligence-slow-adoption/#1d5c1b6b4f8b

57. ZorBari-Nwitambu, B. (2017). Positive Word of Mouth and Profitability: The Experience of.

International Journal of Managerial Studies and Research (IJMSR), 5(5), 2-48.

Bibliography

58. Checkland, P. B. (1999). Systems Thinking, Systems Practice. Chichester, UK: John Wiley and

Sons Ltd. ISBN 0-471-98606-2

37
59. Wilson, B. (2001), Soft Systems Methodology Conceptual Model Building and its Contribution,

Chichester,UK: John Wiley and Sons Ltd. ISBN 0-471-89489-3

60. Saaty, T.L., 1980. The Analytic Hierarchy Process, McGraw-Hill, New York.

38