Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
185
ARTIFICIAL INTELLIGENCE APPLICATIONS IN BANKING SECTOR
1
Dr Supriya Mahesh Lakhangaonkar,
2
Dr Kavita Keshav Kamath
1
Assistant Professor, Commerce & Management Dept., Vishwakarma University, Pune, Maharashtra, India.
2
Assistant Professor, RMD Sinhgad School of Management Studies, Pune, Maharashtra, India.
Abstract:
Indian banking industry has witnessed a remarkable change in recent times. Modern banking has included technology in
various aspects of business - like consumer expectancies, regulations, economic changes and off course the constant competition.
Digital interface, mobile and internet banking, and payments bank have transformed this sector in a considerable way. In recent
times, Technology is being implemented for front as well as back-end banking processes. This is possible mainly because of the use
of artificial intelligence in various banking operations like fund transfer, credit scoring, wealth and risk management, fraud detection
etc. This study seeks to map the present state of use of Artificial Intelligence in the banking sector. The study is divided into four
parts – 1. Fundamental Technologies in AI 2. Scope of AI technologies in banking functions 3. Impact of AI technologies in banking
operations and 4. Importance of AI in banking sector. The study first offers an overview of a series of fundamental and interrelated
technologies around machine learning and natural language underpin of AI. It also elaborates the kinds of AI used in different banking
operations followed by the ways in which AI is being implemented in these operations. It includes analytics, bots, robotic process
automation (RPA) and report generation. The study focuses on AI in banking operations like front, middle, and back-office work
(either customer-facing or employee-facing). The study also discusses about how, where, and why AI applications are critical in
banking and also lists its pros and cons.
Key Words:
Artificial Intelligence, Banking Sector, Banking Operations, AI applications.
Article History
Received: 17/01/2021; Accepted: 15/03/2021
Introduction:
Artificial Intelligence:
Artificial intelligence (also called as AI) is the field of technology and science. AI mainly focuses on developing the abilities of
modern operation systems to resolve issues using human-like complex skill set like reasoning, learning and self-correction.
Application of artificial intelligence is wide-ranging, it is applied in the field of optical communication, network planning,
programming, transportation networks, medical science etc. Artificial intelligence systems use various complex algorithms that
communicate at high speed and designed with superior decision-making capabilities.
There are many terms and definitions in AI that makes it hard to explore the difference between categories, subsets, or variety of AI
and each one of them are different ideas. Some subsets of AI include Machine Learning, big data and
Neuro-Linguistic Programming
(NLP). However, in this research article the researcher has considered the four main types of AI: reactive machines, restricted
memory, mind theory, and self-awareness.
Fig. 1.1 Main Types of AI
•Has no Memory
•Only react to different stimuli
•Ex. - Chess playing Supercomputer.
1. Reactive
•Uses memory to learn and improve its
response.
•Ex. - Self Driving Car
2. Limited Memory
•Understands the need of other intelligent
entries.
•Ex. - A robot equipped by Theory of Mind
AI understands the intent of another similar
robot.
3. Theory of Mind
•Has human like intelligence and self
awareness.
4. Self - Aware
Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
186
(Source – Secondary Data)
Artificial Intelligent in Banking Sector:
Technological adaptations in the banking sector have been gradual as compared to technological adoption in other sectors. One of
the crucial reasons behind this is the high dependence of banking industry on human involvement. With time, technological
inventions were adopted in day-to-day working of banks and has efficiently improved their performance. Application of artificial
intelligence in banking sector can make the operations more impactful and hassle free. Some of the areas in banking sector, where
artificial intelligence can be successfully applied are listed below –
Tailored Financial Services:
Artificial intelligence can aid in analysing stock markets and provide recommendations as per
financial goals of individual customers.
Underwriting:
Automation of this process is possible with the use of artificial intelligence services that can use unstructured
information in the decision-making process.
Voice Aided Banking:
Language processing using artificial intelligence can reduce human error and improve efficiency of the
process.
Data-Driven Loaning Decisions:
Artificial intelligence systems’ assistance in analysing huge chunks of data, performing
calculation and prediction capabilities can aid in developing strategies in financial loaning department for better results.
Digitalisation of Branches:
The lengthy process of banking can be replaced complete digitisation of documents by developing a
comprehensive platform using artificial intelligence.
According to Accenture’s recent Accenture Banking Technology Vision 2018 Report, 83% of Indian bankers believe that AI will
work alongside humans in the next two years — a higher than the global average of 79%. “93% bankers in India said they increasingly
use data to drive critical and automated decision-making. More partner-supplied customer data means a higher degree of
responsibility for banks. Yet, 77% Indian bankers agree that most firms are not prepared to confront impending waves of corrupted
insights from falsified data.
Research Objectives:
1.
To study fundamental technologies in AI.
2.
To study scope of AI technologies in banking functions.
3.
To study the impact of AI technologies in banking operations.
4.
To study the importance of AI in banking sector.
Literature Review:
Ayushman Buruah (2020),
discussed that the applications of AI and ML in data analytics and customer service create the
opportunity for exponentially more personalized and faster customer experiences, significantly better insights, and, automation of
back-end workflows. More than 36 percent of large financial institutions are already investing in these technologies, and almost 70
percent report that they are planning to in the near future. The researcher explored that the AI applications are in use in the leading
commercial banks in India viz. State Bank of India, HDFC, ICICI and Axis. In these four banks various AI technologies have been
introduced successfully. From a customer chatbot perspective, SBI has launched SIA. HDFC Bank has developed an AI-based
chatbot, “Eva” (which stands Electronic Virtual Assistant). ICICI Bank, has deployed software robotics in over 200 business
processes across various functions of the company. Axis Bank has launched an AI & NLP (Natural Language Processing) enabled
app, Conversational Banking, to help consumers with financial and non-financial transactions, answer FAQs and get in touch with
the bank for loan other products.
Navleen Kaur, Supriya Lamba Sahdev, Dr Monika Sharma (2020),
stated that artificial intelligence (AI), from time to time called
machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by machines, in contrast to the
natural knowledge demonstrated by humans. Artificial Intelligence in finance is more than about chat bots. Artificial Intelligence has
taken over numerous sectors including banking industry. The principal thought behind this investigation was to comprehend the
impact of AI on present day banking. This research has mainly focused on the concept of AI in the field of banking, how it has
brought revolutionary changes in banking and its impact on human manpower. This research is a preview of the current applications
of AI in the banking industry and how it is changing the face of banking in India.
C. Vijay (2019),
mentioned in his research article that Artificial Intelligence (AI) is fast developing technology in all over the World.
Banks are exploring as well as implementing technology in various ways. Artificial Intelligence is getting better and smarter day by
day. AI is changing business processes and customer-facing services in the banking sector. It is also being used to meet regulatory
compliance, detect fraud, or assess individual creditworthiness. The application of AI pertaining the potential to create more efficient
business processes, offer personalized services, and assist in larger goals such as financial inclusion.
Martin Leo, Suneel Sharma and K. Maddulety (2019),
concluded that
there is an increasing influence of machine learning in
business applications, with many solutions already implemented and many more being explored. Since the global financial crisis,
risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected,
measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and
Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
187
risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse
and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas
or problems in risk management that have been inadequately explored and are potential areas for further research. The review has
shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk
and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk
management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from
the study of how machine learning can be applied to address specific problems.
Ms. Bhavna Agarwal, Dr. Himanshu Agarwal and Dr. Parvez Talib (2019),
indicated that
Banks have been already offering a
wide variety of products and services, integrated with technology and automation, the most familiar being ATM machines all around
us. In the present Industry 4.0 era, Banking sector is all set to amplify its strategy implementation by leveraging latest digital
technologies so that its customers may experience swift and secure processing of transactions. This study examined the evolution,
adoption, implementation and future opportunities of leveraging Artificial Intelligence (AI) for successful strategy implementation
in India‟s banking sector. The researcher tried to investigate the current status of practical application of AI in various aspects of
banking strategies for example surveillance, fraud detection, ensuring compliances, credit assessment, customer service, handling
monotonous voluminous tasks etc. Finally, the researched suggested the banking leadership to shift their focus proactively towards
leveraging AI with an objective to bring in delight for customers.
Amer Awad Alzaidi (2018),
pointed out that Artificial intelligence is cutting-edge technologies that have been forefront in
technological revolution worldwide. Artificial intelligence systems have potential in transforming all operations of banking industry
is seen as (AI) and is received with enthusiasm due to its capability of taking human-like decisions and avoiding human-like errors.
Artificial intelligence has been adopted in some sectors more widely than others, banking sector is amongst the few sectors that had
shown moderate level of acceptance and adoption of this technology.
Dr. Simran Jewandah (2018),
noted that the technology has changed the ways businesses are done and over the last couple of years.
There is a growing need to use Artificial Intelligence (AI) and the Indian banking Sector is gradually shifting itself towards using AI.
The banking sector is adopting the AI has been gradually as compared to other sectors. This can be due to the fact that banking is
still a manpower-led sector, with operations that require human involvement. Yet the Indian Banking sector understands the need to
cut down cost and the expenditure on redundant tasks. The Indian banking sector is exploring the ways by which it can harness the
power of AI to improve the processes and enhance the Customer Service in the long run. The paper tried to explore the areas where
the AI is being used in the Banking Sector and its implication in the top banks in India.
Fundamental Technologies Used In AI:
Fig. 4.1: Fundamental Technologies used in AI
Machine Learning
Analytics
Risk Modeling
Biometrics
Underwriting
Fraud Detection
Chatbots
RPA
Banking Applications
Natural Language
Processing
Generation
Report
Generation
Foundational Technologies in AI
Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
188
(Source – Secondary Data)
Scope of AI Technologies in Banking Operation:
AI must make interpretation, not just decisions. Researcher has examined four specific cases of AI in banking.
Analytics
- Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent
transactions and improve compliance. AI enables banks to manage huge volumes of data at record speed to derive valuable insights
from it. AI-driven analytics test vast quantities of data to search for patterns, groupings, and correlations by employing a wide variety
of techniques, including data mining and hypothesis testing. AI enabled analytics can move from merely descriptive to predictive.
AI algorithm accomplishes anti-money laundering activities in few seconds, which otherwise take hours and days.
Chatbots
- The most visible instance of AI today is the chatbot. A chatbot is a technology service powered by algorithms that
interacts with a customer in a natural (human-like) manner, either by voice or text. Accessible by a bank’s own digital properties,
virtual agents are marketed explicitly to customers as non-human technology tools that will let them interact with the bank better
than call centres.
Robotic Process Automation -
RPA uses a number of techniques to mimic routine human activities automatically, repeatedly,
faster, and more accurately allowing employees to perform higher-value work. In the most basic case, think of any set of routine
bank activities performed by junior-level employees sitting in a cube. They receive inputs (whether on paper or digitally), examine
those inputs, apply a rule to them (with no discretion in this extreme scenario), and then send the output forward to the next step in
the process. RPA bot can free employees from these highly repetitive, manual tasks in a before/after account-opening scenario.
Report Generation
- With AI-powered technologies like natural language generation (NLG), several repetitive, data-oriented
tasks like report generation can be automated for swift results, while duly incorporating current regulations in the financial landscape.
A Quarterly Earnings Report is one of the most important financial statements of the company. Investors assess these statements to
determine the financial health and investment worthiness of the company. The process of gathering information can be quite tedious
and time-consuming. However, that aspect is mostly performed by accounting software. The problem arises in explaining the data in
a manner that every investor understands.
With reporting automation and the use of NLG, companies can easily reduce costs by creating accurate, error-free reports at scale.
NLG is a real game-changer for analytics due to its ability to convert complex data into narrative insights, written in natural language,
and easily understood by everyone.
Impact of AI Technologies in Banking Operations:
There are different ways of examining how AI will support a banking operation. AI has the potential to improve all banking operations
right from front end i.e., customer interaction areas to the middle office and finally the back-office operations. While some argue that
these terms are becoming obsolete, they’re still a useful concept to consider how AI can create business value.
Front Office Operations
- Direct customer interactions can either be addressed by AI directly, most prominently with chatbots or
virtual agents, or by enabling employees to perform their jobs in a better by enabling them to be faster or more accurate or more
efficient. Customer-facing AI can be either one-way, or two-way, where an on-going set of interactions takes place, initiated by either
the customer or the bank. The benefits to customers include improved advice, better offers, and saved time.
Middle Office Operations
-The functions of the middle office include employees supporting other employees, indirectly
supporting customers or conducting compliance activities. Some examples are in report generation, underwriting and credit
decisioning, and risk and compliance monitoring. While middle office activities are generally an effectiveness play, efficiency has a
role, too, as AI technology helps process more work at lower cost.
Back Office
Operations
-Processing and reconciliation, typical back office functions, can use AI to detect anomalies and
exceptions. RPA can serve as a backup and a second set of eyes to make sure that processes are proceeding as they should be. Direct
benefits to customers are hard to imagine in a back-office. A great deal of threat detection and risk mitigation will also take place in
the back office. The biggest risks in back-office implementations lie in over-reliance on AI and in the cost associated with putting
initiatives in place.
Importance of AI Application in Banking Sector:
The main benefit of AI application in banking is cost reduction, but banks shouldn’t ignore risk mitigation and increased revenue.
Cost Reduction -
There are many ways that well-implemented AI can reduce costs. AI may handle many basic inquiries that
nevertheless require some degree of judgment. It may help humans do their jobs better or faster. It can prepare accurate reports that
won’t miss the crucial details.
Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
189
Fig. 7.1: Cost Reduction - Importance of AI application in Banking Sector
(Source – Secondary Data)
Risk Mitigation -
Risk is a huge and complicated issue in banking. Fraud reduction or better underwriting outcomes can be
achieved with the help of AI.
Fig. 7.2: Risk Mitigation - Importance of AI application in Banking Sector
(Source – Secondary Data)
Increased Revenue and Improved Customer Experience -
AI’s ability to increase revenue and improve the customer
experience has been proven. At present, there is more potential to improve the customer experience and marketing activities than
there is in increasing sales directly.
Fig. 7.3: Increased Revenue and Improved Customer Experience - Importance of AI application in Banking
•Handle basic authentication and information requests
before handing off to a live representative.
•Shift common inquiries to bots or virtual agents.
•Move routine task from humans to RPAs.
Shift Task from Humans to AI
•Speed up response times with NLP- driven queris that
respond to the employee.
•Allow customer self- service using NLP, with humans
as fall back.
•Keep humans apprised of latest regulatory changes
.
Augment Humans with AI
•Save time in shifting through data to prepare first draft
of internal reports.
•Pepare prose reports for retail customers.
•Improve accuracy
Use NLG to prepare Reports
•Improve loan underwriting through machine learning.
•Perform comprehensive market risk analyses.
•Run liquidity/ ALM/ stress testing scenarios.
Reduce Financial Risk
•Advance fraud detection.
•Detect suspicious patterns of activity/behavior.
•Improve compliance and controls.
Improve Financial Crime Risk
•Improve accuracy in transcription and production of
documents.
•Spot anomalous activity.
Lessen Operational Risk
•Serve next best offers.
•Target email and other offers.
•Test and learn - Use machine learning to refine process.
Increased Revenue through Offers
•Allows emplyees to focus on highest value tasks.
•Increase productivity of sales representatives.
•Speed up processes though RPA.
Improve Employee Effectiveness
•Use speech analytics to quickly identify cases needing
human attention.
•Reduce false positives.
•Give more attention at a reasonable cost.
Enhance Customer Experience
Research Article (Commerce)
Wesleyan Journal of Research
, Vol.14 No.01(IV)(January 2021)
190
(Source – Secondary Data)
Conclusion:
AI is not new to India. 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. The
adoption of AI has the potential to add nearly $1 trillion to the Indian economy in 2035. AI adoption is still in its budding stages, and
a lot more needs to be done to realise its full potential. There is huge scope for AI in banking operations which can be explored with
time.
References:
Martin Leo & Suneel Sharma & K. Maddulety (2019) "Machine Learning In Banking Risk Management: A Literature
Review," Risks
, MDPI, Open Access Journal, March 2019, Vol. 7(1),
Page No. 1-22
Navleen Kaur, Supriya LAMBA Sahdev, Dr Monika Sharma (2020) “Banking 4.0: -The Influence Of Artificial Intelligence On
The Banking Industry & How AI Is Changing The Face Of Modern Day Banks”, International Journal of Management
, June 2020
11(6),
Page No. 577-585, DOI: 10.34218/IJM.11.6.2020.049
Ms. Bhavna Agarwal, Dr. Himanshu Agarwal And Dr. Parvez Talib (2019) “Application of Artificial Intelligence for Successful
Strategy Implementation in India’s Banking Sector
.” International Journal of Advanced Research, Int. J. Adv. Res. 7(11), ISSN:
2320-5407,
Page No.157 - 166
Dr. Simran Jewandah (2018) “How Artificial Intelligence is Changing the Banking Sector –A Case Study of Top Four Commercial
Indian Banks”,
International Journal of Management, Technology and Engineering, JULY/2018, Volume 8, Issue VII, ISSN NO:
2249-7455,
Page No. 525 - 530
https://emerj.com/ai-sector-overviews/ai-applications-in-the-top-4-indian-banks/
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3661469
https://www.researchgate.net/publication/333055870_ARTIFICIAL_INTELLIGENCE_IN_INDIAN_BANKING_SECTOR_CHA
LLENGES_AND_OPPORTUNITIES
