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ENVISION – International Journal of Commerce and Management

ISSN: 0973-5976 (Print), 2456-4575 (Online)
VOL-16, 2022

Online Banking Frauds and Necessary Preventive Measures

 Dr. Kamal Nain Sharma, Ankit Kala
ABSTRACT

Banking Industry has undergone digital disruptions at a massive level. Online banking, mobile
apps and E-bill payments have become a norm. Banking tractions are digitized from back end
to front end and digital transactions are made without any human intervention. The best part
is that it is speedy but is it reliable every time? Due to some ignorance or silly mistakes online
transactions easily fall into the trap of cyber criminals. This study has been undertaken to
analyze online banking frauds such as phishing, smishing, card fraud, etc., and also the causes
behind it. The bank staff has been interviewed to understand their perspective on these frauds
and whether they have a role to play in such fraudulent activities? Lastly, an attempt has been
made to construct a model of the fraud detector system and consequently the preventive
methods that can help an individual to stay safe from online frauds.

Keywords: Banking Frauds, Scams, Finance, Behavior, Detection of Frauds

1. Introduction
With the ever-increasing demand of people and thus increasing advancements in technological
sectors, we see many illegal activities happening around us. One such activity that has been
referred to quite a few times is Online Bank Frauds, often spoken as E-banking frauds. There
is a long list of illicit acts that occur under the name of these frauds and in our paper we will
be giving an insight about what are the major causes of these frauds and what could be the
subsequent cure or prevention measures for keeping ourselves safe from such illegal intentional
acts. It is indeed a fact that eradicating banking fraud is a tedious job, but as always prevention
is better than cure. (Ram, T., & Acharya, K., 2021).
1.1 Definition
Banking fraud is a term used for acquiring money or any financial possession from financial
enterprises such as Commercial Banks, Investment Banks, Insurance companies, etc. by
unlawful means. The person who carries out illegitimate deceit is referred to as a scammer,
swindler, perpetrator, fraudster, and many more. This law-breaking activity not only hampers
the economy of the institution but the nation as a whole.
Many skillful researchers believe that the causes, as well as checks of a financial misdemeanor,
are contra-position of the same coin. While in several instances it is considered an offence yet


Associate Dean Research, LKCTC, Jalandhar, Punjab, India, kamalnain3@gmail.com

Masters in Computer Applications, Lyallpur Khalsa College Technical Campus, Jalandhar, Punjab, India,
ankitkala36@gmail.com

Page | 50
Online Banking Frauds and their Necessary Prevention Measures

sometimes it might be referred to as a white-collar crime that implies activities or schemes

enabling bank theft. (Ram, T., & Acharya, K., 2021).
Figure 1
Emerging Banking Frauds in India

Source: -- business-standard.com
1.2 The known types of banking fraud
Though there are many different types of financial fraud, we will go ahead with the most
prominent and well-known ones in our study. In our research, we will examine the artifices
first from the customer's point of view and then from the institution's point of view.
Figure 2
Types of banking frauds

(Source: - facebook/oliveboard.com)
1.2.1 Phishing and Spear Phishing
This is the first and foremost attack that compromises the banking credentials of a customer.
Phishing is usually attempted through a small email seeking confidential data, but most of these
are avoidable these days due to repetitive awareness messages from banks themselves.
Spear phishing is also a targeted version of phishing attacks and they are commonly sent by a
trusted source for instance a close relative. It is still advised not to open any link associated
with such emails to secure oneself from Trojans and malware.

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Online Banking Frauds and their Necessary Prevention Measures

1.2.2 Spoofing and Vishing

Website spoofing has become an easy job as every other person is learning to code, not
necessarily for scamming others but there are certain occasions where people are lured by a
clone website of big companies say Amazon, and prompt you to enter your bank details to
receive your freebie.
Vishing is again targeted towards confidential details such as user id, login, OTP, CVV, and
card pin over a call claiming to be from the customer’s associated bank.
1.2.3 Skimming and Smishing
Skimming involves stealing information through the magnetic strip present on the ATM card.
The skimmer first scans the credit/debit card and stores its information, sometimes tiny
cameras are also used in public locations such as shops, and restaurants to capture card pins.
Smishing on the other hand is a combination of SMS and phishing, again to obtain personal
information. It is advised not to open any link sent in the message box through a toll-free
number to avoid this type of fraud.
1.2.4 SIM Swap
Under this scheme, the scammer first issues a new sim card on the customer’s current registered
mobile number thus receiving all the alerts and OTPs that he requires to easily perform any
banking transaction from the customer’s bank account.
1.2.5 Credit card fraud
Credit card fraud is a combined term used for all types of scams perpetrated with the help of
debit and credit cards. The main reason behind doing so is to obtain services or to pay bills to
another account associated with a criminal (Wei, et al., 2013). Around €1.6 billion have been
remotely vanished because of this particular fraud last year. 127 million people from the USA
have accepted being the victim of credit card theft.
1.2.6 Computer Intrusion
This type of attack occurs when the attackers try to access the entire computer system or a
major part of it often referred to as hackers. These intruders used automated computer programs
for breaching the computer security (Wei, et al., 2013).
Hence it is advised to ensure a safe system by deploying good antivirus software. Also, data
mining techniques now-a-days are very helpful in the detection of computer intrusion attacks.
1.2.7 Telecommunication fraud
Telephony is the oldest massive network encompassing 48% share and hence attackers exploit
it very well to gain its benefits. Telecom fraud includes activities directed to gain an advantage
over telecom companies.
Many deceptive practices and frauds have been evident so far namely “IRSF fraud” abusing
phone rates and “Interconnect Bypass fraud” abusing operator call rates.

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Online Banking Frauds and their Necessary Prevention Measures

1.3 Causes of Banking Fraud

It is not necessary to state that there is no single person responsible for the attack or fraud
committed on a financial institution other than the fraudster. There are many substantial
reasons, which contribute to the entire process. We are stating all the reviews that we have
come across while surveying people at the bank and even the staff at the bank premises. The
prominent causes are mentioned hereunder:
1.3.1 Poor internal control
Several times it has been observed that the scams are a due result of the lack of diligence of the
banking staff for disbursing loans, more problematic is that this occurs both before and after
the disbursal. This clearly shows that low levels of checking and balancing may in turn cause
the banking system to fall under the umbrella of fraud.
1.3.2 Poor knowledge of advanced computing
This can also be called technological backwardness in the financial firms, when maximum
dependence is given to the manual work instead of using advanced computing resources, it
becomes impossible for the untrained staff to supervise huge volumes of banking transactions,
increasing the thefts and lowering the fraud detection rate. Hence, it is of utmost importance
for financial institutions to train the staff well and equip secure computing resources for
controlling the transactions (Khanna, A., & Arora, B., 2009).
1.3.3 Lack of adequate monitoring by the managers
As the subordinates must work well, the same applies to the senior staff i.e. managers of the
bank, it is essential to emphasize fraud monitoring systems. These days there are systems
available to monitor the credit flow too, but this again creates risks including market risks and
operational risks which in turn leads to breaches. Hence it is necessary for the head of the
branch to strictly adhere to the process and help the internal auditing team in the detection of
any fraudulent behavior.
1.3.4 Unethical behavior at the workplace
Every workplace or firm has become stressful and competitive over time, this includes the
financial firms too. Employees feel demotivated due to underpaying, lack of appreciation and
often negative reinforcement which leads to unethical practices down the line. Some other
reasons could be nepotism in the internal committee causing a breakdown of the moral fiber of
Indian banks.
1.3.5 Embezzlement Schemes
If we categorize individuals working in a firm, there is a class of employees that are
“opportunist” type. It is often seen that this class of employees perceive the internal problems
of the firm very well and then use this slippage to their advantage causing embezzlement or
money laundering.
2. Literature Review
The trend of internet usage has touched its peak because smartphones have truly provided
customers with digital delights of sharing, transferring, shopping, and receiving funds without

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Online Banking Frauds and their Necessary Prevention Measures

any physical visit to the bank. However, as a bane customers are in deep concern about their
privacy and security due to alarming cybercrimes, particularly bank frauds. According to a
survey conducted by cybercrime cells, around 1.5 trillion dollars are intruded every year
worldwide. Interestingly 60% of these crimes are cropped through smartphones. Studies
indicate that there is an immediate need for the detection of these threats. Thus, usage of
cybercrime avoidance and prevention tools is very vital for abating e-bank frauds. One more
surprising fact that arises from the studies is the excessive use of Trojans (16.98%) and
malware (14.55%) accompanied by social engineering practices for conducting bank frauds
(Ahmad, I. et al., 2021).
Even though advanced security systems are applied but there are several cases in which they
have failed drastically. Hence security systems must be backed up with a fraud detection system
for identification of any unauthorized activity but the major problem behind the application of
the detection system is that they must be continuously active 24/7. (Kovach, S., 2011).
Some common characteristics of fraud have been noted down by empirical analysts. It
includes multiple accounts being accessed by a unique person i.e. fraudster; Small value
transactions occur frequently; Payment transactions exceed the daily transaction limit of a
customer and repetitive use of forgotten password before large money transaction. These
sophisticated traits are widely discussed. Skeptical customers are most active in controlling
banking fraud activities. With time advanced data mining algorithms can detect diverse
behavior patterns along with imbalanced transactions. This has made it comparably tougher for
fraudsters to penetrate the system for a longer time (Wei, W., et al., 2013).
The Trojans are majorly responsible for the attacks; we now clearly visualize how algorithms
can detect a Trojan from a genuine customer by using the time stamping mechanisms.
Table 1
Fraudster accessing the portal
Time Page Link
21:55:42.190 Login.aspx
21:55:43.260 BalanceCheck.aspx
21:55:43.890 PayForm.aspx
21:55:44.121 PayConfirm.aspx
21:55:45.091 HomePage.aspx
(Source: Wei, W., et al., 2013)

Table 2
Genuine customer accessing the portal
Time Page Link
21:58:06.190 Login.aspx
21.58.07.391 HomePage.aspx
21:58:15.260 BalanceCheck.aspx
21:58:27.890 PayForm.aspx
21:59:22.121 PayConfirm.aspx
21:59:27.091 Print.aspx
21:59:32.091 HomePage.aspx
(Source: Wei, W., et al., 2013)

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Online Banking Frauds and their Necessary Prevention Measures

The biggest difference that we come across is that Trojan transactions are way too speedy as
compared to genuine transactions and homepage of the portal is not visited to save time (Wei,
W., et al., 2013).
Many differential analysis algorithms detect these mentioned attributes in a deployed fraud
detector system. Even the Reserve Bank of India (RBI) has issued critical guidelines to all
financial firms that are prescribed to be followed with strict adherence.
Other six security controls are; internal control, card management, deposits received,
passbooks, advances and loans, and handling internal and inter-branch accounts (Khanna, A.,
& Arora, B, 2009).
Since now, we have come across all the perpetrator cases, it is equally important to gather some
evidence that weakens the internal control system of banks to exploit e-banking. This includes
personnel threats where bank officials themselves collaborate with the fraudsters to gain
benefits.
3. Preventive Methodology
To prevent frauds in banking transactions, two major things are to be considered. Firstly, ensure
tight security practices and use of IT especially data mining shall be emphasized for detecting
frauds. Secondly, training of bank employees shall be the top priority so as to detect and prevent
the frauds at the early stage. A brief design and layout has been given about a fraud detector
system below:
Figure 3
A simpler model of fraud detection system

(Source: Kou, Y., et al., 2004)

The above model is a simple architecture proposed by differential analysts. Every device
performing banking transactions is subjected to a unique identity and a set of counters is used
to measure the transactions. A series of patterns are obtained that predict the unusual behavior
from any identified account and whenever unauthorized activity is notched an alarm is
triggered (Kovach, S., 2011).

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Online Banking Frauds and their Necessary Prevention Measures

Another important detection system that we came across in our study is more complex as
compared to the previous one. This model implies a cost-sensitive neural network for the
classification of genuine traits and fraudulent characteristics. Also, decision forests (a
combination of varied decision trees) are done in a cascading manner for accomplishing the
goal (Alotibi, E. M., 2019)
Figure 4
Detailed Fraud detection system model generation

(Source: Wei. W., et al., 2013)

Data is found and connected via the database tier. Online banking fraud detection information
comes from various sources, including real-time transaction logs, recent and historical
transaction data, consumer demographic data, and other external sources. Data types and
formats vary by source. The pre-processing tier collects real-time transactions, maintains
historical data and prepares information for model training and prediction. It also selects and
derives essential traits. For real-time fraud detection, too many features reduce model
efficiency. This model calculates the significance of a characteristic to each class based on its
information gain ratio between the two types and selects those with greater ratios.
Model generation, parameter setting, task scheduling, and retraining are done in the modelling
tier. System data mining uses three methods: Using a cost-sensitive neural network to
emphasize the higher cost of misclassifying a con compared to a real transaction and a decision
forest, which weights individual decision trees by cascading and sharing, to create decision
tree ensembles, we find that banking activity is strongly associated with online banking fraud.
4. Conclusion
Security, as well as privacy issues, could be dealt with to a greater extent by the use of global
counters, global analysis, and differential analysis. The likelihood of fraudsters accessing the

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Online Banking Frauds and their Necessary Prevention Measures

portal could also be detected beforehand and the overall threat could be minimized. Dempster’s
rule initiated with the above inputs will yield a suspicion score of every fraud transaction and
will be used for future transactions as well.
Other mechanisms can be adapted along with data mining principles namely pattern mining,
neural networks, and decision forest. This proves that if IT brings to us cybercrimes, it also
brings us the prevention tactics for securing ourselves from such crimes. Finally, technology
administration risks tend to decrease with organizational flexibility, staff training and
awareness, and support from the top officials to provide a transparent, satisfying working
environment based on legal and ethical values. These are perceived inputs for reducing
collision among employees so as to reduce banking frauds.
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