Fraud Analytics is the process of integration of analytical tools and techniques with human interaction.

1. FRAUD
ANALYTICS
M S M 5 3 3 B U S I N E S S A N A L Y T I
C S & D A T A G O V E R N A N C E
Simran Mondal 1918107
Christ University Bangalore

2. 1. INTRODUCTION
2. IMPORTANCE
3. FRAUD DETECTION & PREVENTION
4. STEPS
5. METHODS
6. TOOLS
7. CRITICISM
8. CASE STUDY
9. REFERENCES
F R A U D A N A L Y T I C S
PRESENTATION
SUMMARY

3. Fraud Analytics is the process of integration of
analytical tools and techniques with human
interaction
In other words, it involves gathering and storing
relevant data and mining it for patterns,
discrepancies, and anomalies. The findings are
then translated into insights that can allow a
company to manage potential threats before they
occur as well as develop a proactive fraud and
bribery detection environment.
What is
Fraud
Analytics?
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F R A U D A N A L Y T I C S
“Organizations lose an estimated 5 percent of their annual revenue to fraud, according to a
survey of fraud experts conducted by the Association of Certified Fraud Examiners.”

4. Why do we need Fraud Analytics?
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Identify Hidden Patterns: Taking advantages of the digital fingerprints and tracing back to the
fraud is one of the advantages of using a Fraud Analytics tool
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Data Integration: Fraud analytics plays an important role in integrating data. It combines data
from various sources and public records that can be integrated into a model.
Enhance existing efforts: Fraud Analytics is one step ahead of the traditional data storage
and retrieval mechanism by integrating data from various sources and files.
Harnessing unstructured data: Fraud analytics helps in deriving the best value from unstructured data.
Unstructured data is the place where more fraudulent activities take place. This is where text analytics
plays an important role in reviewing the unstructured data and preventing fraud from taking place.
Improve the performance: Fraud Analytics is a highly flexible and adaptable process which
can be customized to the needs of your organisation

5. Fraud Detection & Fraud Prevention
Fraud Detection Fraud Prevention
It is the identification of actual or expected fraud to
take place in an organization. Fraud detection in
today’s world involves a comprehensive approach to
match data points with activities to find what is
abnormal
● Fraud detection occurs during the fraud attempt
● The goal of fraud detection is to mitigate fraud
● Sophisticated fraud detection solutions also
reduce false positives which improves the user
experience and increases the productivity of fraud
teams
It is the implementation of a strategy to detect
fraudulent transactions or banking actions and
prevent these actions from causing financial and
reputational damage to the customer and financial
institution
● Fraud prevention occurs before the fraud
attempt
● The goal of fraud prevention is to reduce the
risk of future fraud
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6. Steps to create your Fraud Programme
Create a profile that
includes all the areas
where fraud is expected
to occur and the
possible types of fraud
in those areas.
Measure the risk of fraud
and the overall exposure
to the organization.
Prioritize the risks based
on fraud.
Follow Fraud testing
method to find for
indicators of fraud
in particular areas of
organization
Establish risk
assessment and
decide where to pay
closer attention
Monitor the activity and
communicate it throughout the
organization so that employees
in the organization are aware
about the happening in the
organization
If there is any fraud found
out, inform the
management immediately
to solve out the issue and
to find out why it happened
Fix any broken
controls
Expand the
scope of the
program and
repeat the
process
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7. Methods of Fraud Analytics
CONTINUOUS
ANALYSIS
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Ad-Hoc is nothing
but finding out
fraud by means
of a hypothesis. It
allows you to
explore.
Transactions are
scanned in order
to find any
discrepancies or
fraud
It involves
hypothesis testing
and fraud
investigation
Sampling is the
method of testing
a sample for fraud
rather than the
whole population.
More effective
with larger
population Its main
disadvantage is
that it may not be
able to fully control
the fraud detection
as it takes only
few population
into consideration.
It means creating
and setting up
scripts to run
against big
volume of data to
identify the
frauds as they
occur over a
period of time.
It involves
periodic
notifications and
real time
detection of
fraud.
It helps to find out
frauds that are
not normal.
Calculate
Statistical
parameters to
find out values that
exceed averages of
standard deviation.
Look at high and
low values and find
out the
anomalies(indicators
of fraud) there.
Benford’s
distribution is non-
uniform with
smaller digits more
likely than the
larger digits.
Using Benford’s
law you can test
certain points and
numbers and
identify those
which appear
frequently than
they are supposed
to and therefore
they are the
suspect.
SAMPLING
2. AD-
HOC
3.REPETITIVE/
CONTINUOUS
ANALYTICS
4.
ANALYTICAL
TECHNIQUES
5.
BENFORD’S
LAW

8. Effective tools to minimize fraud based activities
Data analytics & similar technologies are used to help organizations to detect & prevent fraud based
activities Fraud analytics is an umbrella term covering a lot of technologies
1. Business Intelligence:
In the fraud management space, BI can
be thought of as a descriptive
performance reporter. It summarizes
available data to provide business
dashboards and insights to business
leaders and fraud managers so they
can make more informed decisions.
A. Management Information: MI
relates to creating executive
dashboards, data visualisation, data
storytelling and any other reporting
methods.
B. Data Warehousing: A data
warehouse is a large collection of
business data used to help an
organization make decisions.
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9. 2. Data Science:
Data science relates to a set of more sophisticated technologies for performing predictive and
prescriptive analytics Predictive analytics is focused on making predictions about the future of unknown events
(or, in the case of fraud, current events outcomes). Prescriptive analytics relates to choosing the optimal
course of action based on the outcome of those predictions.
A. Artificial Intelligence: AI relates to the computer implementation of human thought processes in a
computerized and efficient fashion.
1. Machine Learning: Machine learning is a subset of AI that relates to the science of algorithms. Machine
learning is a set of numerous algorithmic techniques can be used to extract complex relationships in data
which a human could not find.
2. Deep Learning: Deep learning is a class of machine learning algorithms focused specifically on building
“deep” (multi- layered) neural networks, a form of AI widely used in fraud detection.
Fraud analytics can be defined as a multidisciplinary field that combines numerous
quantitative sciences in order to better understand fraud.
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10. 08
How Data Analytics Can Assist in Fraud Detection
It can handle
massive amount
of data
It can help assess
and improve internal
controls
It reduces sampling
errors.
It helps to revise or
reinforce policies
It can be used
reactively or
proactively
It can track trends and possible
problems substantially faster
than people could, without help
Data analytics in Fraud detection refers to the use of analytics software to identify trends, patterns, spotting
anomalies, and exceptions in data.
● Applying fraud investigation skills to the data analysis results can help identify potential instance for
fraud.
● Data analysis techniques alone are unlikely to detect fraud; human judgment is needed to decipher
results.

11. Challenges Faced by Fraud Analytics
1. Changing Fraud Patterns overtime: fraudsters are always looking to find new and innovative
ways to get around the systems to commit fraud. Hence it is important to update with the
evolved patterns to detect. This results in a decrease in the model’s performance and
efficiency.
2. Class Imbalance: An imbalance in the classification of fraud detection models (that usually
classify transactions as either fraudulent or non-fraudulent) which makes it harder to build
them. The fallout of this challenge is a poor user experience for genuine customers, since
catching the fraudsters usually involves declining some legitimate transactions.
3. Model Interpretations: The models only give a score whether a transaction is fraudulent or
not, without giving any explanation.
4. Quality of the Data: The results from analytics tests can only be as good as the data we feed
in as input.
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12. Case Study: How Fraud Analytics was instrumental in
curbing airline loyalty fraud
Airline travel miles are a coveted loyalty benefit, a gateway for customers to visit places new and old. To an unscrupulous
ticket agent, travel rewards present a good-as-gold opportunity to fraudulently issue miles and then redeem them to book
flights for personal and friends’ use. One major airline recently introduced data analytics into its fraud risk management
processes to identify anomalies, patterns, and trends signalling the potential for fraudulent activity. The analysis honed in
on a variety of data elements, such as number of air miles awarded to customers and agents, flights booked using air
miles, dates of awards, dates of travel, and more. Analysis of these data elements can produce key indicators of potential
fraud, such as:
● Anomaly detection: Were excessive air miles awarded by a single agent or to a single rewards account?
● Predictive classification: Are fraudulently awarded air miles being used to book particular flights?
● Clustering: Are there commonalities in the miles accruing to a rewards account, i.e., the same number of miles every
Wednesday, or the same approving manager sanctioning the awards?
● By leveraging a variety of analytics models: and by testing hypotheses through analysis of combined datasets,
the airline detected fraudulent activity earlier.
Further, by carefully considering the requirements to operationalize fraud risk analytics, and inventorying current tools and
technologies, the company determined that much of what it needed to perform the analytics was already in place, thereby
substantially reducing upfront technology investments.
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13. References
1. https://www.educba.com/fraud-detection-analytics/
2. https://www2.deloitte.com/us/en/pages/advisory/articles/five-insights-into-fraud-risk-analytics.html
3. https://searchsecurity.techtarget.com/definition/fraud-
detection#:~:text=Fraud%20detection%20is%20a%20set,or%20using%20stolen%20credit%20cards.
4. https://www.sas.com/en_in/insights/articles/risk-fraud/fraud-detection-machine-learning.html
5. https://www.crisil.com/en/home/our-businesses/grna/risk-and-analytics/financial-crime-analytics-capabilities/fraud-
detection-and-analytics.html
6. https://www.rd-alliance.org/group/big-data-ig-data-development-ig/wiki/big-data-definition-importance-examples-
tools#:~:text=Big%20data%20is%20a%20term,amount%20of%20data%20that's%20important.&text=Big%20data%20ca
n%20be%20analyzed,decisions%20and%20strategic%20business%20moves.
7. https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/deloitte-analytics/tr-fraud-analytics.pdf
8. https://www.fico.com/blogs/search?keywords%5B%5D=fraud
9. https://www.fico.com/blogs/controlling-obvious-and-hidden-fraud-threats-cristiano-ronaldo-syndrome
10.https://www.crisil.com/en/home/our-businesses/grna/risk-and-analytics/financial-crime-analytics-capabilities/fraud-
detection-and-analytics.html