We know that Big Data is an immensely popular talking point. But here we will be discussing more about Big Data and Security for enterprises. From security perspective, there are two distinct issues in the Big Data Analytics context: security concerns for the organization and security concerns of the customers’ information. We will also talk about how we can analyze, and even predict security incidents using Big Data Analytics techniques. We know that the enormous data held by enterprises and government agencies is growing exponentially. With this, the capability to reduce fraud grows along with it. There are enterprises like banking, insurance and healthcare where frauds are committed quite often. The losses from such frauds may vary from one organization to another but the cost involved is substantial and therefore reducing frauds is necessary not only to maintain world-class standards in the ethical context but also in terms of the business aspect. Organizations are struggling hard to find significant and effective methods to combat frauds happening internally in these enterprises. To give you a better idea, we are talking about a small fraud in an organization that has an enormously huge amount of information- a little fraud-search can be compared to searching for a needle in a haystack. It can turn out to be time and effort consuming. Traversing through such huge information is cumbersome. This type information is usually a sum of complex correlations and aggregations and as such, ‘little’ mistakes can be very crucial. With this blog, we will stress how big data analytics can help in overcoming the frauds happening within various type of organizations. Presentation touch-points: - Frauds, their types and fraud detection techniques. - Big Data and Big Data Analytics. - Fraud Target - Fighting Frauds Using Big Data Visibility and Intelligence - Big Data Analytics Solution Providers 1. Frauds: The term ‘fraud’ is generally defined in the law as an intentional misrepresentation of material existing fact made by one person to another with knowledge of its falsity and for the purpose of inducing the other person to act, and upon which the other person relies with resulting injury or damage. The definition of ‘person’ can be extended to individuals, groups or firm or a group of companies. So fraud can contain a wide range of illicit practices and illegal acts which involves misrepresentation and intentional deception. Let’s quickly get into discussing the types of fraud especially at the occupational level. 1.1 Types of Frauds: 1.2 Analytical Techniques for Fraud Detection:

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Fighting Frauds Using Big
Data Visibility And
Intelligence
sales@algoworks.com Toll Free : +1-877-284-1028

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Introduction -
Big Data
Analytics
Organization
level
Customer’s
Information
Security
concerns at
two levels

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Small frauds in an enterprise can turn out to be time and
effort taking process.
Big Data Analytics can help you in overcoming such
frauds happening in organizations.
Introduction -

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Key Essentials:
1. Frauds, their types and fraud detection
techniques.
2. Big Data and Big Data Analytics.
3. Fraud Target.
4. Fighting Frauds Using Big Data Visibility
and Intelligence.
5. Big Data Analytics Solution Providers.
Let’s Discuss -

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Frauds, Their Types, &
Analytical Techniques for
Fraud Detection

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Defn 1:
The term ‘fraud’ is generally defined in the law as an
intentional misrepresentation of material or existing fact
which results in damage or information.
Defn 2:
Fraud comprises of illicit practices or acts which involves
misrepresentation and intentional deception.
Frauds, Their Types Analytical Techniques for Fraud Detection – 1/12
Defining Fraud

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• Frauds must be reported periodically to
senior management and the board.
• Audit activity must be ongoing process for
detection of Frauds.
• Reporting must be supported with
significant risk exposures and control
issues.
Frauds, Their Types Analytical Techniques for Fraud Detection – 2/12
What is Fraud?

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Conclusively, any illegal act
which is characterized by
deceit, concealment, or
violation of trust, end up
impacting an organization’s
psychological, operational
and financial setup..
Frauds, Their Types Analytical Techniques for Fraud Detection – 3/12
Defining Fraud

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Types of Frauds:
1. Corruption:
Violation of duty of an employee by
misusing the influence in a
transaction for direct or indirect
gain.
Frauds, Their Types Analytical Techniques for Fraud Detection – 4/12
Types of Frauds

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Types of Frauds:
2. Misuse of assets:
Company resources being misused e.g
theft of company’s money, false bills
and inflated expenditure reports.
Frauds, Their Types Analytical Techniques for Fraud Detection – 5/12
Types of Frauds

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3a. False Financial statements:
Omitting the original information or adding
some additional information in the financial
statements thus making false financial
statements.
Frauds, Their Types Analytical Techniques for Fraud Detection – 6/12
Types of Frauds

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Original Line:
Please help me out as I am hiding
inside the cellar downstairs where my
uncle is hunting me out with knives to
kill me. What a dog he has become
you cannot believe it. Do not take this
message for a joke he is acting sick
and crazy.
3b. Omitting and Altering:
Frauds, Their Types Analytical Techniques for Fraud Detection – 7/12
Types of Frauds
An example to emphasize:

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Original Line:
Please help me out as I am hiding
inside the cellar downstairs where my
uncle is hunting me out with knives to
kill me. What a dog he has become
you cannot believe it. Do not take this
message for a joke he is acting sick
and crazy.
3b. Omitting and Altering:
Frauds, Their Types Analytical Techniques for Fraud Detection - 8/12
Types of Frauds
Original Line:
Please help me out as I am hiding
inside the cellar downstairs where my
uncle is hunting me out with knives to
kill me. What a dog he has become
you cannot believe it. Do not take this
message for a joke he is acting sick
and crazy.
Modified Line now becomes:
Please help my dog… you
cannot believe it… Do not
take this message for a joke
…he is sick.
An example to emphasize:

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Step 1:
Know:
1. The areas in which fraud can occur?
2. What fraudulent activity would look like in the data?
3. What data sources are required to test for indicators of
fraud?
Frauds, Their Types Analytical Techniques for Fraud Detection – 9/12
Analytical Techniques for Fraud Detection

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Frauds, Their Types Analytical Techniques for Fraud Detection - 10/12
Step 2:
Techniques auditors use are:
1. Calculation of statistical parameters.
2. Classification for finding patterns amongst data
elements.
3. Identifying duplicity of transactions.
Analytical Techniques for Fraud Detection

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Frauds, Their Types Analytical Techniques for Fraud Detection – 11/12
Step 2:
Techniques auditors use are:
4. Identifying missing values in
sequential data.
5. Validating entry dates to identify
inappropriate times of postings or data
entry.
Analytical Techniques for Fraud Detection

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1. Fighting fraud can be costly and
complex.
2. Large amount of capital is lost in
frauds and even larger amount is
invested to fight it.
3. When data is massive, managing and
detection of fraud becomes a
gruesome process.
Frauds, Their Types Analytical Techniques for Fraud Detection- 12/12
Conclusion

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Why Big Data Analytics

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Big Data:
It is a large volume of high velocity,
complex, and variable data which requires
advanced techniques and technologies to
enable the capture, storage, distribution,
management, and analysis of
information.
Big Data and Why Big Data Analytics - 1/4

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Why Big Data Analytics:
1. Volume of Information and data
which organization generates
everyday makes fraud detection
difficult than ever to detect it.
2. Big data analytics provide a visible,
feasible and intelligent method to
fight fraud.
Big Data and Why Big Data Analytics - 2/4

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Why Big Data Analytics:
3. Financial crimes grew due to weak
global economy growth in organized crime
or increasing sophistication of fraudulent
schemes.
Big Data and Why Big Data Analytics - 3/4
4. Current analytics systems do not support robust analytical modeling which in turn
makes the process of analyzing information more difficult.

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Why Big Data Analytics:
5. Big Data Analytics and Intelligence
tools help in fighting such frauds.
6. Big Data and Security approach
primarily focuses on data management
and consolidation, combining data
integration, data quality and master
data management within a unified
environment. This helps in getting the
cross-channel enterprise data on a
single platform.
Big Data and Why Big Data Analytics - 4/4

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Fraud Targets
Fraudsters’ Favourites:
● Banking
● Insurance
● Government Institutions
● Health Care Industry
● Retail Sector

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Fraud and Predictive
Analysis

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Analysis:
The Aberdeen Group analyzed the responses of 29 companies for the detection and
prevention of fraud.
Fraud and Predictive Analysis 1/7
1. Analysis showed companies using
predictive analytics were investing more
as compared to the other companies.
2. More the investment….
… lesser the fraud

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3. It predicted the average percentage of
loss in annual revenue due to fraud was
1.5%.
Fraud and Predictive Analysis 2/7
Analysis:
5.HCFAC, observed that how huge investments for fighting fraud leads to
satisfactory results.
4. Research also states that almost an
average of 730 terabyte of active data is
available with each organization and this
number is growing quickly.

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“About 26% of the data is currently being accessed by the
companies whereas the average requirement to get appreciable results
is of 52%...
… just about double.”
Fraud and Predictive Analysis 3/7

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1. Analyze organization’s business data
therefore helping in identifying the
transactions which indicate fraudulent activity.
2. Provide an effective way to be more
proactive in the fight against fraud.
Fraud and Predictive Analysis 4/7
Data Analysis Technology Enables Fraud Examiners To:

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3. Effectively test fraud, for all relevant
transactions across all applicable
business systems and applications.
4.Analyze business transactions
therefore helping in focusing on
investigative action of those
transactions that are suspicious.
Fraud and Predictive Analysis 5/7
Data Analysis Technology Enables Fraud Examiners To:

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1. Big Data Tools and Techniques helps in
providing insights to the integrity of financial
and business operations through
transactional analysis.
2. Big Data provides more accurate auditing
reports and better insights into the internal
control framework.
3. Such tools and techniques helps
organizations to improve the ability to
access and manage business risk.
Fraud and Predictive Analysis 6/7
Role of Big Data Tools and Techniques

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Many organizations are using whistleblower
hotlines which people are using as common
mean to report suspected fraudulent behavior
but these alone are not enough to get rid of
fraudulent activities
Why not be more aggressive, be
proactive?
Why not seek out indicators of fraud
in the data?
Fraud and Predictive Analysis 7/7
Things To Ponder

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Big Data Analytics
Providers

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Some of the popular Big data
Analytics Solution Providers are:
1. IBM,
2. KXEN,
3. SAS,
4. Rapid Insight,
5. Think Analytics
6. Siemens
7. Many More..
Big Data Analytics Solution Providers 1/2

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1. Data integration using automatic machines.
2. Easy tools required for analytics.
3. No special expertise required for using tools.
4. Ease of data integration.
5. Satisfactory performance by the machines.
6. Scales to accommodate future growth.
Big Data Analytics Solution Providers 2/2
Solution Selection Criteria

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& Inferences

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Health Care Fraud and Abuse Control,
explains that fighting fraud should be
a planned multi-faceted initiative.
1. Processes to develop risk
assessment
2. Program vulnerabilities should
be identified
3. Fraud audits should be
conducted.
Reaching To Conclusions & Drawing Inferences
HCFAC Initiative Stresses On

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Reaching To Conclusions & Drawing Inferences
HCFAC Initiative Stresses On
4. Analysis must be conducted focusing on
selected geographical areas.
5. Spreading awareness to users to protect
themselves from thefts.
6. Working with law enforcement groups to
make some standardized rules to protect
users.
7. Using innovative techniques to identify any
potential threats.

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It is quite difficult for organizations to stay prepared for all kind
of potential frauds therefore rapid changes in the IT sector calls
for new and enhanced methods to fight fraud.
Final Verdict

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Final Verdict
These organizations are well prepared for frauds which occur quite frequently,
there are still certain chances of those under-prepared frauds which do not occur
frequently such as
P H I S H I N G , S M I S H I N G , V I S H I N G , P H A R M I N G E T C .

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Ajeet Singh
Author
Director
Co-Founder & Director, Business Management
Ajeet is responsible for driving sales, forging strategic
partnerships and managing key Client relationships in the
United States and Canada.
Write to me at ajeet@algoworks.com

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Choose Algoworks - Big Data Analytics
Partner
Call us at: +1-877-284-1028
Mail us at:
sales@algoworks.com,
support@algoworks.com
Official Blog Link –
http://www.algoworks.com/blog/fighting-frauds-using-big-data
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