Fighting Frauds Using Big Data Visibility and Intelligence

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Fighting Frauds Using Big Data Visibility And Intelligence



Introduction -

Big Data Analytics

Security concerns at two levels

Organization level Customer’s Information


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 -


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 -


Frauds, Their Types, & Analytical Techniques for Fraud Detection


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


• 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.


What is Fraud?


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..


Defining Fraud


Types of Frauds:

1. Corruption:

Violation of duty of an employee by

misusing the influence in a

transaction for direct or indirect

gain.


Types of Frauds


Types of Frauds:

2. Misuse of assets:

Company resources being misused e.g

theft of company’s money, false bills

and inflated expenditure reports.


Types of Frauds


3a. False Financial statements:

Omitting the original information or adding

some additional information in the financial

statements thus making false financial

statements.


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.

3b. Omitting and Altering:


Types of FraudsAn example to emphasize:


Original Line:


3b. Omitting and Altering:

Frauds, Their Types Analytical Techniques for Fraud Detection - 8/12

Types of Frauds

Original Line:


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:


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?


Analytical Techniques for Fraud Detection


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.




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.



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


Why Big Data Analytics


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


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.



Why Big Data Analytics:

3. Financial crimes grew due to weak global economy growth in organized crime or increasing sophistication of fraudulent schemes.


4. Current analytics systems do not support robust analytical modeling which in turn makes the process of analyzing information more difficult.


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.



Fraud Targets

Fraudsters’ Favourites:

● Banking

● Insurance

● Government Institutions

● Health Care Industry

● Retail Sector


Fraud and Predictive Analysis


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


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.730 TB


“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.”



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:


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.


Data Analysis Technology Enables Fraud Examiners To:


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


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?


Things To Ponder


Big Data Analytics Providers


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


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


& Inferences


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


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.

HCFAC®


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


Final VerdictThese 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 .


Ajeet Singh

Author

DirectorCo-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 [email protected]

mailto:[email protected]


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Fighting Frauds Using Big Data Visibility and Intelligence

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