The document discusses the integration of analytic technology and human interaction in fraud detection analytics to identify improper transactions such as fraud or bribery. It outlines the steps involved in fraud detection, from defining fraud indicators to data analysis and reporting, emphasizing the need for data quality, security, and appropriate skill sets. Various industries, including banking, insurance, and healthcare, are highlighted for the application of these analytics techniques.

1. Team: Abraca Data
Fraud Detection Analytics

2. The combination of analytic technology and fraud analytics techniques with
human interaction which will help to detect the possible improper transactions like fraud or
bribery either before the transaction is done or after the transaction is done
What is Fraud Detection Analytics
Ounce of prevention=Pound of cure!

3. Why use of Data analytics for fraud?
•Improved efficiency –Automated method
for detectingandmonitoring potentially
fraudulentbehaviour.
•Repeatable tests – Repeatable fraud tests
that canbe run on yourdataat any time.
•Wider coverage –Full coverage of testing
population rather than ‘spot checks’ on
transactions –betterchance of finding
exceptional items.
•Early warning system –Analytics solutions
can help you to quickly identify potentially
fraudulent behaviourbefore the fraud
becomes material.
Fraud Detection Methods
Social
Network
Analysis
Predictive
analytics for
big Data
Social
Customer
Relationship
Management
(CRM)
AI Techniques
• Data Mining
• Expert
systems
• Pattern
recognition
Repetitive or
Continuous
Analysis

4. Application of Fraud Detection Analytics
Banking Industry
Insurance Industry
Healthcare
Other Industries
• Card Fraud
• Application Fraud
• Lost/Stolen Cards
• False cases
• Claims
• Premiums
• Employee-related Frauds
• Billing fraud
• Health Insurance fraud
• Inventory(Manufacturing)
• Audit
• Reimbursement schemes(education)

5. Payroll
• Duplicate Bank details
Payments Analysis
• Adherence to limits
• Trend analysis
1.Fraud test definition
Define the fraud indicators you
wish to test for based on exper-
ience and common fraud
schemes
Accounts payable
• Weekend payments
• Payment to
unauthorised vendors
Financial statement close
• Journals posted after hours
2.Data identification and extraction
Identify source IT systems which store
the data required & extract the data in a
controlled environment.
Operational
Systems
3.Data Cleansing
Clean the data and convert to
format suitable for analysis.
4. Data Analysis
Translate the fraud test into suitable
technical data test & perform analysis
using data interrogation techniques to
identify unusual trends, data anomalies.
5.Reporting and Monitoring
Business focused reports which are
easy to understand summarise results
and provide data insights
Data analytics process

6. Data Analytics Challenges:
• Data quality –The results from analytics tests are only as good as the input data. Before
performing tests, it is important to assess the quality of data and perform
validation/cleansing ifrequired.
• Data volumes – There may be significant data volumes supporting certain business
processes. Your data analytics testing infrastructure should be capable of handling
these volumes.
• Data security –It is essential that appropriate security protocols are considered
throughout the extraction and analysis to protect the confidentiality and integrity of
source data.
• Skillsets –Data analytics requires a combination of business and technical skills to
define the tests, perform the analysis and interpret the results in order to provide
meaningful insights.

7. References
https://www.educba.com/fraud-detection-analytics/
https://www.acl.com/pdfs/ACL_fraud_ebook.pdf
http://www.ey.com/Publication/vwLUAssets/EY_-_Forensic_Data_Analytics/$FILE/EY-Data-Analytics-The-role-of-
data-analytics-in-fraud-prevention.pdf
https://www.pwc.in/assets/pdfs/publications/2015/current-fraud-trends-in-the-financial-sector.pdf