This document provides an overview of fraud analysis. It discusses how fraud analysis uses a combination of analytic technology and detection techniques with human interaction to help detect potential improper transactions. The process involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. Key topics covered include the role of fraud analysts, common types of fraud, importance of fraud analysis, potential benefits, and statistical and artificial intelligence techniques used for fraud detection.

1. FRAUD ANALYSIS
By: Smriti Tikoo

2. Agenda
• Introduction
• Fraud Analysis
• Process of Analysis
• Fraud Analyst
• Types of Fraud
• Importance of Fraud Analysis
• Potential Benefits
• Detection Techniques

3. FRAUD ANALYSIS
• Combination of analytic technology and
detection techniques with human interaction
to help detect potential improper
transactions, such as those based on fraud
and/or bribery, either before the transactions
are completed or after they occur.

4. Process of Analysis
• 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.

5. Step wise procedure
• 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 Ad-hoc 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
• Segregation of duties is very essential
• Expand the scope of the program and repeat the process

6. Need for Fraud Analysis
• Use of new tools and technologies for fraud
prevention to harness their data to sniff out instances
of fraud, potentially before they fully unfold.
• This development couldn’t occur at a better time, as
events and regulators alike are challenging the controls
organizations have used for years.
• In areas of anti-fraud, anti-bribery, and anti-money
laundering, the regulatory environment has tightened.
At the same time, fraud, corruption, and abuse are
unrelenting—and constantly evolving.

7. Role of Fraud Analyst
• Who investigates forgery and theft within
customers' accounts and transactions on
behalf of a bank or a financial institution.
• They track and monitor the bank's
transactions and activity that comes through
the customers' accounts.

8. Types of Fraud
• Financial Statement
• Asset misappropriation
• Skimming of Cash and Cash Larceny
• Misuse of company assets
• Theft of intellectual property and trade
secrets .
• Healthcare, Insurance and banking.
• Consumer Fraud

9. Importance
• More data, more opportunities .
• Anomaly detection and rules-based methods have been in
widespread use to combat fraud, corruption, and abuse for
more than 20 years.
• They’re powerful tools, but they still have their limits.
Adding analytics to this mix can significantly expand fraud
detection capabilities, enhancing the “white box” approach
of the rules-based method.
• Not only can analytics tools enhance rules-based testing
methods, but they can also help measure performance to
standardize and help fine tune controls for constant
improvement. That’s a big deal for companies awash in
data— data that could be put to better use.

10. Potential Benefits
• Identify hidden patterns :Unsupervised or non-rules-based
analyses driven by analytics technology can uncover new
patterns, trends, fraudulent schemes, and scenarios that
traditional approaches miss.
• Enhance and extend existing efforts Analytics need not
replace what you’re already doing—it can be an extra layer
to add punch to your existing efforts.
• Cross the divide Fraud analytics can pull data from across
your organization into one central platform, helping create
a true, enterprise-wide approach.
• Measure and improve performance What’s working?
What’s not? With fraud analytics in place, you don’t have to
guess. The data tells the story

11. Statistical detection techniques
• Techniques used for fraud detection fall into two primary classes: statistical
techniques and artificial intelligence. Examples of statistical data analysis
techniques are:
• Data preprocessing techniques for detection, validation, error correction, and
filling up of missing or incorrect data.
• Calculation of various statistical parameters such as averages, quintiles,
performance metrics, probability distributions, and so on. For example, the
averages may include average length of call, average number of calls per month
and average delays in bill payment.
• Models and probability distributions of various business activities either in terms
of various parameters or probability distributions.
• Computing user profiles.
• Time-series analysis of time-dependent data.
• Clustering and classification to find patterns and associations among groups of
data.
• Matching algorithms to detect anomalies in the behavior of transactions or users
as compared to previously known models and profiles. Techniques are also needed
to eliminate false alarms, estimate risks, and predict future of current transactions
or users.

12. Artificial Intelligence Techniques
• The main AI techniques used for fraud management include:
• Data mining to classify, cluster, and segment the data and
automatically find associations and rules in the data that may
signify interesting patterns, including those related to fraud.
• Expert systems to encode expertise for detecting fraud in the form
of rules.
• Pattern recognition to detect approximate classes, clusters, or
patterns of suspicious behavior either automatically (unsupervised)
or to match given inputs.
• Machine learning techniques to automatically identify
characteristics of fraud.
• Neural networks that can learn suspicious patterns from samples
and used later to detect them.
• Other techniques such as link analysis, Bayesian networks, decision
theory, and sequence matching are also used for fraud detection