Transcript The Fraud Detection Process - McGraw Hill Higher Education

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Fraud Detection
McGraw-Hill/Irwin
Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
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The Fraud Detection Process
 The
fraud detection process involves identifying indicators
of fraud that suggest a need for further investigation.
 Various means of detecting fraud exist, including tips and
hotlines, financial statement audits, and by accident.
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Hotlines and Fraud Discovery
 Hotlines
are very effective
 They must have a disclosure policy
 Confidentiality
 They
and anonymity
must be supplemented by an ethics code, employee
training proper monitoring, advertising, and the right tone
from top management
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Other Means of Fraud Discovery
 By
accident
 This
happens frequently, especially in companies with weak
controls. But it might happen too late for a small company to
survive.
 By
external auditors. SAS 99 requires that auditors design
financial statement audits in such a way so as to have a
reasonable chance of detecting misstatements in the
financial reports. But not all fraud leads to misstatements.
Still, external auditors must consider fraud risk and should
use the fraud triangle.
 External
auditors must report frauds to the appropriate level of
management.
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Other Means of Fraud Discovery
 By
internal auditors
 Internal auditors should report directly to the board of
directors
 By inspectors general
 By security departments
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Fraud Issues
 Often
fraud and waste or errors are indistinguishable from
one another
 There is a tradeoff between prevention, detection, and
correction
 Detection produces false positives and false negatives
 False
positives indicate fraud when there is none
 False negatives indicate no fraud where this is fraud
 One
goal is to balance the rate of false positives versus the
rate of false negatives so that Total Fraud Costs are
minimized
 Total
Fraud Costs = Prevention Costs + Detection Costs +
Correction Costs + Fraud Losses
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Fraud Indicators
 Composite
indicators
 Are
typically produced from weighted sums of individual
indicators. The weighted sum is called a risk score.

One example of a risk score is a FICO credit score
 Single-factor
 Are

 In
indicators
also called red flags
In the typical scenario, a single red flag may initiate an
investigation
many cases, the reliance on fraud indicators alone is not
sufficient. Random tests also may be needed, because
fraudsters may manipulate fraud indicators, or the set of
detectors in use might not be capable of detecting some
frauds.
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Data-Driven Fraud Detection
 Data-driven
fraud detection involves the formal process of
sifting through data in search of fraud indicators.
 Sources
of data include internal control data, basic tips and
hotlines, security breaches, and pattern data.
 Internal
control data include reconciliation failures, control
total failures, exception transactions, and apparent errors.
 Security breaches occur when an individual accesses some
entity resources without first being granted a sufficient
privilege to do so.
 Pattern data analysis, or data mining, combines different
data items in complex and non-intuitive ways to signal
fraud.
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Steps in Building a Fraud Detection
System
 The
general approach involves 1) risk analysis and control
development, 2) exploitation of expert knowledge, 3)
knowledge discovery, 4) implementation.
 Knowledge
discovery involves SEMMA: Sampling,
Exploration, Modification, Modeling, and Assessment.
 Various common modeling techniques exist, including linear
regression analysis, for example.
 Various special modeling techniques also exist, including
social network analysis, content analysis and text analysis,
and Benford analysis, for example.
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Benford Analysis