Podcast
Questions and Answers
What is a common characteristic of fraud detection that involves examining changes over time?
What is a common characteristic of fraud detection that involves examining changes over time?
What is the purpose of generated norms in fraud detection?
What is the purpose of generated norms in fraud detection?
What is the advantage of the strategic approach in investigating fraud symptoms?
What is the advantage of the strategic approach in investigating fraud symptoms?
What is the result of waiting until fraud symptoms become egregious to detect fraud?
What is the result of waiting until fraud symptoms become egregious to detect fraud?
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What is the purpose of Step 6 in the fraud detection process?
What is the purpose of Step 6 in the fraud detection process?
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What is the benefit of using inductive search for red flags in fraud detection?
What is the benefit of using inductive search for red flags in fraud detection?
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What is the relationship between historical patterns and data measurement in fraud detection?
What is the relationship between historical patterns and data measurement in fraud detection?
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What is the role of sharp and unexpected increases in spending, purchases, labor, or account balances in fraud detection?
What is the role of sharp and unexpected increases in spending, purchases, labor, or account balances in fraud detection?
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What is the difference between inductive and deductive approaches to fraud detection?
What is the difference between inductive and deductive approaches to fraud detection?
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What is the goal of fraud detection analytics?
What is the goal of fraud detection analytics?
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Study Notes
Data Analysis for Fraud Detection
- Data analysis involves comparing retrieved data against expectations and models to identify anomalies, unknown values, suggestive trends, or outliers.
- Algorithms used in data analysis must be company- and data-specific to be valuable.
Detecting Fraud Symptoms
- Fraud symptoms can be categorized into four types: Document and Record, Analytical, Behavioral, and Lifestyle Symptoms.
- Examples of Document and Record Symptoms include increasing purchases from favored vendors, increasing prices, and decreasing quality.
- Analytical Symptoms include larger order quantities, and buyers who don't relate well to other buyers and vendors.
- Behavioral Symptoms include kickbacks, use of unapproved vendors, and employee work habits changing unexpectedly.
- Lifestyle Symptoms include buyers living beyond their known salary, building expensive homes, and owning expensive automobiles.
Strategic Fraud Detection Techniques
- Techniques such as time trending, regression, cusum, fuzzy text matching, stratification and summarization, z-score distancing, and relation matching should be used for analysis.
- A matrix, tree diagram, or mind map should be created to correlate specific symptoms with specific possible frauds.
Gathering Data about Symptoms
- Data are extracted from corporate databases, online websites, interviews, and other sources.
- It is preferable to run fraud-detection queries against full transaction populations rather than using sampling or summarization.
Using Technology to Support Fraud Detection
- Scripting languages such as ACLScript, IDEAScript, Visual Basic, PowerScript, and Python can be used to analyze large numbers of transactions and perform repeated analyses.
Time Series Models in Fraud Detection
- Time series models are often used in fraud detection to examine changes over time and identify anomalies.
- Historical patterns within the data are used to set the standard against which data are measured.
Investigating Symptoms
- Once anomalies are highlighted and determined to be indicators of fraud, they are investigated using targeted and specific approaches.
- The strategic approach allows for a cost-effective and focused investigation effort.
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Description
This quiz covers data analysis techniques used to identify anomalies and detect fraudulent activities, such as price manipulation and vendor favoritism.
