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Consumer studies can estimate missing cash
Consumer cash-use studies like the U.S. Federal Reserve’s Consumer Payment Choice study can be an effective means of estimating missing funds in cash skimming cases. The authors explain how they’ve used these studies in fraud cases and how fraud examiners may deploy these studies.
Written By: Amy Cooper, Ph.D., CFE, CPA, Robert Logan, Ph.D., Ken Abramowicz, Ph.D.
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- Accounting and Auditing
- Data Analytics
- Financial Transactions and Fraud Schemes
Investing in the fight against fraud
The ACFE Research Institute funds real-world, actionable research that’s helping professionals detect, prevent and respond to fraud more effectively.
Written By: Crystal Zuzek
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- Accounting and Auditing
- Career and Professional Development
- Data Analytics
Data-driven compliance: A new frontier for anti-bribery and anti-corruption risk programs
Despite significant regulatory advancements and the availability of innovative tools, many anti-bribery and corruption compliance teams avoid implementing data-driven strategies. The author emphasizes the necessity of embracing an innovative approach to address these risks in the wake of evolving regulatory and enforcement demands.
Written By: Rajesh Melappalayam, CFE
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- Anti-Fraud Laws Regulations and Compliance
- Bribery and Corruption
- Data Analytics
Detecting fraud without historical data
Datasets of documented fraud cases can be instrumental in detecting anomalies and patterns in all sorts of transactions, but many times those datasets aren’t available. With fraudsters perpetrating increasingly sophisticated schemes, fraud fighters need an edge on the criminals with advanced techniques. Here, the author describes how to use machine-learning models and other advanced techniques to detect fraud when you don’t have historical data to learn from.
Written By: Penny Li, CFE, CPA, Ning Ping Wang
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- Computers and Technology
- Data Analytics
- Fraud Investigation and Examination
Who owns transaction and controls monitoring?
Who monitors and oversees high-risk transactions in your organization? In this article, I explore who owns transaction and controls monitoring for vendors, customers and employees.
Written By: Vincent M. Walden, CFE, CPA
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- Data Analytics
- Fraud Risk Management
Revisiting Benford’s Law with added AI horsepower
For over a decade, CFEs, auditors and analysts have used Benford’s Law to identify journal entry irregularities. Today, this analysis method remains relevant as ever as new applications using AI and simulation could bring Benford’s Law to the forefront of your fraud risk management controls.
Written By: Vincent M. Walden, CFE, CPA
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- Data Analytics
- Fraud Risk Management
- Investigation
Can generative AI give us prescriptive analytics?
One of four key types of analytics has long been considered a “pie-in-the-sky” concept for fraud investigators. Rather than describing or diagnosing something that’s happened or predicting what could happen, prescriptive analytics can tell us what we should do about it. With the growth of generative AI and large language models, it may just be in reach.
Written By: Vincent M. Walden, CFE, CPA
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- Accounting and Auditing
- Data Analytics
- Fraud Prevention and Deterrence
Breaking down data silos
The better the data, the better the insight. The better the insight, the better the results. In this issue, we explore how CFEs can help their organizations break down data silos to improve business transparency.
Written By: Vincent M. Walden, CFE, CPA
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- Cyberfraud
- Data Analytics
Measure and monitor your fraud risk management program success
‘Tis the season to set company goals. As we wrap up 2023 and look to the new year, ponder this: What are some of the key performance indicators (KPIs) companies use to measure the effectiveness of their fraud risk management program? Here, we explore some leading examples of anti-fraud KPIs that drive business value.
Written By: Vincent M. Walden, CFE, CPA
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- Corporate Governance
- Data Analytics
- Technology
From many, comes one (algorithm)
Recent anticorruption research shows that when companies collaborate to share information about third-party payments and high-risk transactions, they have a 25% greater chance of predicting improper payments than when each company’s model is performed in isolation. A new data-sharing consortium led by a nonprofit at MIT is working to make such collaboration possible.
Written By: Vincent M. Walden, CFE, CPA
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- Bribery and Corruption
- Data Analytics
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