
How Data Analytics can Help Curb Fraud Risks?
Data analytics is much more powerful than randomly sampling a small number of transactions. When analyzing a large number of transactions over time, data anomalies may be discovered, which can reveal outliers, patterns, or trends that inform t
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CIO Applications Europe | Thursday, April 01, 2021

Data analytics is much more powerful than randomly sampling a small number of transactions. When analyzing a large number of transactions over time, data anomalies may be discovered, which can reveal outliers, patterns, or trends that inform the team's risk assessment of those transactions.
Fremont, CA: Internal auditors and other compliance-related diligence practitioners have a broad mandate to provide assurance and risk-based insights to their clients, often in highly nuanced and heavily controlled environments.
At the same time, these professionals acknowledge the need to improve their efficiency and effectiveness in detecting and reacting to risks, particularly when more people work from home. The use of data analytics to uncover fraud threats is not new, considering that would-be fraudsters are continually inventing new ways to escape detection. However, audit teams are at various stages of development and application of data-driven tools. Audit teams can leverage data analytics to control fraud risks.
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Data analytics is much more powerful than randomly sampling a small number of transactions. When analyzing a large number of transactions over time, data anomalies may be discovered, which can reveal outliers, patterns, or trends that inform the team's risk assessment of those transactions.
Collect Information
To establish fraud risk considerations within each risk type, use the organization's risk inventory, its risk, and control self-assessment (if available), as well as a cross-functional understanding of the processes and controls.
The design of the data analytics tools will be guided by determining all possible data sources available, such as the general ledger, vendor/customer lists, human resources metrics, employee training logs, hotline data, and external data resources — including considering relationships between the data sets that should, or should not, be present. Recognize that useful data sources can exist outside of the company, such as external benchmarking data, third-party due diligence, as well as business intelligence. Important considerations such as applicable timeframes, data availability, and data integrity should all be included in the inventory for internal data sources.
Discuss Schemes and Scenarios
The best outcomes come from discussions between the audit team and relevant stakeholders — two (or more) heads are always better than one! It would be easier to recognize red flag indicators, minimize false positives, and create analytic data queries to check for them if the scheme and scenario are more precise.
A good fraudster understands not only how to commit a crime but also how to conceal it, which might necessitate the use of controls that must be circumvented or overridden. As a result, audit teams should "think like a fraudster," which means comprehending the entire information flow, including controls, control weaknesses, tracking, and reporting, from beginning to end.
See Also :- Top Data Analytics Solution Companies
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