Visual techniques in fraud detection play a crucial role in identifying subtle patterns, offering analysts a comprehensive perspective that can lead to quicker and more accurate identification of fraudulent activities.
One of the most challenging tasks when using visualization for fraud detection is the sheer amount of data that is usually obtained by auditing systems. First, the auditor has to retrieve the data from the auditing system.
As Visualization techniques, may enhance the quick identification of relationships and significant structures and the detection of suspicious patterns that may hide in the amount of data. Besides the visual exploration, interaction with the data allows for a deeper understanding of the dependencies within the data changing over time.
One of the techniques used in visualization is the fraud ring, i.e., several individuals or persons with specific needs or wants are involved in a series of fraud events.
For example, for fraud schemes like insurance fraud or first-party bank fraud, auditors look for potential fraud rings. These structures are perceivable in a suitable visual representation like a diagram. Therefore, it is easier to detect fraud cases through visualization rather than when trying to dig into a large number of database rows.
Visual Techniques in Fraud Detection
Data visualization is especially important in the early stages of fraud investigation, where the investigator is attempting to perform an efficient and effective data analysis and desires to better understand the relationships that may be present in a complex data set. The fraud investigator may employ data visualization as a proactive detection approach, using it to search for data patterns that suggest fraudulent activity.
Alternately, the investigator might be evaluating a predication of fraud, using data visualization to develop a fraud theory that is subsequently used to select additional investigative procedures. In either case, the investigator is following a hypothesis testing approach, developing preliminary hypotheses about fraud and analyzing relevant data to see if they appear to be true.
If the data analysis phase of a fraud investigation does not support the hypothesis that fraud has occurred, then the investigation ends. On the other hand, if data analysis produces results consistent with the hypothesis that fraud has occurred, the investigator will proceed to conduct other investigative procedures, such as examination of documents and interviewing possible witnesses.
Since fraudulent actions are deliberate and non-random, traditional audit methods involving the use of statistical sampling are often ineffective in discovering fraud. Hence, fraud examination professionals recommend the use of data mining procedures for detecting fraudulent transactions.
In performing data mining procedures, investigators may brainstorm about possible irregularities that could occur in the business processes or transactions they are examining.
Alternatively, they may have been given a prediction that fraud has occurred. Regardless of whether they identify possible fraudulent activity through brainstorming or receive a prediction of fraud, investigators then outline ways that the schemes might show up in data patterns.
For each indicator thus identified, investigators design a data mining query or procedure intended to identify whether there are individual transactions that need to be examined more closely. Audit software packages facilitate data mining for fraudulent transactions within institutions
Graphical analysis may facilitate identifying suspicious patterns of transactions in data. While spreadsheet programs can facilitate graphical analysis, such analysis can be cumbersome if the user wants to change the variables being graphed or focus on a subset of the data, it is usually necessary to generate a new graph.
Final Thoughts
Fraud detection through data visualization presents a powerful tool for auditors and investigators, enabling them to quickly identify suspicious patterns amidst vast amounts of data. Traditional methods, like statistical sampling, often fall short in identifying deliberate fraudulent actions, underscoring the significance of advanced techniques like data mining. By visually representing data, auditors can swiftly pinpoint irregularities, such as fraud rings, that might be obscured in extensive database rows.
Whether used proactively or in response to a fraud prediction, visualization aids in hypothesis testing and, when supported, paves the way for deeper investigative procedures. Although tools like spreadsheets offer some capabilities for graphical analysis, they may be limiting in adaptability and scope, emphasizing the need for specialized audit software.
