Increase confidence in your organization with proactive fraud prevention measures

March 2023
3 min read

Financial institutions spend billions per year in their fight against fraud.


With every improvement, fraudsters look for and find new opportunities to exploit. When the opportunity arises, some people see a big incentive or pressure to commit fraud, and most will be able to justify to themselves why it is acceptable to commit fraud (as shown in the Fraud triangle – Figure 1). Unfortunately, the impact of fraud on organizations, individuals and society in general is substantial.

In a recent report by the Association of Certified Fraud Examiners (ACFE), Occupational Fraud 2022: A report to the nations, it is estimated that organizations lose about 5% of their revenue each year due to employees committing fraud against their employer. It is estimated that more than USD 4.7 trillion is lost worldwide to occupational fraud.  Of these, most cases were identified through a tip to a hotline and most were not detected until 12 to 18 months later. The longer the fraud was undetected, the higher the loss. But organizations do not only fight fraud internally; external threats are also on the rise. As businesses evolve and processes are automated and digitalized, fraud activities become much more complex.

Data and modeling approach to fraud prevention

To effectively prevent fraud, it first needs to be identified. Traditionally, employees are trained to identify anomalies or inconsistencies in their daily work environment. It is still crucial that your employees know what to look for and how to spot suspicious activities. But due to the complexities and vast amounts of information available, and because fraudsters are becoming more sophisticated, it becomes much more difficult to determine whether a potential customer is a fraudster or a real client.

The good news is that digitalization and increased data availability provides the opportunity for data analytics. It is important to note here that it does not completely replace your current processes; it should be used in addition to your traditional prevention and/or detection methods to be more effective to proactively identify and prevent fraud in your organization.

Benefits of data analytics

Traditionally, sampling was done on a population to test for fraud instances, but this may not be as effective because it only looks at a small population. Because fraud numbers reported usually being small (but with a large monetary impact), it is possible to overlook valuable insights if only samples of populations are investigated. Ideally, all data should be included to identify trends and potential fraudulent activities, and with data analytics that is possible. By analyzing large amounts of data, organizations can identify patterns and trends that may indicate fraudulent activity. It can help to improve the accuracy of fraud detection systems, as they can be trained to recognize these patterns.

Data analytics can increase efficiency by reducing false positives and false negatives and assists organizations to automate parts of the fraud detection processes, which can save time and resources. This allows the business to focus on other important strategic objectives and tasks such as customer service and product development.

By using data analytics to identify and prevent fraudulent activity, organizations can help to protect their customers against financial losses and other harm. Customer trust and loyalty are built when organizations show they are serious about the welfare and safety of their customers.

Detecting and preventing fraud

Reality is that preventing fraud upfront or in an early stage is much more economical and beneficial than having to detect fraud after the fact, as investigations are time-consuming and the fraud is not always easy to proof in court. Moreover, by the time it is detected, a loss may have already been incurred. Using data analytics to identify fraudsters and fraudulent activities earlier, can protect the bottom line by reducing financial losses and improving the organization’s overall financial performance.

By using analytics to detect and prevent fraud, organizations can demonstrate to regulatory bodies that they are taking compliance seriously. Reporting suspicious transactions and activities to regulatory bodies is a key component of complying with anti-money laundering and counter-terrorism financing legislation, and analytics can assist with identifying these transactions and activities more effectively.

Data analytics can be used to prevent fraud at onboarding, detect it in the existing customer base, and to make your operational processes more efficient. More specifically, data analytics can be used and leveraged as follows:

  • Identifying outlier trends and hidden patterns can highlight areas and/or transactions that are more vulnerable to fraud.
  • Automating identification of exceptions removes manual intervention and makes the identification criteria more consistent.
  • Traditional physical reviews using limited resources to investigate is time-consuming. Data analytics can be used to prioritize the ones with the highest impact and risk, e.g. investigate the suspicious transactions with the highest value first.
  • Combining data from different data sources to feed into a model provides a more holistic view of a customer or scenario than looking at individual transactions or applications in isolation.
  • Both structured and unstructured data can be used to prevent and detect fraud.
  • Fraud propensity model scoring can run automatically and generate results to be reviewed and investigated in real-time or near real-time.
  • Analyzing relationships between various entities and customers using Social Network Analysis (SNA). Traditionally, networks/links were identified by the investigator while building a case. By using analytics, less time is needed to identify these relationships. Also, it identifies valuable links previously unknown, as additional levels of relationships can be examined.
  • Specific modus operandi identified by the organization’s internal fraud team can be translated into data models to automate identification of similar cases. (See Case study below)
  • Applying a fraud model to the organization’s bad debt book can assist with your collections strategy. Fraudsters never intended to pay and focusing your collection efforts on them wastes time and valuable resources. Most efforts should be on those cases where money can be collected.

Case study

The Zanders data analytics team has experience with applying data analytics within a company to identify customers who create synthetic profiles at point of application. By working closely with investigators, a model was developed in which one out of every three applications referred for investigation was classified as fraudulent.

The benefit of introducing analytics was twofold – from an onboarding- and existing customer point of view. The number of fraudsters identified before onboarding increased, preventing (potential) losses. Using the positive identified frauds at point of application, and checking the profile against the existing book, helped to identify areas that were more vulnerable where investigation should be prioritized.

The project proved that:

  • Data analytics is valuable and combining it with insights from the operational teams is powerful.
  • The buy-in from the stakeholders made the model more effective. If the team investigating the alert does not trust the model or does not know what to look for, there will be resistance in investigating the alerts.
  • Taking your internal fraud team on a data and analytics journey is a must. They need to understand the impact that their decisions and captured outcomes can have on future models.
  • Challenges with false positives (within business appetite and investigation capacity) are a reality, but having a model is better than searching for a needle in a haystack. Learning from the results and outcomes of the investigations, even if they were false positives, will enhance your next model.
  • One size does not fit all. Fraud models need to be tailored to the business’ needs.

Conclusion

While using data analytics to identify fraudulent activities is an investment, organizations need to outweigh the benefit of incorporating data analytics in their current processes against the potential losses. Fraud not only results in monetary losses, it can lead to reputational damage and have an impact on the organization’s market share as customers will not do business with an organization where they don’t feel protected. Your customers also expect great customer service and implementing proactive fraud prevention measures increases confidence in your organization.

How can Zanders help your organization?

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