The long-term profitability of your venture will be primarily affected by the quality of the services and products you market, the competitiveness in your field of activity, and the experience of your employees. However, especially in our current digital age, another crucial element for corporate success will be determined by how you prevent financial fraud attempts. Has your company reached a superior level, and do you have to process financial transactions regularly? Are your employees unable to handle the real-time verification of recorded financial operations? If so, an excellent solution would be to use an AI-based fraud prevention application.
Financial fraud can have a devastating effect on the profitability of your business and impact the reputation and productivity of your employees. Have you been the target of a fraud attempt that resulted in a financial loss of thousands of dollars? It may not seem like much, but this breach in your security measures will likely have a domino effect, making your customers think twice before using your services. Fraudulent transactions can result in chargebacks, which will put you in conflict with collaborating banks and potentially result in losses of the commercialized products or services.
Not least, depending on your field of activity, falling victim to successful fraud attempts could put you at odds with regulatory bodies. Are you a publicly traded company? If so, a successful fraud attempt that affected the reliability of your financial reporting could make you breach the Sarbanes-Oxley Act, which the United States Securities and Exchange Commission enforces. Are you a private company? In that case, you could fail to comply with data protection regulations, like the GDPR, or defy the PCI DSS guidelines.
What Is a False Positive Rate?
In the context of fraud prevention, a false positive rate refers to the regularity of genuine financial transactions reported as fraudulent by the fraud-prevention tool used. A high rate of false positives will negatively affect your brand reputation and significantly affect your medium—to long-term revenue. Was the transaction made by your customer wrongly classified as fraudulent? Then, they will most likely abandon the purchase, as proceeding with the payment again would be too inconvenient.
A false positive rate between 2 and 5% should be avoided, but it’s not the end of the world. Is the percentage of false positives surpassing 5%? If so, that’s a significant problem that will affect the faith that target demographics place in your services. False positives will affect your clients’ experience and the productivity of your employees, as they could be forced to manually review the financial entry blocked by the tool you use. There are solutions, however, as AI-based fraud detection systems are significantly more efficient than traditional detection methods, and for national or international businesses, they represent the most cost-effective way to avoid potential fees associated with chargebacks reprocessing operations.
What Is the Difference Between True Positives and False Positives?
To make it simple, true positives refer to the percentage of fraudulent transactions that are correctly identified. False positives, on the other hand, are the financial operations that have been erroneously identified as fraudulent. Ideally, a qualitative fraud-prevention tool should have a low percentage of false positives and a high accuracy of true positive results. False positives can inconvenience customers, increase the workload of employees, and lead to system disruption. Moreover, they are unfortunately common, as 17% of online users had a valid transaction rejected in the last twelve months.
True positives, however, will raise customer confidence in the utilized fraud prevention systems, help you comply with fraud reporting regulations, and mitigate the risks of your professional activities. In traditional fraud-prevention systems, the false positives of flagged financial transactions will be higher. Moreover, conventional non-AI fraud prevention tools are less adaptable to changes in digital fraud techniques and can’t provide real-time insights into industry-adopted trends. For traditional approaches, an expected FPR will revolve around 2-5%. Yet, for AI-based applications, the reported false positive rate should be less than 1%.
Why Do AI Fraud Detection Systems Produce Fewer False Positives?
AI-based systems are continuously evolving. Because of this, their main advantage is the ability to interpret data models in real-time and improve their ability to spot patterns in the analyzed transactions without direct human interventions. In other words, AI models are adaptive, and they only get better with time. Moreover, high-quality AI-based fraud detection systems, thanks to the utilization of comprehensive neural networks, are much more suitable for complex and repetitive tasks that involve a lot of variables. What is one example of such a task? Structure identification in datasets.
AI models can detect anomalies in the analytical data before they can affect the financial stability of your venture, and their rule-based models are flexible and adaptable to each encountered situation. AI fraud prevention tools can utilize methods like support vector machines, GNNs, ARIMA, and t-distributed Stochastic Neighbor Embedding to detect temporal patterns in financial sequential exchanges. Plus, they are more scalable than traditional non-AI fraud detection solutions, and they are better at simulating adversarial external attacks.
A Staple of Well-Run Businesses
Fraudulent transactions can affect not only the direct profitability of the products or services you market but also the reputation and trust your target demographics place in your brand. Want to avoid problems with the regulatory bodies active in your sector? To automate the detection and management of the internal rule-based systems utilized to prevent financial fraud? Do you wish to reduce the false positive rate of utilized fraud-prevention approaches? If that’s the case, you need to invest in a comprehensive AI tool.
AI-based fraud detection applications can continuously monitor your recorded financial transactions and take real-time action to keep the productivity of your employees at a high level. AI tools can proactively identify and resolve fraudulent activities before they have the opportunity to test the vigilance of your workers. On top of that, they are adaptable to modification in widespread fraud approaches and utilize machine learning algorithms to report patterns in the transactional data with an FPR as close as possible to 1%. High-quality AI-based fraud prevention tools have become essential for companies wishing to extend their services to a transnational level. Are you the owner of a rapidly developing venture? If so, the utilization of such a tool could represent a cornerstone of your financial and reputational success.
