Artificial intelligence is one of the buzziest technological innovations out there, primarily because of its wide range of potential use cases. Manufacturers, educators, healthcare professionals, and various other industry sectors are actively exploring how AI can streamline workflows and reduce labor-intensive tasks, making their employees’ jobs easier.
A particularly valuable use case for AI is in online payment fraud detection. Data from Juniper Research predicts that total losses to payment fraud will exceed $343 billion over the next five years—a massive hemorrhaging of capital that could potentially be stemmed by using advanced fraud detection tools. Major players in the financial services field are already using AI to forestall fraudulent payments, and if you’re considering adopting this technology, it’s about time too.
Infrastructure Requirements
Before purchasing a fraud detection tool that leverages AI, it’s crucial to audit the environment to ensure the right systems are in place. AI, especially in its early stages, can require massive amounts of processing power to analyze data. Additionally, network security is paramount to prevent cybercriminals from feeding fraudulent data into the model. Networks lacking the capacity for high bandwidth data transfers, tight security controls, or consistent uptime standards might benefit from switching to a dark fiber network.
A clean, consolidated pool of data is also essential for AI to function effectively. AI trained on incomplete or poor-quality data will fail to identify outliers that could indicate fraudulent transactions. Furthermore, there’s risk of alienating customers when using AI tools, so having a comprehensive communication plan in place before fully adopting the technology is important.
AI Best Practices
Making sure employees know how to use AI tools within regulatory and cybersecurity standards is important. In that spirit, here are a few guidelines to ensure proper AI usage.
- Review and fact-check content: AI is effective, but not perfect—and it’s entirely possible that the technology can produce incorrect results as it learns. Regularly checking its output helps avoid false accusations that could harm your brand. Ensuring that employees are diligent in verifying AI-generated content can prevent misunderstandings and maintain customers trust.
- Keep your databases clean: After the initial cleaning of your database, it’s crucial that you keep your data in order. AI continually learns from the same data set, and corruption over time can cause its results to become increasingly unreliable. Employees should follow best practices for data recording and storage. Consistently clean and organized data allows AI to function optimally, reducing the risk of data corruptions over time, which can lead to unreliable results.
- Enlist your employees in mandatory refresher training: Even if your employees initially took technological training courses when the tool was debuted, ongoing training keeps everyone updated on best practices and regulatory changes. It also identifies knowledge gaps and empowers your team to handle fraudulent transactions effectively. Regular training sessions reinforce how important it is to stay current with any emerging AI developments and cybersecurity protocols. This also helps ensure that all team members are proficient in using AI tools.
Teaching your employees how their AI tools work, and the best practices for using them, will empower your team to identify, prevent, and handle fraudulent transactions more accurately than ever.
Interested in more about how cybercriminals are using AI to circumvent security and identity protocols? Javelin delved into this very topic in a recent report, Unmasking the Threat of AI: Deepfakes and Financial Security.