As artificial intelligence continues to impact our lives and the businesses we do business with, its evolution is providing new avenues for those who commit fraud. According to industry estimates, AI-powered fraud is expected to reach $10.5 trillion by 2025.
As organizations seek new ways to combat this fraud, they must be careful not to alienate customers in the process. A multi-layered anti-fraud framework powered by advanced AI and machine learning-based technologies achieves these objectives by proactively mitigating risk while minimizing losses from fraud. In a recent PaymentsJournal webinar, Max Spivakovsky, senior director of global payments risk management at Galileo Financial Technologies, spoke with Kevin Libby, fraud and security analyst at Javelin Strategy & Research, about how fraud risk is changing in the age of AI. We talked about how it evolved and what kind of organization it was.What we are doing to counter new risks
Where is AI heading?
Advances in AI and machine learning have opened the door to advanced modeling capabilities, advanced pattern recognition, and behavioral analysis. This generated adaptive learning of customer activities and behaviors. Financial institutions are also increasing their use of chatbots, equipped with intelligent digital assistants (IDAs) that interact with customers in real-time, to address emerging fraud risks. This is another step in the evolution of AI as it relates to fraud.
The natural language processing capabilities that this opens up, and the ability to process these models more quickly, have led to significant advances in available fraud control. Regarding the impact on customers, Spivakowski said leveraging AI to mitigate fraud can help financial institutions strengthen their overall fraud and risk analysis approach.
“Model creation is automated and learns recursively from previous experience, so there are fewer and fewer exceptions that require manual review over time,” Spivakowski said. “This is a huge benefit for commercial enterprises and financial institutions in that it frees up human capital that would otherwise be tied up in manual reviews. As a result, they can utilize their employees more efficiently and This will allow us to utilize our resources even more than before.”
Automation is especially helpful in dealing with the mountain of suspicious activity reports that financial institutions must submit each month. AI can create rules and models that digest many more testable parameters than traditionally manually created rule-based systems, allowing for the creation of more complex models.
take a proactive approach
A reactive approach will not allow you to stay ahead of payment fraud trends. To maintain their presence in the industry, financial institutions must develop a proactive approach.
A proactive approach allows you to detect anomalies faster than manual, reactive fraud and risk analysis.
“Link analysis and model accuracy make proactive approaches more accurate,” Spivakowski said. “Some of the examples currently available on the market are able to notify financial institutions and customers of possible fraudulent activity. So that they can save their financial means, they can replace the card or You can even limit some of your customers' spending. Being more reactive means staying up-to-date on the accuracy of your models.”
Proactive fraud prevention systems are set up to not only identify which payment cards have already been compromised, but also how many cards are likely to experience fraud due to the compromise. Masu. When using AI-powered fraud detection tools to make these types of predictions, the technology relies on rich data and learns from past fraud incidents. AI tools can better determine the scope of a potential breach and proactively identify accounts most at risk.
break down silos
One of the biggest challenges in securing multichannel systems is that each channel provides its own set of testable parameters to identify fraudulent activity. Some channels have more robust data to scrutinize than others, and some channels have little access to data when addressed individually. It's important to break down these silos and integrate your organization's efforts.
“Previously, you would have to create separate models to detect and prevent fraud on each channel without actually incorporating information about the last interaction with a particular user on another channel. “I did,” Libby said. “Things tended to be fragmented and siled. One of the strengths of AI-assisted decision-making, he says, is that the program can incorporate data from a variety of sources.”
The customer experience with chatbots and the ability for clients to lodge complaints about specific incidents in real-time allows organizations to translate this input into actionable methodologies within their operational realm or within their first line of defense. This allows existing models to learn more quickly.
Financial institutions now have more customer data than ever before, including incidents reported in real-time through customers using chatbots. By implementing AI and machine learning models, organizations can gain actionable insights from all this data, build a faster line of defense, and proactively stay one step ahead of fast-moving fraudsters. Masu.
“I usually talk about this issue in terms of a digital arms race, where criminals and cybersecurity and fraud experts are constantly trying to stay one step ahead of each other,” Spivakowski said. Ta. “The difference between what we've seen recently and what we'll see in the near future is that given natural language models, the pace at which we're trying to outpace each other is only going to increase. We're going to try to do that, but I hope that in the end we can find a way to stay ahead of the curve.”
In today’s digital environment, AI and machine learning-based fraud prevention technologies are essential allies for banks and fintech companies. By proactively identifying and stopping fraud, these advanced systems not only save significant costs from fraud losses, but also protect financial institutions' reputations from potential damage. . And their proactive approach not only strengthens security, but also instills confidence in customers and ensures a resilient and reliable financial ecosystem.
