As AI technology continues to advance at an unprecedented pace, the financial services industry finds itself at the forefront of both innovation and risk. One of the most pressing concerns today is the potential impact AI-generated voice impersonations will have on financial institutions.

This technology has emerged as a significant threat to financial institutions worldwide. In recent years, high-profile incidents (such as when an unnamed CEO was scammed out of $243,000) have highlighted the devastating impact of these sophisticated attacks, where hackers use AI-driven tools to create convincing voice deepfakes, leading to severe financial and reputational damage.

Financial institutions in particular are increasingly vulnerable to these sophisticated AI voice generated cyber threats, such as voice spoofing and voice phishing (vishing) - with deepfake incidents in the fintech sector increasing by 700% over the last year. Hackers leverage advanced generative AI technology, such as text-to-speech (TTS) systems and deepfake audio generators, to deceive and exploit both employees and customers into revealing sensitive information such as account numbers, passwords, and personal identification details.

Due to the valuable financial information and frequent interaction financial institutions have with customers, the space has emerged as an attractive and accessible target for hackers seeking to exploit trust and security weaknesses. The impact of these attacks on financial institutions can be profound, affecting not only their financial stability, but also their reputation and operational integrity.

As financial institutions strive to protect their clients and maintain the integrity of their services, the rise in voice phishing and spoofing attacks highlights the need for robust fraud prevention strategies to safeguard against evolving threats. By implementing multi-layered anti-fraud solutions, including advanced voice verification, liveness detection, ANI spoofing risk analysis, known fraudster blocklisting and speaker change detection, and augmenting the role of call center employees (who provide unreplaceable human judgment) to recognize and respond to phishing attempts, a financial institution's call center can be better protected against malicious actors.

One way that financial institutions can combat these scammers and enhance compliance and security is to leverage voice verification systems. When a customer contacts the call center, voice verification technology  compares the voice of the caller against a previously recorded voiceprint stored in the financial institution’s database. This voiceprint captures various attributes of the caller's voice, such as pitch, tone, and cadence, which are distinct to each individual. The system uses these attributes to match the incoming voice against the stored voiceprint in real-time. If the voiceprint matches, this provides an added layer of security and certainty that the caller is who they claim to be.

This process alongside additional anti-fraud measures enhances security by reducing the risk of fraudulent access but also improves customer experience by streamlining the authentication process and minimizing the need for additional security questions.

In addition to voice verification, AI models can play a crucial role in detecting anomalies in financial transaction patterns and communications. These systems utilize pattern recognition to identify deviations from historical transaction norms, while behavioral analytics allows them to flag unusual activities based on users’ typical spending habits and transaction frequencies. Real-time monitoring enables AI to swiftly detect and respond to anomalies by continuously comparing each event against expected patterns. Anomaly detection models, often based on unsupervised learning algorithms, help identify outliers and rare events indicative of fraudulent activity.

Additionally, natural language processing (NLP) techniques and behavioral biometrics analyze the content and context of communications, detecting suspicious language or requests. Through adaptive learning, AI systems refine their models over time, enhancing their detection capabilities with new data and feedback. This combination of tools and techniques gives financial institutions the ability to identify, target and adapt faster to newly found loopholes or fraudulent activities. Being able to find the right patterns early enough gives enterprises the edge on closing the gap before it is exploited and make fraud techniques increasingly expensive to maintain for potential fraudsters.

AI can also identify instances where a different person takes over the phone from the legitimate caller, after the legitimate person successfully authenticates, and escalate the call to an agent or specialist fraud team.

While AI is a critical tool for protecting financial institutions from fraud, automating routine tasks and streamlining operations, human agents still play a critical role in the call center. AI continues to improve to service the range of use cases, however, it’s not perfect and human oversight is still necessary.

This human touch is especially important in the case of an account being hacked. Should it happen, customers can be directed to human agents, to interact with professionals who are not bogged down by routine requests – thanks to AI – offering better, tailored assistance. These human interactions complement technological solutions, adding an extra layer of security and judgment that can adapt to unique situations or unexpected challenges in the authentication process.

Research from IBM found that organizations with fully deployed security AI, such as voice verification, and automation save themselves nearly $3.05 million per data breach compared to those without. Malicious actors are always at work and in today’s day and age, fraudulent activity such as voice spoofing or vishing is not a matter of ‘if’ but ‘when,’ which is why there has never been a better time for companies to enhance their security protocols.

Looking ahead, the potential for AI agents in the financial sector is boundless. In the next five years, we will see contact centers leveraging more powerful AI tools, enabling human agents to engage with customers in a more personalized and empathetic manner, significantly improving customer relations and brand loyalty, resulting in a better overall customer experience.