HOW ARE SPEECH RECOGNITION AND AI FIGHTING FRAUD?

Nigel Cannings is the founder of Intelligent Voice

 

Speech recognition and AI provide innovative methods for businesses to significantly develop and improve their fraud detection systems. With the technology and techniques used by fraudsters rapidly changing, AI can evolve and adapt to provide more comprehensive protection, assisted by the use of machine learning. The acceptance of AI as a crucial asset to fraud detection and prevention is already being recognised, with 31% of CIOs having already reported the implementation of AI systems in their business, and a furth 23% expressing intent to have the technology deployed within the next year. Crucial to the effective implementation of this technology, however, is having a basic understanding of how it functions and will assist business needs.

 

What are the roles of AI and machine learning in fraud detection and prevention?

AI can take a variety of forms, with the core systems required for anti-fraud measures being Conversational AI, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR). Automated, voice-enabled applications rely on the use of Conversational AI to allow efficient communication between technology and humans. ASR is the model tasked with translating verbal data into different formats, facilitating the recording and processing of data. The crucial bridging of the gap between the rules of human language and machine learning is carried out by NLP systems, allowing technology to process the sentiment and intent that can be derived from human interaction.

Together, these AI systems are used to both develop and augment machine learning models. The machine learning process involves the application of data from previous interactions with the intent to enable algorithms and analysis to develop and evolve alongside rapidly changing fraudulent technology and techniques. Through the collaboration between machine learning, Conversational AI, NLP, and ASR, data that would have previously been considered difficult or impractical to apply to anti-fraud measures can be repurposed. Fraud detection procedures such as checking for consistency in the details of claimant stories, identifying connections between claimants and witnesses that may be problematic, or detecting more complex behavioural indicators can be carried out more effectively, enabling a more comprehensive anti-fraud system.

 

What are the features that AI can recognise, and how does this help prevent fraud more efficiently?

Modern AI systems have the capabilities to detect a range of both speech and behavioural patterns, providing a more comprehensive analysis of the mannerisms and language features displayed in customer-facing interactions. There are several features that have been traditionally associated with fraudulent intent, with the most notable being frequent pauses in speech, hedging, delaying responses, indirectly answering questions, and displaying heightened emotional responses. AI not only has the ability to detect these traditional features of fraud, but it will also use its recorded history of confirmed fraudulent calls to continue tracking trends in behaviour and speech by fraudsters. Customers who have been identified to be displaying suspicious behaviour can be more closely monitored, and if the potential for fraud is confirmed, customer records can be updated with the necessary information and warnings concerning their claim. Currently, it is possible to also use AI systems to record a biometric voiceprint of known fraudsters, allowing their detection even when they call back with a new claim and different details. Through these measures, it can be possible to detect fraudulent intent from the first phone call.

However, it is important to be aware that these systems and tactics are not static, and constantly evolve depending on the new techniques being adopted by fraudsters to avoid detection. The most recent development in fraudulent operations is the use of “deepfake” technology, which can be used to mimic audio and mask a human voice in real-time. This allows fraudsters to create entirely new identities to recommit fraud with the same company, without being detected by biometric voiceprint technology. Traditional anti-fraud measures without the input of AI and machine learning will struggle to adapt to these new technological challenges. AI-based systems provide the flexibility and adaptability to allow businesses to keep up with these evolved techniques quickly, often with minimal human involvement.

 

How can speech recognition AI impact wider business goals?

The reach of AI is not limited to efficient fraud detection – important business goals such as the improvement of customer services also benefit significantly from the implementation of AI-based systems. Functions such as sentiment and emotion analysis now allow businesses to detect and interpret the nature of customer experiences, identifying positive and negative language and speech indicators. This enables businesses to gain a better understanding of their customer interactions and where improvements or reviews may be required. This form of analysis can also provide more detailed information about whether customers are displaying a sense of urgency, frustration, contentment, or confidence in response to their experience. Details provided by this analysis allows businesses to create more specific targets and methods to increase customer satisfaction.

Implementing wider behavioural analysis through AI systems also provides new opportunities for businesses to provide improved safeguarding for vulnerable customers. Employees can be notified when customers are displaying worrying indicators of being uncertain, confused, or concerned as a result of their interaction, and respond accordingly. These more vulnerable customers are often unemployed, young, or older adults that may require a more in-depth explanation of how the business can serve their personal needs. Follow up contact, reassurance, or in more extreme cases, welfare checks can be provided to these customers. The introduction of more thorough AI-based analysis can feel more intrusive to some customers – however, this technology also enables the provision of better customer care. The shift towards more analytical, adaptive technology increases our capabilities to care for the most vulnerable in society.

 

Nigel Cannings is the founder of Intelligent Voice, a company leading the international development of proactive compliance and technology solutions for various forms of media. His experience in both technology and law provides a unique insight into the future of these technologies and the legalities surrounding them.

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