Speech Recognition and AI are providing new, effective technologies to drive better fraud prevention. While fraudsters are quickly developing new techniques to remain undetected, AI can evolve and adapt to these changes, providing more reliable protection. Companies have already started investing in AI technology in force, with 31% of CIOs reporting having already implemented AI, and 23% expressing the intent to deploy the technology in the next year.
How does voice recognition and AI work to combat fraud?
Machine learning is vital to anti-fraud AI systems – it is the process that enables algorithms and analysis to adapt to evolving fraudulent techniques. Through the application of data from previous interactions, machine learning operates different algorithms and processes to improve the technology’s functional capabilities over time with limited human involvement. Data that was previously difficult to understand or apply to anti-fraud measures can be effectively repurposed, allowing for various indicators of fraud to be detected. For example, checking for consistency in the details of a claim, discovering social connections between claimants and witnesses (through social media connections), and detecting more complex behavioural indicators.
Other AI such as Conversational AI, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR) are used to build on and augment machine learning models. Conversational AI facilitates automated, voice-enabled applications allowing more efficient technology-to-human communication. NLP bridges the gap between machine learning and the rules of human language, allowing the processing of sentiment and intent behind human interactions, and ASR allows for the translation of speech into different formats, assisting with the recording and processing of data.
The collaboration of these systems – combined with the efficiency of machine learning – enables a more comprehensive detection and prevention system against fraud.
What can these AI systems detect and analyse?
Modern AI systems have the ability to detect various speech, language, and behavioural traits during customer-facing interactions. Features such as indirect language, hedging, delaying, and frequent pausing have been traditionally associated with fraudulent calls, and can be detected via the use of AI analysis. AI will also detect abnormally high emotive indicators or exaggeration in fraudulent calls.
However, it is important to remain aware of the rapid evolution of fraudulent strategy. Organised fraudulent operations will always be seeking new ways to avoid detection and are swiftly adopting new technologies that assist them in bypassing voice recognition measures. Currently, biometric voiceprints can be taken of fraudulent callers, meaning that if they call again assuming another identity, they can be stopped. To combat this measure, fraudsters have been utilising “deepfake” technology, which can mask their voice in real time, creating a new biometric voiceprint. It is vital that businesses have the latest anti-fraud technology at their disposal to adapt to these new techniques.
How does the use of AI extend beyond fraud detection?
The capabilities of AI are constantly expanding. Sentiment and emotion analysis provide some of the newest developments to AI technology, allowing AI to detect and interpret the tone and sentiment in customer interactions, providing new insight into whether they are reacting positively or negatively to certain methods or communications. Information gathered about customer interaction, sentiment, and emotion can be a significant asset for businesses, providing the opportunity for evidence-based improvements to customer-facing operations.
Using wider behavioural analysis through AI is also an asset to the development of better safeguarding for vulnerable customers. Individuals who are identified as vulnerable – most commonly unemployed, young, or elderly adults – can be provided with the necessary attention and care, allowing employees to take the relevant measures to ensure their needs are met. Although some feel that the current shift into more developed voice analysis is intrusive, it allows companies to take better care over their customers, detecting potential vulnerabilities, and protecting those who are more susceptible to risk.
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.