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.
Speech Recognition and AI are cementing their place in anti-fraud intelligence for the insurance industry, providing new opportunities for comprehensive, rapid, and effective detection of fraudulent action and intent. According to a 2021 survey, AI has already been deployed by 31% of insurance CIOs, with 23% reporting the intent to also deploy this technology in the next year. Across the next two years, 21% of respondents from a survey undertaken by the Coalition Against Insurance Fraud have also reported the intent to invest in anti-fraud focused AI systems. Although the adoption rate of AI systems has been high, many companies fail to effectively utilise their unstructured data – information that can be significant to the validation of customer claims. Effective use of these new AI and Speech Recognition systems can help to tackle this problem through the integration of processes such as machine learning, automating certain processes in the verification process while creating and evolving algorithms to better detect fraudulent intent.
What are the core AI systems and processes in anti-fraud technology?
The term AI refers to an extensive range of technologies. However, the most relevant processes to anti-fraud intelligence are Conversational AI, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR). Conversational AI provides automated messaging and voice-enabled applications, which facilitates conversation between people and computers. To enable processes such as Conversational AI, NLP combines human language models with machine learning to better understand and interpret interactions. ASR then provides the means for translating speech from a verbal to text format, with the ability to identify certain language features. Collaboratively, these processes deliver comprehensive anti-fraud measures, improved customer interactions, and accurate compliance monitoring, especially when integrated with machine learning AI.
What is machine learning and how is it being used?
Machine learning AI analyses precious interactions and its recorded history to improve its function over time, evolving to continuously meet the changing nature of customer interactions and provide AI-based algorithms for anti-fraud measures. It can utilise previously neglected unstructured data, repurposing it for analysis. Any previous instances of fraud can reveal certain indicators that may have revealed fraudulent intentions in advance – machine learning can monitor these indicators with minimal human involvement.
What is AI able to recognise and how does this help prevent fraud? AI has developed the ability to detect numerous verbal and behavioural indicators to assist in fraud detection. Through the integration of machine learning into other voice-based AI systems, algorithms can be tasked with identifying these indicators in real-time, making it possible to flag calls with the intent to commit fraud as early as from the first call. Individuals who have displayed fraudulent intent through these indicators can be monitored, and their claims investigated in greater depth. For example, AI systems can check whether the recorded weather on the day of the claim matches the claimant’s story, or whether the claimant and listed witnesses are connected socially.
The use of biometric voiceprints ensures that despite callers rarely interacting with the same employee twice, they remain identifiable regardless of altered details or claims. However, organised fraudulent operations have initiated the use of “deepfake” technology to mask their voice in real-time. This allows them to avoid detection through biometric voiceprints. Nevertheless, AI-based algorithms are developing rapidly, allowing businesses to tackle these issues at a faster rate.
How can AI help improve the customer experience?
The uses of AI extent beyond fraud detection, also proving invaluable for improving the customer experience. The integration of sentiment and emotion analysis allows businesses to better understand the customer experience, determining the nature of interactions and the satisfaction of callers. The information provided can inform businesses of where improvements to the customer experience are required.
Wider behavioural analysis also provides safeguarding opportunities, allowing businesses to identify potentially vulnerable customers (such as the very young, unemployed, or elderly) and offer them increased reassurance. If customers appear particularly confused or uncertain, welfare checks can also be arranged.
How should AI be applied for compliance and monitoring within the insurance industry?
It is vital that alongside the implementation of AI technology, businesses apply the appropriate legal requirements and regulations. Voice recorded calls should be prefaced with a message (often automated) alerting customers to the purposes of the recording to comply with two-party consent (where all present in the call are aware and consenting).
AI can also be applied to monitor wider regulatory compliance through the gathering and subsequent analysis of data, automation, and risk management. Companies are required to uphold a standard of data management. AI provides the framework for the precise collection, organisation, and processing of information to meet the necessary regulations, without the need for the unnecessary translation from audio to text format that is notorious for inaccuracies.
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