A Conversational AI Approach to Detecting Deception and Tackling Insurance Fraud

This talk will present a real-world case study in the insurance domain that exploits speech and language to produce an explainable pipeline that identifies and justifies the behavioural elements of a fraudulent claim during a telephone report of an insured loss. To detect the behavioural features of speech for deception detection, we have curated a robust set of acoustic and linguistic markers that potentially indicate deception in a conversation. Statistical measures and machine learning were used to identify these linguistic markers in the right context. The explainable pipeline means that the output of the decision-making element of the system provides transparent decision explainability, overcoming the “black-box” challenge of traditional AI systems. This patent-pending technology, made possible through the support of funding from UK Research and Innovation (UKRI), is now part of a real-world commercial system, called LexiQal. This talk will outline the LexiQal approach to address the need for an efficient data-driven deep learning transparent approach (Explainable AI) to call analytics, an automated approach to forensic statement analysis, where there is a need to interpret the context of the spoken utterances accurately.