AI has rapidly progressed from an undeveloped concept to a highly effective technology with seemingly endless possibilities.
Adopting AI technology allows businesses to improve efficiency, functionality, and quality of service through multiple avenues, including effective fraud detection. AI’s potential is already apparent, with 31% of CIOs reporting having already implemented AI, and 23% expressing the intent to deploy the technology in the next year.
Which AI technologies are used to counter fraud?
The key features of fraud detection AI lie in conversational AI, natural language processing (NLP), and automatic speech recognition (ASR). Machine learning systems process the data provided, improving algorithms and analysis to detect indicators of fraudulent activity.
Conversational AI uses technology to provide automated messaging and voice-enabled applications, allowing people and computers to converse in a more human-like way. NLP combines machine learning models with human language models, facilitating conversational AI and other voice recognition technologies. ASR translates speech from a verbal format and identifies certain language features. Collaboratively, they comprehensively analyse customer interactions.
Conversational AI, NLP, and ASR can augment other AI and provide the foundations for machine learning – where the analysis of previous interactions is used to improve function over time. Data that has previously been difficult to understand is repurposed for analysis. Previous fraud can reveal indicators that may have revealed its presence in advance – for example, whether the recorded weather on the day of the claim matches the claimant’s story, or the presence of certain social interactions (such as whether the claimant and witnesses are digitally connected).
Machine learning also detects the patterns in behaviour, speech, and language that fraudsters often display, utilising this information to improve algorithms tasked with detecting potentially fraudulent activity. Calls with the intent to commit fraud can be flagged from the first point of contact, and individuals can be monitored if necessary.
Call centres have certain flaws that fraudsters exploit – when calling a company, they rarely speak to the same employee twice. Consequently, they can afford mild inconsistencies or mistakes in their stories while remaining detected.
Organised fraudulent operations are developing new techniques to defeat automated systems, utilising technology such as “deepfakes”, mimicking and masking a human voice in real-time. This allows them to avoid detection through biometric voiceprints. However, AI-based algorithms have developed quickly, allowing businesses to tackle these latest techniques.
What features does AI detect and how do they indicate fraud?
AI can incorporate the analysis of speech, language, and behavioural patterns into its records. Certain features commonly identified in fraudulent activity can be quickly detected, such as negation, hedging, over-emotional responses, and latency, which are frequently found in the speech of fraudulent callers.
They will often be indirect in answering questions and may react more emotionally when they feel they are being over-interrogated or suspected of fraud. While a call handler may be too busy to notice these issues, AI can fill this role instead, allowing employees to focus on the work at hand.
What are the wider benefits of using AI?
Anti-fraud AI and machine learning can assist businesses beyond fraud detection. Sentiment and emotion analysis can integrate into AI systems, allowing businesses to better understand the customer experience through analysing positive or negative indicators, urgency, or confusion detected in the language they use. This information can then be used to identify where customer service needs improvement or review.
Wider behavioural analysis can even be deployed to safeguard vulnerable customers, detecting whether the appropriate questions have been asked and if customers are confused or uncertain. Although the amount companies can learn from voice analysis may feel intrusive, this technology also allows early intervention in the case of health or welfare issues. If concerns are detected, reassurance and welfare checks can be provided where necessary.
– Originally written by Nigel Cannings, Founder of Intelligent Voice –
