Speech recognition and AI technology are at the forefront of the fight against fraud in the insurance sector. As fraudulent individuals and organisations evolve, so does the software required to battle them, with new approaches integrating machine learning to combat fraud effectively and systematically. Voice recognition is pushing the boundaries of fraud detection to include the analysis of audio and video data, ensuring that even in the increasingly technological world of customer interaction, fraud does not go unnoticed. Updated anti-fraud measures utilise artificial intelligence to detect features such as emotion, tone of voice, and speech patterns to identify fraudulent intent from the first phone call. Businesses are rapidly adopting these new measures to protect both their business and their customers.
What is the role of machine learning and voice recognition in fraud prevention?
There are several forms of voice recognition technology that factor into fraud prevention. These processes integrate into AI machine learning to provide automatic structuring of both audio and video data, which can cover both call centres and, increasingly, online meetings. The utilisation of NLP (Natural Language Processing), which facilitates the understanding of the human language by computer programs, helps businesses identify risk of fraud through technology. Computers are now able to comprehend the human language to a far greater extent, making the collection of audio and video data more accurate and easier to process. The data provided from NLP can also assist in the understanding of regulatory compliance and sales opportunities, all from the same dataset.
Machine learning plays a vital role in the utilisation of anti-fraud data. Computer algorithms can build an adaptable model from the data collected through voice recognition technology, capable of improving through experience and further data collection. The integration of machine learning into anti-fraud technology is a valuable asset in the race to keep pace with fraudsters.
These approaches to machine learning and voice recognition technology are extremely evident in new anti-fraud technology such as LexiQal, where contact centres are fortified with behavioural analytics that can detect fraudulent intent at the earliest possible contact. Integrated into existing fraud detection solutions, these behavioural analytics provide an end-to-end fraud management strategy with more comprehensive anti-fraud coverage. Overall, this makes it easier for insurance companies to detect fraudulent activity without impeding on the ability of employees to perform their customer service duties – a drastic improvement from outdated anti-fraud strategies.
What features can AI now recognise, and how does this factor into fraud?
AI has rapidly developed in recent years, now able to recognise features such as emotion, tone of voice, speech patterns, and more. It has been established that fraudsters display certain trends in their behaviour and speech characteristics. Features such as negation, latency, and extreme emotion are often missed by even the most highly trained staff. However, AI can now identify these features where people cannot, allowing employees such as call handlers to focus on providing the ideal customer service experience while fraud detection is taken care of.
Within call centres and other points of contact, customers will rarely speak to the same person twice. For fraudsters, this is an opportunity to be exploited. Inconsistencies in their stories are far less likely to be detected, and the repeated presentation of suspicious features may fly under the radar. AI and automated fraud detection systems allow for improved pattern recognition – another crucial aspect of anti-fraud strategy. Records of their claim and any suspicious language or speech characteristics can be automatically created as each point of contact occurs, providing call handlers with a more accurate customer history and warnings for potential fraud.
How can the use of AI extend beyond fraud detection?
Despite the increasing pressure on fraud detection, it is vital not to lose sight of the importance of the customer experience. These two priorities must be balanced. If fraud detection becomes too dominant in customer interactions, individuals may be placed under excessive scrutiny. This can make these customers feel uncomfortable in their interactions with the business. Simultaneously, if employees are under extreme pressure to identify fraud, they may not be able to provide the correct quality of service. This adds to the total cost of fraud, as genuine customers can begin to reject company services. The use of AI and voice recognition technology empowers customer service agents to serve their genuine customers, while automated fraud detection can monitor interactions in the background. As a result, anti-fraud strategy can be strengthened without negatively impacting the customer experience.
The same systems used to detect fraudulent intent through behavioural and language features can also be utilised for customer protection. Vulnerable customers often go undetected, not receiving the support that they need from insurance companies. Speech characteristics and key phrases indicating discomfort, confusion, or susceptibility can also be identified by AI and voice recognition. When deployed in customer interactions, individuals can be flagged as vulnerable or at-risk, and additional provisions can be made for their safety and security to ensure that they are receiving the best possible service.
Integrating AI into financial services additionally assists in regulatory compliance, reducing unnecessary costs such as fines. The insurance industry is under heightened scrutiny in the enforcement of regulatory compliance and customer protection. Updating and improving data collection through voice recognition and AI integration facilitates improved record keeping, making it easier for businesses to present evidence of compliance to the relevant regulatory bodies.
Both voice recognition and AI are crucial in meeting the increased demand to combat fraud. The modernisation of anti-fraud strategy – combined with vastly more efficient data collection and processing – enable insurance companies to meet these demands without compromising the quality of customer interactions.
Nigel Cannings is the CTO at Intelligent Voice. He has over 25 years’ experience in both Law and Technology, is the founder of Intelligent Voice Ltd and a pioneer in all things voice. Nigel is also a regular speaker at industry events not limited to NVIDIA, IBM, HPE and AI Financial Summits.