Artificial intelligence is quickly becoming essential for SMEs looking to effectively manage the risks of fraud.
In the insurance industry alone, 31% of CIOs have reported having already implemented the use of AI, with 23% expressing intent to deploy the technology in the next year. The latest technology facilitates the performance of tasks at optimal efficiency, removing the hindrances of human error and time constraints. Through machine learning, sentiment analysis, and voice recognition, AI is working towards an improved understanding of fraud, customer interactions, and data management.
What roles do machine learning and voice recognition hold in preventing fraud?
Machine learning is the process of operating different algorithms and programs to learn using previous interactions, allowing the technology to improve its functional capabilities over time with limited human involvement. This ensures that data which was previously hard to understand or apply can be effectively repurposed in the analysis of customer-facing interactions. This can involve examining previous instances of fraud and building a system that analyses the indicators of potential fraudulent calls. For example, simple patterns such as what the weather was on the day of a claim can be noted, social interactions (such as connections between claimants and witnesses) can be identified, and more complex behavioural cues such as reactions or retaliations can be detected.
These machine learning models are often built on or augmented by other AI, including Conversational AI, Natural Language Processing (NLP), and Automatic Speech Recognition (ASR). Conversational AI facilitates voice-enabled applications and automated messaging, allowing computers to communicate with people (for example, via chatbots and automated phone systems). NLP combines language rules and interaction with machine learning, allowing AI to process the meaning of and sentiment behind human interactions. NLP often works in conjunction with Conversational AI to better interpret the wants and needs of customers. ASR facilitates the translation of speech out of a verbal format, enabling better storage of data collected from customer interactions, particularly through digital communication.
These different systems are collaborative – when applied together they provide a comprehensive analysis of customer interactions and identify fraudulent calls from the first call.
What are the features that AI can detect and analyse?
Modern AI detects several speech and behavioural patterns, analysing the language and mannerisms of customers during customer-facing interactions. Pauses in speech, indirect answers, and hedging or delaying responses are frequently identified in fraudulent callers. They commonly adopt these mannerisms to maintain a convincing story with consistent details. AI detects these behaviours during a call, recording data about the customer who is behaving suspiciously – the call handler can be alerted to potential fraud, and customer records can be updated with warnings in case the fraudulent caller returns, calling another employee hoping to remain under the company radar. AI will also detect abnormally high emotion and exaggeration in the language of fraudulent callers – features also identified to indicate fraud.
These systems are not static. Organised fraudsters rapidly adapt, applying new techniques to defeat automated systems. Biometric voiceprints can currently be taken of known fraudsters, tracking them if they attempt repeat calling with different details. To overcome this, the use of “deepfake” technology has risen, which mimics audio and masks a human voice in real-time, creating a new identity for fraudsters. Businesses must have the technology to keep up with these new techniques, helping detect and defeat them.
How Can AI Help Provide Better Overall Customer Satisfaction and Protection?
The capabilities of AI are rapidly expanding, now including sentiment and emotion analysis. This allows AI to detect and interpret a customer’s tone, determining whether they react positively or negatively to an interaction, are relaxed or speaking with a sense of urgency, and whether they are content and confident with their result. Information sourced from sentiment and emotion analysis has several important uses for SMEs. It can help improve the quality of service available to customers and allow call handlers to better target customer needs. It also allows businesses to gain a deeper understanding of their brand image, especially if trends emerge during customer interactions.
Wider behavioural analysis also helps in safeguarding vulnerable customers. If individuals are identified as appearing confused, uncertain, or concerned, employees can be notified of the resulting problems that could arise. These more uncertain customers – often unemployed, young, or elderly adults – can be provided with a more in-depth explanation of how the business is serving their needs, with follow up information or contact being booked accordingly. It is then possible to provide reassurance and, where necessary, a welfare check.
While we are witnessing a shift into a more intrusive world where companies can learn more about us simply from the way we speak, this same technology also allows us to better care for customers, detecting health issues, and protecting the vulnerable in society.