Business Advice · 29 November 2021

How Can AI Protect Your SME From Fraud?

AI

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.


 
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