AI & AUTOMATION: NLU Great minds: Could advances in AI redefine the industry?
A greater understanding Insurers handle a greater variety of data than possibly any other branch of financial services. Natural language technology to process and, more importantly, interpret it, will fundamentally change the way the industry operates, say expert.ai’s Daniele Cordioli and Chris Pearce from esure
Anyone familiar with the work of comedian Steve Coogan and the antics of his alter ego Alan Partridge, may recall a scene where the hapless DJ tries to order a cinema ticket on the phone, via a speech recognition bot. It’s 90 seconds of delicious torture, as he repeats the word ‘Inception’ over and over again, using different intonation and inflection with increasing levels of desperation and, ultimately, despair. It’s only when you start breaking down what computers need to be programmed with – or machine-learn – that you realise just how awesome our brains are when it comes to understanding the complexities of language, its context, nuance, sentiment and syntax – and then responding appropriately. Natural language understanding (NLU) is all about eliminating those elements of the written and spoken word that are lost in translation between us and ‘them’, thereby ensuring that the right outcome is achieved through natural language processing (NLP). It’s a branch of AI that has actually been around since the beginning of early computing and it’s said that, back in ffnews.com
the 1950s, a translation from Russian to English was the first example of a natural language application. It’s an apocryphal tale: a computer was asked to translate the biblical saying ‘the spirit is willing but the flesh is weak’ into Russian, and then back into English where it reappeared as ‘the vodka is good but the meat is rotten’. It was clearly early days… but it does illustrate the crucial role of NLU in recognising not just what has been said or written, but, more importantly, the intention behind it. A lot of water has flowed under the digital bridge between human and machine cognition since the 50s, of course, and machine-learned nuance and context inform exchanges in so much of our everyday lives and over multiple channels today. Developments in NLU allow us to have what feel more like proper conversations with virtual assistants like Alexa, Siri and Cortana and, as a result, they know, for instance, that we probably want a local weather forecast when we ask ‘Alexa, what’s it like outside?’, rather than a description of the street on which we live or indeed an existential summary of geopolitical events.
Understanding is key to progress of any sort – and it’s fundamental to how we leverage the sheer superior lifting power of computers when we want to do things faster, more accurately and more profitably.
Intelligent at the core The case for broader AI adoption in insurance had already been made before the events of the past two years brought any laggards abruptly face-to-face with it. In 2019, Lexis Nexis’ The State of Artificial Intelligence And Machine Learning In The Insurance Industry report, suggested those that had adopted AI and machine learning witnessed 88 per cent faster claims settlement, 88 per cent better cross-selling, 87 per cent better fraud detection, 85 per cent better risk scoring, 80 per cent improved pricing decisions, and 78 per cent improved profitability, when compared to those that had not. Three years on, insurance businesses must be brave enough to rethink their structure and model, rather than just tinkering at the edges with AI, says Daniele Cordioli, head of solutions consulting EMEA for Expert.ai, a specialist in NLU. They should put it at the core of their operations. Issue 7 | TheInsurtechMagazine
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