According to a report by the Evening Standard, the technology, which is a ‘trait analysing software’, was built after the online lodging and homestay platform received complaints from hosts in London that some of their guests used their properties for rowdy parties. One such incident reported by an owner reveals that her £2.5 million flat was misused and wrecked by hundreds of drug-fuelled ravers, who rented the property ostensibly for a baby shower.

In 2019, Airbnb’s background check technology was revealed in a patent issued by the European Patent Office and published in the US.

The patent states that Airbnb could deploy its software to scan sites including social media for traits such as “conscientiousness and openness” against the usual credit and identity checks. Personalities like “neuroticism and involvement in crimes” and “narcissism, Machiavellianism, or psychopathy” are “perceived as untrustworthy”.

Last month, Google announced tests which argued its AI could beat human doctors at detecting breast cancer. The technology has potential for future applications and could actually enhance the accuracy and efficiency of screening programs, along with reduced wait times and stress for patients.

Around the same time, the company partnered with several conservation organisations to launch an AI-powered online portal “Wildlife Insights”, which has more than million photos dating back to 1990 and can access them and pinpoint the location of wildlife from anywhere. It helps collaborators to drop their own clicked images to map wildlife across the globe and grow the database.

The post Airbnb uses AI-enabled trait analyser to check if its customers are psychopaths appeared first on AI News.

Anadolu Insurance is using IBM technology for assessing its auto claims – specifically, the IBM Watson Visual Recognition service.

On average, Anadolu reviews around 1200 vehicle damage files each day. Manually checking each file was a slow and tedious process, but – with 63 percent contradicting the amount of described damage – it’s a vital operation.

Mehmet Abacı, Deputy Chief Executive Officer of Anadolu Insurance, said:

“Insurance is one of the primary industries that are affected by technological developments the most. As the largest and long-established company of Turkey, we are also a leader in the use of technology.

We want to reduce the analysis and repair processes to a few hours by using artificial intelligence in analysis of minor damages in our customers’ vehicles. Before that, we also performed such work and managed to pay our customers for minor housing damages such as glass breakage within five seconds. Now we have also started to use artificial intelligence technology in auto insurance more efficiently.

IBM Watson is helping Anadolu’s customers take optimal photos of their damaged vehicle. Affected parts are identified along with the scope of the damage for the AI to determine whether the parts need repair, replacement, or further expert consultation.

Volkan Sözmen, IBM Turkey Country General Manager, comments:

“We are proud that our long-standing collaboration with Anadolu Insurance has acquired a new dimension with this project. IBM Watson’s visual recognition capabilities will greatly contribute to making Anadolu Insurance’s processes more efficient and hassle-free for its customers.

We believe that this project will not only help the insurance industry gain new momentum, but also change the course of the digital transformation journey.”

Experts from Anadolu Insurance are working alongside IBM’s data scientists to train Watson with the language associated with damages and auto parts; such as buffer, door and mud-guards.

The insurance claims solution is expected to launch for contracted auto repair shops in the second half of this year.

/">AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , & .

The post IBM Watson speeds up insurance claims by 70 percent appeared first on AI News.

The insurance sector is therefore one which is tailor-made for this mission. Last April, Raconteur summed up this rising trend. “What is new is that data has grown in volume, quality and accessibility, and there is now the ability to combine and analyse multiple data sources – which is giving insurers plenty to think about,” wrote Sooraj Shah. “How quickly companies can adapt and ensure data science is a part of their organisation will determine how competitive they’re likely to be in the years to come.”

Pardeep Bassi (left) has been head of data science at LV= since June 2017, having spent all of his career in insurance in some capacity, firstly at AXA and then at Domestic and General. It makes for an interesting analysis to compare where the space has come in just a handful of years.

“Back then, it was called the innovation team – data science wasn’t a term widely used,” says Bassi of starting at AXA in 2012. “We were a statistically focused team looking at new technology and better ways to predict outcomes, predicting models using open source technology. The change which has happened now… [it] has become much more mainstream.”

“We’re at a stage now where we are scaling it up significantly and are encountering a lot more difficulties in terms of things we hadn’t known about last year”

LV= utilises this expertise across its general insurance arms, focusing on car, home, pet and travel insurance. Each of these use cases has differences, but the underlying template to build each of these models is reusable. One particular example was where LV= General Insurance worked with Microsoft to create a scalable machine learning solution to more easily solve the 20% of car insurance claims where liability is a grey area and claims can take up to 12 months to resolve.

Bassi notes that following the rollout, the company’s Net Promotor Score (NPS) had gone up while the average time to settle a claim had dropped ‘significantly’. “Not only are we making our processes more efficient from an operational perspective, we’re helping our customer experience,” he says.

This is not to say that everything is plain sailing, however. As with many companies, Bassi explains, the challenge is one of overhauling legacy systems. “From a technical point of view, we can build pretty much any algorithm we want to at the moment using open source technology – we have the right compute power, [and] because we’re in the cloud we’re able to spin up however much compute we need,” he says. “Where [we’ve] got legacy systems, [it’s] not only how do you build these models to start off with, but how do you actually integrate them back into existing systems to make sure they’re actually used and having an influence on the business?

“We’ve overcome this by having not only data scientists in the team but AI Analytics Engineers,” adds Bassi. “It’s their role to productionise and implement models – so they’ve got to understand existing systems and where it plugs in. A lot of the models we build require a real-time link to systems… so it’s understanding if the system is fit for purpose, can it accept API calls, does the latency have to be within a certain time window?”

Overall however, Bassi says that in the journey from first efforts to everything becoming machine learning-led, his team is ‘somewhere in the middle.’ Bassi spoke at last year’s AI & Big Data Expo where he said LV= was ‘implementing a couple of solutions’ – but he is back this year and is interested to not only explain his journey, but explore that of the audience too.

“A language is only a tool – what’s more important for a data scientist is having the core theoretical understanding of how a model works”

“Considering our own journey, there are various different stages of applying machine learning,” says Bassi. “The first stage is – can you get something live? The second stage is can you scale up, and how many different decisions can you affect? And the final stage is [where] everything is machine learning-led.

“We’re at a stage now where we are scaling it up significantly and are encountering a lot more difficulties in terms of things which we hadn’t known about last year,” he adds. “I think the rest of the industry will follow that same suit – so there’s going to be a number of people who were thinking about it last year who have now started doing it, and they can hopefully learn from the experiences that we’ve had.”

Much like Ben Dias, head of data science at Royal Mail, Bassi – who also holds a strong mathematical background – prioritises principles over the practical per se when looking for talent. “A language is a tool… I think what’s more important is having the core theoretical understanding of how a model works,” says Bassi. “Do you have that intuitive understanding right from first principles what a machine learning model is doing? Without that, you will really struggle to be a true data scientist and really know the limitations of certain algorithms.”

Pardeep Bassi is speaking at AI & Big Data Expo in London on April 25-26. Find out more about his session by visiting here.

The post Pardeep Bassi, LV=: On data science best practices and AI for insurance appeared first on AI News.

Insurance may be deserving of its reputation as archaic and old-fashioned; legacy systems continue to prove a headache and many internal processes are still conducted manually. We are often told of the masses of paperwork most underwriters still contend with. And yet, this reputation may become its saving grace; the opportunity to walk untrodden ground is attracting innovation pioneers from all industries.

Chris Castan, Head of AI – Digital Transformation at AXA and Alessandra Chiuderi, Group Head of Analytics at Generali, are no different.

Previously working in AI strategy, Chris Castan was drawn to insurance by the lack of innovative solutions he saw being implemented: “Insurance is well behind”, he begins, when asked what made him pursue a career change.

But where others may have simply seen an innovation desert and stayed well away, Castan saw opportunity. “Insurance has the most potential for AI disruption”, he continues; there are “not many industries that [AI] changes” as much, in terms of benefits for businesses and customers. For those aiming to merge these two facets, insurance appears to be the perfect setting.

Chiuderi agrees. Having previously worked in telecoms, publishing and media, she was always “attracted by the power of analytics on one side and the innovation aimed at improving customer experience” on the other. For them both, “the insurance industry was the ideal opportunity to combine the two aspects”.

Do not re-invent the wheel

On how prepared insurance companies are for wholesale embedding of AI, they were a little more hesitant (as most with substantial knowledge of the subject tend to be). According to Chiuderi, insurance carriers “are still in the process of shaping their digital transformation to be more efficient and provide customers with more flexible and personalised products”.

This requires companies to redefine how they work internally, including re-training or “adding new skills to existing ones”, since very few carriers have the means to hire a new data science or innovation team. Traditional roles must also change. Business teams often ask for the impossible from data scientists, due to lack of technical knowledge. If data scientists can “help the business understand the technology”, business teams can then narrow down their objectives to what is feasible, rather than idealistic. Otherwise, business executives may demand something that isn’t possible, or data scientists may produce a solution that the business does not need.

To become more flexible and not risk resources on doomed initiatives, Castan believes that companies should not be overly ambitious. Business and customer needs, as well as the technology, are changing so rapidly now that solutions can very quickly become redundant. “Have a vision”, yes, but match this “through small, incremental steps”. Those who attempt to “re-invent the wheel” with grand, ambitious projects may quickly find months of work and investment going to waste.

“The technology is there to give good returns”

Many carriers who have invested in AI are yet to see a return on their AI investment. Imperfect data and disparate, siloed legacy systems mean that the data that AI needs to create models and decisions is not readily available. There is work to do here, but also cause for optimism. Castan is admiring when speaking of the new breed of purely digital insurers who have emerged in recent years, particularly in east Asia. Having built their architecture with AI in mind, and employing it for all process automation, the resulting improvement in efficiency has produced “huge ratios”.

So, “when the set-up is there, it works”. But what can insurers do today? While newer methods of data organisation, such as ‘data lakes’, are often discussed in relation to AI, they also address the problem of legacy system data; centralising this data allows for more accurate “automated decision making [which] can save millions.” Chiuderi agrees that “automation is limited by legacy systems”, so provided the required infrastructure is in place, “the technology is there to give good returns”.

“AI makes us more human”

Discussing which business areas would see the biggest impact from AI, both agree that customer experience will see the most significant change for a variety of reasons. For Chiuderi, this rests in AI’s ability to perform time-consuming, “difficult or impossible” tasks. For example, software can analyse speech or thousands of images a second while a claims-handler is on the phone and alert them to any relevant issues.

While this does improve detection of fraudulent or inaccurate claims, the claims-hander can dedicate more time to the claimant, which is essential. As Castan puts it, insurers are “selling the promise that you’ll be there…in times of need” and if “AI makes us [as insurers] more human”, the customer can only benefit.

Whilst other areas such as pricing and underwriting will undoubtedly benefit hugely from advanced, AI-powered analytics, regulatory uncertainty around the use of external data means that neither expert can see past customer experience as the biggest beneficiary of AI. A prominent example of this is the disruptive insurer Lemonade. They are making the biggest noise, says Castan, not because they “changed underwriting” but because their technology-driven customer experience is (or was, at least) unlike any others.

Ask not what AI can do for you, but what you can do for AI

Castan will tackle the subject of AI from a novel angle when he addresses the Insurance AI and Analytics Europe Summit. Reprising his inner John F. Kennedy, he says that for too long, people have asked “what can AI do for me?”, not “what can I do for AI?”. Perhaps inspired by the successes of the new breed of digital insurers, he will explore how insurers can put the correct components in place now, so that AI can be effectively implemented in future. Addressing the scarcity of data-scientists, data architecture, decision-making processes and managing expectations from above, Castan will deliver a comprehensive outline for insurance carriers to lay the groundwork for AI success.

Taking this further, Chiuderi will focus on the strategy behind applied AI, relating to intelligent automation (IA) and customer engagement. While many organizations already employ robotic process automation, there is still uncertainty as to how and where this should be extended into IA. Through an exploration of immediately actionable insights, attendees will gain a detailed understanding of how robotic process automation can be augmented and intelligent automation embedded enterprise-wide. Chiuderi will also touch on how AI can improve customer engagement, using advanced analytics on structured and unstructured data for more accurate customer risk profiling, segmenting and greater product personalization.

Insurance Nexus is holding its 5th annual Insurance AI and Analytics Europe on 9-10 October 2018 in London. Readers of AI News can get an exclusive £200 off their tickets by visiting here and entering the discount code AINews200.

About the author: Gabriel is a content-writer at Insurance Nexus, focusing on the applications of AI and machine learning for insurance. Passionate about politics, the past and music, he also contributes to music and history blogs in his spare time.

The post AI-driven insurance: AXA and Generali on how the industry is catching up appeared first on AI News.