The AI system is also aimed at making assessment of external vehicle damage more-standardized and objective, reducing the potential for human claims adjusters on the scene to be influenced by the parties involved in the accident. We are opening up our AI capabilities to our partners, so that they can reduce cost and better serve small and micro-businesses," said Yin Ming, president of Ant Financial's Insurance Business Unit. Ant is initially offering the AI damage-assessment system only to insurers, but plans to make the product available to car owners within a year. Ant said its technology is pushing out the borders of the financial services sector's technological infrastructure and making the sector more-accessible.
Getting good labeled data to build a trained machine learning model is always a challenge. To run this in your azure subscription you'll need to create an Azure Machine Learning Workspace (note the workspace id), and retrieve the authorization token for that workspace. After doing separate experiments to identify the strongest determinant features we identify ACCS (Acceleration), LANECS (Lane Changes), TURNS (Turns) and BRKS (Braking) as the key features to include. From those we select: ACCS,LANECS,TURNS,BRKS Note: the the difference between TER and TERS is the TERS column (like all the columns with the extra S) is normalized data.
The "CONNECTED: Insurtech and IOT" event to be held in Milan on June 15th @ Microsoft House, will be centered around one of the most exciting topics of 2017: the evolution of the insurance world through new technologies like Artificial Intelligence, Social Networks, Augmented Reality and IoT. Gartner currently predicts some 8.4 billion connected things in 2017, to reach 20.4 billion Internet of Things (IoT) devices by 2020. The graph above by Venture Scanner highlights venture investing trends into the Internet of Things (IoT) sector, with data through April 2017. In the meantime Insurtech startups are creating new businesses in 14 categories with a total funding up to date of 18Billion according to Venture Scanner data, with $417 million raised between January and March 2017.
In addition to the plethora of data that insurers hold in their own systems, the Internet of Things, social media, and insurers' increasingly large ecosystems of partners and suppliers offer a wealth of structured and unstructured information that can be used to drive new business models, greater efficiency, and increased competitiveness. As a result, drivers can reduce their premiums and insurers can reduce risk, a win-win situation. Embedding machine learning intelligence into a cloud platform and applications supports more intelligent business processes. Discover Gartner's key findings and recommendations for insurance executives looking to drive digital transformation.
"Our experience working with insurers suggests that – by using machines instead of humans – insurers could cut their claims processing times down from a number of months to just a matter of minutes. But, when it comes to the advice and advocacy provided by an experienced broker, BizCover Managing Director Michael Gottlieb isn't convinced intermediaries are an endangered species. Suncorp's latest Insurance Insights white paper suggests that the automation of individual consumer products and small business packages is affecting the way that insurance professionals are recruited. However, BizCover's Michael Gottlieb approaches the human resource debate from a different angle, reflecting a more future-focused solution.
Apart from the likes of Google, Facebook, Amazon, and Tesla and their mainly digital business models and obvious applications of AI, a lot of traditional industries are employing intelligent algorithms to augment previously manual approaches. AI gives us means to automate processes, personalize products, communications and care, predict personal and collective developments, discover trends and unusual patterns in the data, and more. It has the potential to impact the insurance industry in numerous areas, such as marketing, customer interaction, claims processing, fraud detection, and underwriting. One use case for the application of AI lies in marketing and customer acquisition.
After watching just 600 hours of TV, an MIT deep-learning AI algorithm was able to predict future human interactions after two people met 60.5% as accurately as human subjects. IoT car safety technology already reduces crashes significantly and will save insurance companies $45 billion over the next five years in the United States alone. Source: "Usage-Based Insurance Expected to Grow to 142 Million Subscribers Globally by 2023, IHS Says," IHS Markit. Source: "Industrial Robots Will Replace Manufacturing Jobs – and That's a Good Thing," TechCrunch.
This means that providers can collect observational data from their users' everyday behavior and, by experimentation, identify which techniques and interventions are more effective. The Booking Experiences app learns over time and combines this knowledge with geo-location data to provide a traveler with increasingly personalized just-in-time suggestions to enhance the in-destination experience. Similarly, digital marketers use A/B tests to infer the effectiveness of different web and mobile app frames to generate leads. We can use data mining to extract information from data and knowledge engineering to extract knowledge from information.
In March this year, PWC released a report saying that 10 million UK jobs are at risk of being replaced by AI within 15 years. Insurance companies are already dinosaurs and while we will still need insurance, we don't need our current insurance companies. Those expensive on-site skilled jobs are gone forever, replaced by massive automation and AI from mining operations to plant operations to administration. Australia, as a home of the corporate oligopoly, suffers the associated elitism, complacency, lack of innovation and resistance to change which are characteristics of all oligopolies.