London-based insurtech Concirrus has launched a new artificial intelligence-powered cargo data analytics solution in partnership with brokers Marsh JLT Specialty and Willis Re Specialty. The company claims it has the potential to "revolutionise" the way cargo insurance is priced, selected and managed in the industry. The new solution, called Quest Marine Cargo, will be available from first quarter 2020, and will fully integrate with Concirrus' existing suite of hull and P&I capabilities. It will provide full analytics on the entire cargo journey - from factory gates to customer warehouse using advanced AI combined with the latest cargo monitoring. Concirrus said the launch is a part of its wider innovation strategy which is centred around developing solutions that deliver greater efficiencies to the 300-year old re/insurance industry.
With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business. A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business. ML is a data analysis process which leverages ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for. With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Surprisingly, we all have witnessed ML without actually knowing it.
The OCBC Mobile Banking app now offers an artificial intelligence-powered, voice-based virtual assistant, which has already helped with over 20,000 requests made via voice since the feature's launch last August. In half of these requests, the customer was seeking information about spending categories and budgets; another 30% concerned past banking transactions. Other mobile banking services that were performed using voice included locating ATMs, paying bills and changing banking PINs. The new voice-activated banking service, which was developed and trained over 13 months and is called the OCBC Banking Assistant, 'lives' in the OCBC Mobile Banking app. The customer speaks to the assistant as if conversing with a human assistant – and the requested banking task gets done.
As more and more banking institutions look to find out more about the importance of artificial intelligence, app developers are playing a much larger role. App developers are able to guide their clients in the proper direction. They are at the cutting edge of all new technologies. Banking institutions that do not take the time to meet with app builders are placing themselves behind the proverbial eight ball. They are not going to be able to enjoy all of the benefits that artificial intelligence has to offer.
The insurance industry has been very risk averse industry. Traditional carriers have been typically laggard in adopting emerging technologies until 2010s. However, insurance companies has not been new to big data, predictive analytics and modeling. They have been designing, selling and servicing data products to many industries and consumers. With advent of AI and technology capabilities to mine unstructured data from several new sources and sensors, insurance industry is going through a significant disruption.
AI and RPA are only beginning to transform how business is done in the insurance industry. We can expect to see burgeoning usage in operations, customer service, risk assessment, and mitigation and regulatory compliance. Insurance companies are only beginning to harness the potential of artificial intelligence (AI) and robotic process automation (RPA). AI refers to computer systems that can mimic human capabilities by learning and solving problems. RPA is an emerging form of business process automation technology based on using software robots or AI "workers."
Telstra's independent venture capital arm has shown its intention to expand into the artificial intelligence data market following a $US100m (145m AUD) capital raising for San Francisco company Trifacta. Trifacta employs machine-learning technology to deduce a greater depth of insights from the increasing level of data migrating to cloud-based storage. Australia's largest venture capital fund, Telstra Ventures Fund No 2, led the investment, joined in the round by the likes of Energy Impact Partners, NTT Docomo, BMW Ventures and ABN AMRO. Telstra Venture joins a long and credible list of existing investors from Accel Partners, Greylock Partners, Ignition Partners and Google. "The share register for Trifacta is very impressive. It is great to have so many experienced and impressive co-investors in this deal. That is a really massive plus for us," Mr Koertge said.
The US labor market looks markedly different today than it did two decades ago. It has been reshaped by dramatic events like the Great Recession but also by a quieter ongoing evolution in the mix and location of jobs. In the decade ahead, the next wave of automation technologies may accelerate the pace of change. Millions of jobs could be phased out even as new ones are created. More broadly, the day-to-day nature of work could change for nearly everyone as intelligent machines become fixtures in the American workplace. Until recently, most research on the potential effects of automation, including our own, has focused on the national-level effects. Our previous work ran multiple scenarios regarding the pace and extent of adoption. In the midpoint case, our modeling shows some jobs being phased out but sufficient numbers being added at the same time to produce net positive job growth for the United States as a whole through 2030.
Loading in and preprocessing a data-set in ML.NET is quite different than when working with other machine learning packages/frameworks because it requires us to explicitly state the structure of our data. To do so we create a file called ModelInput.cs inside a folder called DataModels. Inside this file, we will state all the columns of our data-set. For this article, we will use the Credit Card Fraud Detection data-set which can be freely downloaded on Kaggle. This data-set contains 31 columns.
The graph represents a network of 2,183 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 04 September 2019 at 11:32 UTC. The requested start date was Sunday, 01 September 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 8-day, 2-hour, 46-minute period from Friday, 23 August 2019 at 07:01 UTC to Saturday, 31 August 2019 at 09:47 UTC.