If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Good scientists are not only able to uncover patterns in the things they study, but to use this information to predict the future. Meteorologists study atmospheric pressure and wind speed to predict the trajectories of future storms. A biologist may predict the growth of a tumor based on its current size and development. A financial analyst may try to predict the ups and downs of a stock based on things like market capitalization or cash flow. Perhaps even more interesting than the above phenomena is that of predicting the behavior of human beings.
Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).
In Property and Casualty Insurance, information is the currency that drives pricing, claim loss prediction and prevention, risk management and customer experience. Each year approximately 10% of AXA customers experience a loss. AXA used existing information such as customer demographics and historical claim data, local and regional data, and other external data with machine learning to identify those customers who were likely to experience a loss in excess of $10,000 so that they could price it appropriately. They began by identifying 70 risk factors; driver age, address, vehicle type, prior loss history, vehicle age, original purchase channel …etc.
Image classification AI within the app compares the customer's photos with thousands of other anonymized crash photos to generate a cost estimate for their repair. At Solaria Labs, the innovation incubator for Liberty Mutual Insurance, a team is developing machine learning tasks to enable safe routing and parking for drivers. Right now, if you were to ask a virtual assistant "Call Brenna, no, call Nora," most virtual assistants will call Brenna first, or ask you to repeat your request. The more natural and seamless the customer's interaction with the technology (think of interacting with a virtual assistant), the less effort it takes them to accomplish a task, and the more likely they are to accomplish that task.
Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. What have been the leading factors enabling recent advancements and uptake of deep learning? Yuanyuan: Customer experience could be significantly improved using AI by analyzing individual level attributes to make traditional service much more tailor-made. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it.
Technology company Nauto has entered into agreements with BMW i Ventures and Toyota Research Institute, as well as with Allianz Ventures, part of the leading global financial service provider and insurance company Allianz Group. Under the agreements, Nauto and its auto and insurance industry partners will license data and technologies, including Nauto's artificial intelligence-powered vehicle network. Insurers get a more precise view of each and every driver that can help personalize coverage, deliver precision risk assessments, reduce fraudulent claims and provide enhanced urban mobility services for commercial fleets. Nauto's artificial intelligence platform drives deep learning that goes beyond the basics of recorded events by capturing driver behavior, inside-the-vehicle activity and correlations from road, weather and traffic conditions.
Here's more: A test driver "operating" a Google Lexus-model autonomous vehicle on September 23 was fortunate to escape with no serious damage. We're talking about solving and integrating concepts such as computer vision, deep learning, machine learning, and latency. We're talking about solving and integrating concepts such as computer vision, deep learning, machine learning, and latency. Jon Hilsensrath of The Wall Street Journal, a reporter with particularly strong sources inside the Marriner S. Eccles Federal Reserve Board Building, wrote Friday morning, "The subdued September jobs report ensures the Federal Reserve won't be raising short-term interest rates at its November meeting, a week before the U.S. presidential election, and creates a new thread of uncertainty about its action in mid-December."
As the number of applications grows, a strong ecosystem is forming around the connected car, involving a range of participants--among them automakers, insurance companies, telecommunications firms, sensor and chip manufacturers, and digital-platform giants like Amazon and Uber, as well as academic institutions and standards-making bodies. The rise of this ecosystem is changing the competitive landscape for all participants, especially for companies in the insurance industry. Insurers face digital disruption in a number of areas. Their analytics capabilities, for instance, may be displaced by predictive-modeling or machine-learning technologies. And their traditional data sets, which contain risk profiles based on claims history, may be losing value given the growing availability of real-time data streaming from connected cars.
In the long run AI, will completely change our investment industry, but (certainly on the institutional investment side) we are only at the beginning of a long and slow transition of 50 years. Financial advisory is another under developing area, where in future, individuals could expect a machine to suggest best investment portfolios based on their own family balance and consumption behaviors. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it. To continue on the above examples, advances in NLP assure that bots will be able to handle simple tasks in customer service and AI systems will on the other hand provide automated information from news, press releases and other textual documents to prices.
Here's a comment from a January article in Nature that describes deep learning's step forward. Traditional machine learning techniques require us to put a great deal of energy into these types of questions. While we are excited about what we might achieve, our expectations are tempered by the caveat that deep learning generally requires vastly more data than traditional approaches to machine learning. It is in this area of modeling sequences of data through time that machine learning has recently made huge steps forward.