You own a small insurance company that sells accident protection and marine policies. Your business is growing quickly but faces one key challenge: There's no electric light. You own a small insurance company, your business is growing quickly, and you have a chance to take advantage of the biggest transformative technology since electricity: artificial intelligence. Fanuc is working on an unsupervised learning system, so A.I.-powered robots can learn skills on their own.
Ant Financial Services Group, a subsidiary of e-commerce giant Alibaba, last month introduced an automated system to assess car damage by scanning accident-scene images and calculating payouts on insurance claims. If there it's consolation to human, most pilot projects to date are tapping the capability of machines largely for assistant roles involving repetitive, high-volume and rule-bound tasks. Andy Gillard, Asia Pacific digital operation leader at EY, one of the world's "big four" accounting firms, has looked at the wide-ranging applications of Robotic Process Automation across the securities, banking and insurance sectors. Ant Financial's car damage assessment system builds upon the second level of artificial intelligence called "machine learning."
Far too often when a broker is sitting opposite a client or has them on the other end of the phone they are unable to draw up a single customer view that shows all the policies that the client has taken out such as for example home insurance, car insurance, business liability insurance, making it impossible for to offer timely and relevant offers. It is, however, possible for experienced system integrators to collect data from these different systems and form a single repository for a complete and organic single customer view. Add to this the contribution of Artificial Intelligence and companies will be enabled to further improve strategy and decision making across the business in an over-arching Business Intelligence framework. It is therefore highly cost effective to engage with a third-party consultant to help provide a roadmap of the process of improving user experience via greater digitalisation and to help implement or entirely outsource the process.
And some insurance pioneers are already taking AI to the customer frontline, using it to streamline claims, answer basic customer queries and, increasingly, to offer straightforward advice about complex products to customers in a codified and consistent manner. Whether deployed alone or to augment agents and employees, AI offers insurers the potential of significant efficiency gains and scalable ways to improve service. Such assistants will, in time, evolve to answer more difficult questions and support the sale of more complex insurance products. Spixii is in early testing for both P&C and life insurance sales.
As chief data scientist and senior vice president at American International Group Inc., Dalal is now applying technologies like computer vision, natural language processing and sensors to probabilistic risk analysis. The discipline of risk analysis that underlies insurance underwriting has historically relied on humans asking the right questions, but machine learning excels at letting the data determine what questions to ask. Machines can, and that's why Dalal sees so much promise in algorithms that find patterns humans can't. In the same way that a more complete correlation of temperature to o-ring failure might have prevented the Challenger disaster, machine learning works best when "we're not looking for the obvious risks.
"Passwords are a mainstay of conventional online authentication and are considered to be a binary control - if a consumer has the user ID and password, they are enabled to use the application," said Jim Routh, chief security officer at Aetna. The risk engine compares the benign behavioral attributes to the existing behavioral model and determines a risk score based on the match. The attributes have a weighting so if an attribute is not available then the other attributes are used by the risk engine to consistently produce a risk score. "The risk engine is using unsupervised machine learning to match attributes to the existing model, so the more data provided into a model the better it performs over time," Routh explained.
Last month, Ant Financial launched an AI-driven, image-recognition system to help the investigators of vehicle insurance claims do their jobs faster and better. According to Ant Financial, exterior damage claims make up about 60 per cent of the 45 million private vehicle insurance claims filed in China every year. In a demonstration, Ant Financial's algorithm took 6 seconds to assess the damage in 12 different cases, whereas human investigators needed over 6 minutes to reach a verdict over the same claims. "We use machine learning to detect frauds," Qi said.
Tom Singh OBE, a leading British entrepreneur and founder of fashion retailer New Look, has made a major investment in a machine learning (ML) software company, which helps the financial services and insurance industry make fast, informed, data-driven decisions. A notable feature of Logical Glue is their patented'white box' technology, as opposed to the more commonly used'black box', which fully explains the reasons why the ML system makes certain predictions. "It will give you a score back for its predictions in the same way that any other machine learning or statistical method would do", Robert De Caux, Logical Glue's Chief Product Officer told Access AI. "Where typical ML output would struggle", De Caux explained, "is that you could put a factor in and see how it would influence the probability of whether, for example, a debtor will repay or not, but it's not always clear at an individual level how much that factor was contributing relative to all the other things about that person.
For velocity, Complex even processing or stream processing allows us to handle the velocity of time; real time generated by countless sensors involved in every bit and inch of the supply chain process can be automatically fed into stream processing which uses defined algorithms to analyze it almost instantly. Even when the evidence starts becoming more and more corroborative and certain, the insurers may not collect adequate premium as actuarial modeling using historical claims data for ratemaking. Consequences of liability catastrophes cab include bodily injury, property damage or environmental damage Commercial general liability insurance covers such liability catastrophes usually. To improve our chances of collecting adequate premium so that insurers do not go bankrupt when a new liability catastrophe arises, big data tools with machine learning algorithms focused around emerging risk approach is being utilized.
Often called out for being slow to change, the insurance industry is beginning to catch up quickly on cognitive technologies. Often called out for being slow to change, the insurance industry is beginning to catch up quickly. AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation. Automating repetitive processes means tasks are completed quickly with fewer errors, opening up new opportunities for employees to focus on more customer-centric tasks.