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."
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.
Themes ranged from providing more personalised products and services, better risk management, offering customers greater insights into their transaction, personalised money management systems and real-time AI chatbots. Axyon AI from Italy: offers Deep Learning-powered Artificial Intelligence solutions for finance businesses like hedge funds. Sentimer from Spain: Sentimer Technologies is an Artificial Intelligence chatbot platform for customer acquisition, cross-selling and service for banking, insurance companies and financial services providers. Spin Analytics from UK: Spin Analytics brings digital transformation in Credit Risk Management by leveraging predictive analytics, AI and ML techniques on Big Data.
In the United States, for example, insurance fraud--excluding health insurance fraud--incurs an estimated $40 billion in costs every year, boosting premiums across the board. As companies struggle to cut costs by mitigating the effects of fraud, predictive analytics algorithms scrutinize claims in a multistage process designed to help insurance companies efficiently detect and eliminate fraudulent activity by revealing insights into fraudulent patterns and claims data. By implementing IBM SPSS predictive analytics solutions, the Infinity Property and Casualty Corporation of Birmingham, Alabama, gained the ability to closely scrutinize claims histories, flagging suspicious claims for further investigation while fast-tracking legitimate claims. To learn more, discover the full scope of IBM SPSS predictive analytics capabilities.
More than half of today's insurance companies use machine learning for predictive analytics, according to a new report by Earnix, an analytics software provider for the financial services industry. Roughly 200 insurers were surveyed as part of Earnix's global "Machine Learning: Growing, Promising, Challenging" study, and they were prompted to select all business areas applicable to them. In total, 70% deployed the technology for risk modeling, the study found. Industry consensus is machine learning will bring significant change to insurance over the next five years, with 71% of companies believing investments in the technology will increase, Earnix says.
To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled data set) and social network learning (using a networked data set). He has done extensive research on big data& analytics, fraud detection, marketing analytics and credit risk management. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, and Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS.
A company has developed facial analytics technology to help estimate life expectancy by analyzing your face from a photo you upload online. "Your face is something you wear all your life, and it tells a very unique story about you," says Karl Ricanek Jr., co-founder and chief data scientist at Lapetus Solutions Inc. in Wilmington, N.C. Several life insurance companies are testing Lapetus technology that uses facial analytics and other data to estimate life expectancy, he says. Insurers use life expectancy estimates to make policy approval and pricing decisions. Many life insurance companies are exploring how to use additional data, statistical models, artificial intelligence and other techniques to help make quick decisions to ease the policy buying process and boost sales.
In some sectors, such as health insurance or life insurance, regulators can ensure that eligibility and pricing are not discriminatory among certain segments. As AI permeates more parts of our daily lives– automobiles, banking, insurance, and education, for instance – this threat is likely to become more serious. For example, any recommendation, pricing, or advice engine could go through a similar rigorous development, testing, and validation approach. For example, loans, health, or life insurance purchases, and the purchase of any high-priced good, such as a home or car, should undergo rigorous AI testing before recommendations get made.
Insurance companies are keen to adapt artificial intelligence in the enterprise, but first they must build up a layer of data and analytics excellence. "People are talking about not just big data, but fast data, how we need to get that data and make decisions," says Anand Rao, partner and innovation lead for PwC. That allows us to sell redacted versions of that data to insurance companies," he says. "In the underwriting process we're using information every day to make a decision," Billmeyer says.