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.
Insurers 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.
In this piece we take a look at some interesting African startups involved in developing artificial intelligence (AI) solutions. South African startup DataProphet last year received a significant investment of an undisclosed amount from Yellowwoods Capital Holdings to expand its international offering. The company uses AI to understand user requests, drive conversations, understand user spending habits and prevent fraud. Founded by former equity-derivatives specialist Annabel Dallamore (pictured here) together with Julian Dallamore and Mark Karimov in 2013, Johannesburg startup Stockshop.co.za offers a number of artificial intelligence solutions to financial institutions such as banks and insurance companies.
In today's world where companies are actively looking to use artificial intelligence to replace human intelligence (jobs) more and more, ensuring you stay as sharp as possible is more critical than it's ever been. The new requirements for staying competitive are accelerated learning, deep creativity, invention and entrepreneurship and most importantly, a belief (algorithm) in one's self to think they're capable of more advanced work. Billions of dollars are being invested in companies replacing humans in higher skill jobs with AI and robots more and more every single day. Just recently, the Japanese insurance company, Fukoku Mutual Life Insurance replaced 34 human insurance agents with artificial intelligence and is citing that productivity will increase by 30%.