Internet and technology make it easier for businesses to develop and reach a larger range of their target market. The internet has truly become the backbone of business in the 21st century and is almost impossible to ignore. Although, as technology develops, so do cyber risks such as AI cyber-attacks. Cyber-attacks are getting more innovative and very common in the business world, from phishing emails, ransomware attacks and now deepfake AI cyber-attacks. Artificial intelligence(AI) is now programmed in almost all devices, with functions like advancing camera quality, face recognition and virtual assistants.

Companies are interested in buying insurance to mitigate the risk of adoption and deployment of new AI applications with no history of use. "When it comes to the commercial use of AI, businesses can't rely on government regulation to protect them against potential losses in the event it fails to live up to its promise," stated Saar Yoskovitch, CEO and cofounder of Augury, in a recent account in Open Access Government. As deployed AI systems mature, they will increasingly make high risk decisions. "But AI models are often brittle, do not deal well with edge cases and may have been trained on a dataset with inherent biases," stated Yoskovitch. This is especially prevalent with AI systems that use human behavior as an input, such as auto insurance applications that capture an individual customer's driving behavior.

By Unique Kumar Mental Health: There is a sincere need to improve mental health, As per the reports available, it is estimated by the WHO World health organization, the burden of mental health to the tune of 2443 disability-adjusted life years (DALYs) per 100, 000 population, and the age-adjusted suicide rate is 21.1. Between 2012 & 2030, the health conditions are pegged at 1.03 trillion dollars. Similarly, the mental health survey, 2016 estimated that over 85% of people with common mental disorders such as depression or anxiety disorder and 73.6% of people with mental disorders such as psychosis or bipolar disorder. Interestingly the ideal psychiatrist rate is 1:8000 to 10,000 but actual figures stand at 1:2,00,000 and this is a huge gap. It is not possible to fill the vacancies by employing more and more people & the way to address the problem is only possible by leveraging technology and use Artificial Intelligence, Automation with help of Mobile apps to improve mental health.

Most major companies, including Google, Amazon, Microsoft, Uber, and Tesla, have had their artificial intelligence (AI) and machine learning (ML) systems tricked, evaded, or unintentially misled. Yet despite these high profile failures, most organizations' leaders are largely unaware of their own risk when creating and using AI and ML technologies. This is not entirely the fault of the businesses. An emerging solution is AI/ML-specific insurance. But who will need it and exactly what it will cover are still open questions.

Twenty IT leaders look into their crystal balls to predict the technologies and trends that will drive the sector in 2020. CIO Australia asked Australian technology bosses about their top line predictions for 2020, the technologies that will have the greatest impact next year, and what top trends will impact the IT and business landscape. Here are the predictions from IT leaders across vendor land to CIOs and CTOs across a host of industries. Intelligent systems (machine learning, artificial intelligence and automation) are the top trends in 2020. Intelligent systems will have a significant impact on increasing situational awareness (insights) and using these insights to enhance decision making – to deliver optimal outcomes for customers.

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.

Artificial intelligence is going to have a significant impact on the future of insurance. This revolution is not far off, and the industry is on the verge of a monumental, tech-driven shift. The deep learning technologies needed for this are already here -- think neural networks or machine learning techniques. All of these fall under the umbrella of artificial intelligence, and will help the insurance industry move from a'detect and repair' model to a'predict and prevent' model. It's a similar story with other industries which are considering implementing AI. 'The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs and optimise the customer experience,' writes Ramnath Balasubramanian, partner at McKinsey, Ari Libarikian, senior partner at McKinsey, and Doug McElhaney, associate partner at McKinsey. "Currently, the greatest benefit we see is an enhanced and prioritised customer experience, followed by streamlined back-office processing and improved fraud detection," explains Harald Gölles, CTO at omni:us.

Envelop Risk, a new global specialty cyber insurance analytics and underwriting firm, has been launched in London. The firm also operates offices in Boston, Washington DC, and Bermuda. Envelop Risk provides cyber underwriting to re/insurers, partnering with cyber security firms to deliver custom cyber insurance products for their customers. Using its proprietary artificial intelligence-based simulation model the company claims to provide "more-accurate policy and portfolio pricing with increased access to capital markets, while reducing overall risk exposure". Envelop applies an augmented intelligence approach, combining quantitative methods with human expertise in underwriting and cyber security.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.