Private-sector insurer ICICI Prudential Life has extended the availability of its conversational artificial intelligence (AI) chatbot'Ligo' on Google Assistant to address customer queries. Its customers will need to activate Google Assistant on their Android smartphones and speak out their policy number or registered phone number to get various details about their policy. "It is as simple as asking Google for directions or traffic. In this ever-evolving digital world where speed, efficiency, and convenience are continually being enhanced, there has been rapid adoption of AI-powered voice assistants by individuals owing to personalized and immediate experiences being provided," said ICICI in a statement. ICICI Prudential introduced conversational AI-based Ligo chatbot on its website and mobile app in 2018.

At a time when consumers are hesitant to go out into the world and book appointments in doctors offices and insurance offices, they also want to ensure that their families are taken care of in the event of death. These circumstances have life insurance companies quickly transforming their services and offering digital options in the post-COVID era. "The industry overall has fully embraced digital transformation and we are looking to undertake such an increased focus on analytics, automated underwriting, artificial intelligence, machine learning and to be capitalizing on the rich cache of data that most organizations have," said Kartik Sakthivel, chief information officer at LIMRA. "The general consensus is that covid did not necessarily change projections for the transformation, it has just accelerated a bunch of programs that were already under way." Even before the pandemic, 75% of North American companies had invested in digital, according to a report from LIMRA.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.

Prudential Corporation Asia and UK-based Babylon Health (Babylon) have partnered for AI-backed digital health services in Asia. The partnership will see Babylon's AI expertise made available to existing and new customers of Prudential across Asia. Prudential has more than five million medical insurance customers in Asia and premium income exceeded £800 million in 2017. Based in the UK, Babylon offers AI-powered health services, including personal health assessment and treatment information. The Prudential-Babylon proposition will offer customers in up to 12 markets in Asia 24/7 access to a comprehensive set of digital health tools, complementing Prudential's existing suite of insurance products.

The global insurance industry will grow more strongly than the global economy in 2018 and 2019, Munich Re predicts in its latest outlook. "This year and next, we expect global premium to grow by more than €460 billion in all. This is equivalent to average annual premium growth of 5.3% (in real terms, i.e., adjusted for inflation: 3.7%), whereas global GDP is expected to grow by only 4.9% (3.3% in real terms). Life insurance, in particular, looks set to return to strong annual premium growth of 5.6% (3.9% in real terms) after a weak 2017. Property-casualty insurance is benefiting from the currently favorable economic environment. In this segment, we are expecting annual growth rates of close to 5% (3.3% in real terms). Emerging countries are the primary growth drivers, but somewhat stronger growth rates in high-volume industrialized countries are also contributing to this positive development."

Say you have a complicated task at hand, like reading the next paragraph of this article in a language you don't know. That's certainly daunting, but you can solve it easily with the help of an expert who does know the language, thanks to clever technology that exists today. Welcome to the world of artificial intelligence (AI), machine learning and cloud computing. If you think about it, these three elements exist all around us all the time. This is, after all, a website, and those are all on the Web, which is facilitated by the Internet. Today, cloud computing powers the Internet, while also needing the Internet to function.

Deep learning has the potential to revolutionise the insurance sector – but the challenge is how to make the artificial intelligence (AI) models auditable. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Prudential's global head of AI, Michael Natusch, took to the stage at RE•WORK's Deep Learning in Finance event in London this week to explain the global financial services firm's approach to artificial intelligence (AI) – explaining which algorithm to use and when. Natusch gave a top level view of Prudential's thinking around what AI user interfaces to use in what situations and which algorithms should be applied, depending on the requirements for causation or correlation. He began by explaining the fundamentals of AI (see below image), stating that organisations need three fundamentals: data, intelligent agents (data scientists/algorithms) and user interfaces. Very clearly we need data. On top of data you need some sort of model that sits on top that makes sense of what's in that data.

Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a'Silicon Roundabout' based startup and prior to that was a partner at a major consulting firm. Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.