While ethical artificial intelligence (AI) and analytics adoption can add about US$2 trillion (S$2.7 trillion) of value each year to the global banking and insurance industry, a Temasek survey showed that only 13 per cent of the sector uses AI solutions across the bulk of its processes. Financial firms in select markets including Singapore were polled in the survey. About 31 per cent of the companies are still only dipping their toes in AI-driven solutions, while more than half of the respondents fell somewhere in the middle - using AI in some areas, but not harnessing its full potential. The findings mean that, while almost all financial services companies use AI in their processes in some way, they differ in the extent of AI deployment, with an overwhelming 93 per cent of the companies demanding that AI solutions should be trustworthy, said Temasek. The study was conducted by Temasek in March this year, on an anonymous basis, with 39 decision-makers from banks and insurance companies in the United States, Europe, Singapore and Hong Kong.
"We're excited about it," Peter Colis, Ethos CEO and co-founder, said of the SoftBank investment. "It's more capital to fuel our mission of protecting families." Ethos plans to use the funds to build out its engineering and products team, as well as for research and development. Employees currently number about 200 people and are expected to jump to 350 to 400 by the end of the year, he said. SoftBank's investment is coming from its $30 billion Vision Fund 2. The pool typically focuses on companies that use artificial intelligence like Carro, the Singapore online car marketplace; DiDi, the Uber of China; and eToro, the Israeli online stock brokerage.
When we think of air pollution, we often think of Delhi, perhaps Beijing, or even Shanghai. Hence, the World Health Organisation (WHO) reports that 9 out of 10 people around the world breathe polluted air. As humans, we contribute the most to air pollution by using energy to drive our vehicles, power our houses, run our data centers, and to travel. So much so that everything we use today was made at a factory that has contributed to air pollution. Today, technology has become an enabler to help address air pollution.
Hong Kong-based regional InsurTech venture – YAS Digital Limited (YDL), unveiled today YAS, a microinsurance marketplace which utilizes innovative technologies such as 5G, AI, blockchain, data analytics, and open API, to reshape the insurance industry while creating an ecosystem and business model for B2B and B2C customers. YDL has mentioned that it is the first InsurTech venture in Hong Kong to employ YAS, which functions like an app store and empowers insurers with open APIs ready to plug and play. YAS utilizes a customer-centric open marketplace that offers a diverse and affordable range of products tailored to the customer's needs. Andy Ann and William Lee, Cofounder of YDL said, "YAS is a perfect blend of technological innovation, digital capabilities, and customer-centric experience; it fosters to build a community with utility, loyalty, and experience to protect people through lifestyle. What's more, it also forms a powerful transformative insurtech business model and ecosystem, leading the evolution of the global insurance market, and reshaping the insurance industry for the next generation.
Against a backdrop of startling international developments, such as Brexit and the Hong Kong protests, Japan's financial sector is uniquely positioned to step out of the shadows of its competitors in Singapore and Hong Kong. This is the assessment of The Organization of Global Financial City Tokyo -- also known as FinCity.Tokyo -- which, on March 19, held its FinCity Global Forum at the Grand Hyatt Tokyo in Roppongi to explore the opportunities and challenges that await Japan in its pursuit to become a top global financial hub. Established in April 2019, FinCity.Tokyo is an organization that promotes Tokyo as a global financial hub and supports foreign financial services firms set up in Tokyo. In addition to the keynote and other speeches, the forum consisted of a series of panel discussions that invited industry veterans to discuss a wide array of topics, ranging from regional revitalization and socially oriented asset management to competition and collaboration among international financial cities. The first panel, centered on the theme of "Advancement of the Asset Management Industry and Global Financial City Initiative," invited panelists Yasumasa Tahara, director of the strategy development division at the Financial Services Agency; Kazuhide Toda, managing executive officer and chief investment officer at Nippon Life Insurance Company; and Oki Matsumoto, chairman and CEO at Monex Group Inc., to share their thoughts on how the industry can improve its asset management environment.
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
Artificial intelligence has evolved from an esoteric research topic--with its origins six decades ago in corporate and academic computer science labs--into a collection of powerful technologies with mainstream business promise and applicability. Deloitte's global AI study finds that, in organizations adopting AI, more than eight in 10 leaders see AI as "very" or "critically" important to their business success in the next two years.1 AI adoption and spending are surging globally. According to one report, 37 percent of organizations have now deployed AI--a 270 percent increase from four years ago.2 Analysts project global spending on AI to top US$35 billion in 2019 and more than double to US$79.2 billion by 2022.3 What is driving this tremendous upswing? Many foresee AI helping to spur enormous productivity gains over the next decade, making it essential to the competitiveness of national economies.4
It is fascinating to see how fast financial services leverage the initial personalised scores built by big tech giants in China to onboard their new customers and enhance their more traditional risk models. Some insurers in China are also using technology to offer services outside the boundaries of traditional insurance through digital ecosystem partnerships. This process has enabled them to leverage new customer interactions and gain deeper risk insights to further personalise their product and services proposition to improve the insurance customer journey. Often times the personalisation of products also requires dynamic risk management features where a strong reinsurance partner is proven to be valuable. Building stronger partnerships with digital partners, accessing more customer data and digital interaction while leveraging developments on artificial intelligence and cognitive systems will enable insurers to activate dynamic risk management.
--Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculate the contributions of multiple parties in FML, in the context of both horizontal FML and vertical FML. For Horizontal FML we use deletion method to calculate the grouped instance influence. For V ertical FML we use Shapley V alues to calculate the grouped feature importance. Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning. I NTRODUCTION Federated Learning or Federated Machine Learning (FML)  is introduced to solve privacy issues in machine learning using data from multiple parties.
Hong Kong Exchanges and Clearing Limited (HKEX) is today (Friday) pleased to announce that it has entered into a memorandum of understanding (MOU) with Ping An Insurance (Group) Company of China Limited (Ping An) to explore possible areas of cooperation and collaboration in Fintech and data analytics to enhance the region's financial market ecosystem. HKEX Chief Executive, Charles Li, joined Ping An Chairman, Ma Mingzhe, in Shenzhen to mark the signing of the MOU. In attendance were other senior executives from HKEX and Ping An. HKEX and Ping An will work together to identify areas of collaboration, including Fintech solutions across different asset classes, as well as the application of data and Artificial Intelligence technology to support the mutual connectivity of the Mainland Chinese, Hong Kong and international markets. Ping An, which is a leading financial services provider in insurance, banking, and securities in China, has significant technology capabilities and is a provider of Fintech solutions to support other financial institutions on their digital transformation.