One of the two task forces announced to be formed by the US House Committee on financial services will be investigating the use of artificial intelligence technologies (AI) for FinTech. The focus of the task force will be to examine digital identification technologies using AI to reduce fraud. It will also look into issues such as regulating ML in the financial services industry, risks associated with algorithms & big data, and the impact of automation on jobs and the economy in the US. AI has been one of the hottest technologies used by emerging FinTech players. It is used in automation, social media analytics & intelligence tools, cybersecurity, fraud prevention, and other areas.
How they describe themselves: Actionable analytics is the backbone of NYSE-listed Enova International, a global online lending company. In the past 14 years, the analytics team has applied predictive and prescriptive analytics to fraud detection, credit risk management, and customer retention and built the Colossus Digital Decisioning Platform to automate and optimize many of Enova's operational decisions. As a result, Enova has extended over $20 billion in credit to over 5 million customers worldwide. Enova Decisions was launched in 2016 to help businesses in financial services, insurance, healthcare, telecommunications, and higher education achieve similar outcomes by leveraging the same analytics expertise and decisioning technology. How they describe their product/innovation: Enova Decisions Cloud is a complete decision management suite where clients can integrate 1st and 3rd party data, deploy machine learning models, manage business rules, monitor performance, and continuously optimize performance.
Within the next decade, healthcare will see emerging technologies including artificial intelligence, cloud computing, predictive analytics and blockchain spurring billions of dollars in value increases, according to a new McKinsey & Company report on this tech-driven "era of exponential growth." For these innovations to impact areas like clinical productivity, care delivery and waste reduction, though, certain value pools will need to be disrupted across the entire industry. Here are four possible disruptive changes that could transform healthcare in the coming years, according to McKinsey. More articles about AI: How AI can enhance clinical productivity IBM Research using self-driving car tech to promote seniors' wellbeing Bill calls for $2.2B in federal AI funding
Being a board member is a hard job -- ask anyone who has ever been one. Company directors have to understand the nature of the business, review documents, engage in meaningful conversation with CEOs, and give feedback while still maintaining positive relationships with management. These are all hard things to balance. But, normally, boards don't have to get involved with individual operational projects, especially technical ones. In fact, a majority of boards have very few members who are comfortable with advanced technology, and this generally has little impact on the company.
The global analysis of Artificial Intelligence In Finance Market and its upcoming prospects have recently added by Research N Reports to its extensive repository. It has been employed through the primary and secondary research methodologies. This market is expected to become competitive in the upcoming years due to the new entry of a number of startups in the market. Additionally, it offers effective approaches for building business plans strategically which helps to promote control over the businesses. "Artificial Intelligence is the intelligence which is shown by machines. Cognitive computing, Chatbots, Personal Assistant, Machine Learning are all peripherals of AI used in the finance industry extensively nowadays."
Megvii Technology, a Chinese company, founded in 2011 and widely known for its Face system, is one of the world leaders in facial recognition and artificial intelligence technology. While they might be best known for Face, Megvii uses artificial intelligence and machine vision in a variety of amazing ways. Megvii was the concept conceived by friends and Tsinghua University graduates Yin Qui, Yang Mu, and Tang Wenbin. After tremendous success in China (especially since they were able to train algorithms from China's vast pool of data) with clients such as Ant Financial, Vivo (smartphones), Didi Chuxing (ride-sharing) and investments from Bank of China, the State-Owned Venture Capital Fund, China-Russian Investment Fund and other private investors including Ant Financial (Alibaba's payment affiliate), Megvii is ready to go global. They have projects slated in the coming year for Japan, Europe, the Middle East, Southeast Asia, and the United States and have secured a distributor in Thailand.
Like so many industries it came for over the past few years, the fourth industrial revolution is now heading straight for real estate, making huge waves that are only going to get bigger. Some of these advancements, particularly in artificial intelligence (AI), are going to have significant impact on the management of commercial real estate properties. One of the biggest benefits to advancements in AI and other technologies is having the ability to streamline so many of the property management processes, processes that are all critical to the management of a commercial building, especially ones that house a significant number of tenants. Managing these types of properties has never been an easy feat, but technology is reshaping what property management looks like, ultimately giving time back to property management companies to focus on revenue, continue to grow their business and provide more positive tenant experiences. AI in Commercial Property Management When it comes to commercial real estate, just like residential, there is constant change.
The IIF surveyed 59 institutions (54 banks and 5 insurers) on their exploration and adoption of Machine Learning techniques in Anti-Money Laundering. While the detailed version of our resultant report is limited in its distribution to the regulatory community and those 59 firms, a short-form summary report has also been prepared for public distribution. This study covers the particular purposes of application in the AML space, as well as which types of specific techniques are in scope, firms' maturity in adopting, benefits, challenges and model governance. Our findings indicated that the application of machine learning techniques in AML is spreading quickly across the industry, driven by a dedication to build a stronger and more effective defense system against illicit activity. Significantly, none of the 59 surveyed firms were pursuing machine learning as a means to reduce staff, but rather to gain greater and faster insights that can be made available for their trained AML analysts.
Sarah Runge, Global Head of Financial Crime Compliance Regulatory Strategy for Credit Suisse, joins us on this week's episode of FRT to discuss the benefits and challenges of applying Machine Learning in Anti-Money Laundering and Countering Terrorism Financing (AML/CTF). Sarah highlights the potential that enhanced analytics hold to strengthen the defense mechanisms against financial crime. Today's framework and practices lead to inefficiencies that can make one lose sight of the bigger picture. However, we also explore how technology cannot (and should not) replace the human element and vigilance in a financial institution's safeguarding measures. It should be seen as a way to empower analysts and focus their resources on the cases that need their attention the most.
Corporate payments technology firm Bill.com is rolling out a new platform designed to deploy artificial intelligence (AI) for automated workflows. In an announcement on Wednesday (May 22), Bill.com said it has launched the Intelligent Business Payments Platform for small and medium-sized businesses that need a more efficient business payments solution. Bill.com pointed to its own research, which found the payments platform saves SMB users an average of 5.5 hours every week, or 35 business days a year, by automating processes that professionals had previously completed manually. The platform includes an Intelligent Virtual Assistant, which automates invoice processing and approval, and uses machine learning to automatically capture data from those invoices and identify potential errors. It can also recognize bill approval routing and payment thresholds.