regulator


The New-Paradigm: Key Trends in AI-Driven Fintech

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Technology is reshaping the operating-model of financial institutions fundamentally, and the attributes necessary to build a successful business. AI is weakening various components of incumbent financial institutions, thereby creating an opportunity for an entirely new operating-models and category-dynamics focused on the scale and sophistication of product, tech & data much more than the scale or complexity of capital. Unlike past'AI Springs', the science and practice of AI is poised to continue an unprecedented multi-decade run of progress. A clear vision of the future financial landscape is critical for good governance and strategic decisions. AI systems will eventually underwrite credit and insurance across the world.


AI in Banking Part 5: ML and DL -- it's time

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Before I delve into Machine Learning (ML) and Deep Learning (DL), I must admit that I'm far from the best person in @SAS to blog on the inners of these techniques although, I have no qualms about borrowing from my colleagues to provide a quick and dirty understanding of ML and DL. The sketch below should also help. Where I can add value is as a longtime voyeur of the banking industry -- watching how banks use new technologies and techniques to resolve business issues. ML trains the computer to learn on its own. Algorithms look for patterns in data that explains the data and then makes a prediction.


Growth in risk-based approaches the 'main challenge' to address in 2020

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As we come towards the end of the year, industry experts discuss how the clinical research market has evolved and how they are looking to prepare for the challenges to overcome in 2020. The past year saw CluePoints, a software developer providing clinical trial monitoring services, build on its agreement with the US Food and Drug Administration (FDA) to provide its services supporting the regulator's oversight of the clinical trial market. Asked about how market demands have shifted since 2018, the company's CCO, Patrick Hughes pointed to the ICH E6 (R2) good clinical practice (GCP) guidance, which'became a reality' for sponsors and clinical research organizations (CROs). As a result, this made 2019, "the year in which we have seen the biggest momentum shift across the industry in the adoption of a risk-based approach to trial management," Hughes said. Risk-based quality management in clinical trials focuses on identifying the most important compliance risks in a study and setting them as a priority in order to prevent and avoid potential disruptions.


Industrial revolution race: who will be the global winner of 4IR?

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Today's largest manufacturing country headed the output league table nearly two centuries ago before being ousted by Britain in the first industrial revolution. China accounts for 20 per cent of global output, followed by the United States with 18 per cent, Japan 10 per cent, Germany 7 per cent and South Korea with 4 per cent, according to the most recent (2015) data from the United Nations Conference on Trade and Development. The UK is ninth with 2 per cent. In the intervening centuries there have been sizeable shifts. China reclaimed its crown after 150 years by overtaking America during the past decade.


Major U.S. bank, a pioneer in the use of machine learning models, teams with Protiviti to improve its model validation framework

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Following the financial crisis of 2007-2008, regulators issued specific guidance to help banks reduce the risk of financial losses or other adverse consequences stemming from decisions based on incorrect or misused financial models. Since then, the guidance has become the model risk management bible for financial institutions. It is used to ensure that model validation, typically performed annually, can identify vulnerabilities in the models and manage them effectively. Recently, the rapid advance and broader adoption of machine learning (ML) models have added more complexity and time to the model validation process. Specifically, ML models have highlighted expertise gaps in in-house model validation teams trained in traditional modeling techniques.


AI Collaboration Forum

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Is my organisation a member? The Whitehall & Industry Group's AI Collaboration Forum will bring together a wide audience from our 230 members, spanning the private, public and not-for-profit sectors, as well as academic institutions. Supported by the Office for Artificial Intelligence and kindly hosted by EY. The agenda will explore the vital role of cross-sector collaboration to ensure the endless possibilities of AI are harnessed and regulated effectively, generating maximum positive economic and societal impacts for the UK. Holding a BSc in Computer Science and an MBA from the Massachusetts Institute of Technology, Sana Khareghani has over 20 years' experience in technology and business across the private and public sectors.


Reducing Risk in AI and Machine Learning-Based Medical Technology Artificial Intelligence Research

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Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. For more information see the IDTechEx report on Digital Health 2019: Trends, Opportunities and Outlook. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML.


Reducing risk in AI and machine learning-based medical technology

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Along with the benefits come new risks and regulatory challenges meaning doctors now have to consider how best to reduce risk when it comes to medical technology. From spotting malignant tumours to reading CT scans and mammograms, Artificial Intelligence and Machine Learning-based technology is faster and more accurate than traditional devices – or even the best doctors. In a new article, Algorithms on regulatory lockdown in medicine, recently published in Science, researchers look at the new challenges facing regulators as they navigate the unfamiliar pathways of Artificial Intelligence and Machine Learning. In the paper, the researchers consider the questions: what new risks do we face as Artificial Intelligence and Machine Learning (AI/ML) devices are developed and implemented? How should they be managed?


Reducing Risk In AI And Machine Learning-Based Medical Technology

#artificialintelligence

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices – or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?


Reducing risk in AI and machine learning-based medical technology

#artificialintelligence

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?