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Covina CA, Jan. 25, 2021 (GLOBE NEWSWIRE) -- Trade finance is the financing of international trade rows, acting as an intermediary between importers and exporters to mitigate the risks involved in transactions and enhance working capital efficiency in businesses. It deals with activities related to financing of domestic and international trade. The trade finance includes issuing letters of credit (LCs), receivables and invoice finance, credit agency, export finance, bank guarantees, insurance, and others.

The global trade finance market accounted for US$ 41,075.4 million in 2019 and is estimated to be US$ 53,015.6 million by 2025 and is anticipated to register a CAGR of 4.2%.

The report "Global Trade Finance Market, By Product Type (Guarantees, Letter of Credit, Documentary Collection, Supply Chain Finance, and Others), By Services Providers (Banks, and Trade Finance Houses), By Application (Energy, Finance, Transport, Power Generation, Healthcare, Metals and Non Metallic Minerals, Renewables, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2025.

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Key Highlights:

In July 2018, IBM has launched in collaboration with CLS a blockchain platform, LedgerConnect aimed at the financial services industry and till now, nine budgetary administrations organizations, including banks Barclays and Citi, have associated them with this platform.

In May 2019, Deloitte Tests Data Management on Ethereum Blockchain with Three Irish Banks, named Institute of Banking (IoB), Bank of Ireland, AIB and Ulster Bank, for verification of staff credential.

Analyst View:

Increasing investment in trade finance

The development of technologies such as optical character recognition (OCR) to read container numbers, radio frequency identification (RFID) and quick response (QR) codes to identify and trace shipments, blockchain, and enhancing digitization of trade documents drive the trade finance market growth. Advancements in technology, switching from traditional banking methods for documentation to ease the paperwork, and efficient enhancement in trade finance industry provide opportunities for the market. In addition, strategic formulation along with adoption of structuring and pricing tools offer some other growth opportunities to the market. Support from banks to firms ability to mitigate payment risk by purchasing trade credit insurance boosts the market growth.

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Browse 60 market data tables* and 35 figures* through 140 slides and in-depth TOC on Global Trade Finance Market, By Product Type (Guarantees, Letter of Credit, Documentary Collection, Supply Chain Finance, and Others), By Services Providers (Banks, and Trade Finance Houses), By Application (Energy, Finance, Transport, Power Generation, Healthcare, Metals and Non Metallic Minerals, Renewables, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2025

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Key Market Insights from the report:

The global trade finance market accounted for US$ 41,075.4 million in 2019 and is estimated to be US$ 53,015.6 million by 2025 and is anticipated to register a CAGR of 4.2%. The market report has been segmented on the basis of product type, services providers, application, and region.

Depending upon product type, the guarantees segment is projected to grow at highest CAGR over the forecast period. Owing to the fact that agency financing acts as a mediator between buyers and sellers who are involved in international trading. It provides highly structured financing solutions, which, in turn, enhances exporters risk capacity, allowing domestic companies to export goods and services to buyers around the world with a certain level of security. Moreover, international agency financing offer guarantees to cover commercial bank credits, direct funding, and mitigates political risks.

In terms of services providers, the banks segment generated the highest revenue in 2018 and is anticipated to continue the same during the forecast period, owing to the fact that banks act as intermediaries in trade finance ecosystem to provide inter-firm trade credits to buyers, sellers, and other parties involved in the trade. Furthermore, banks are accelerating trade finance processes by transforming their paper-based methods to more efficient and transparent digitized models, thus becoming the highest service providers in the trade finance market.

Depending upon application, the target market is segmented into energy, finance, transport, power generation, healthcare, metals and non-metallic minerals, renewables, and others. Energy segment show the highest growth in the region due to low interest rates and fees provided by international banks.

By region, Asia Pacific dominated the trade finance software market in 2019, followed by Europe and North America. This is primarily due to the increasing adoption of trade finance software to manage and automate the trade finance process. Europe is expected to grow at the fastest growth rate during the forecast period owing to the involvement of export credit agencies (ECA) conducting international trade, enhancing public policy from government agencies, and promoting trade across the globe.

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Competitive Landscape:

The prominent player operating in the global trade finance market includes Banco Santander SA, Bank of America Corp., BNP Paribas SA, Citigroup Inc., Agricole Group, Goldman Sachs Group Inc., HSBC Holdings Plc, JPMorgan Chase & Co., Morgan Stanley and Wells Fargo & Co.

The market provides detailed information regarding the industrial base, productivity, strengths, manufacturers, and recent trends which will help companies enlarge the businesses and promote financial growth. Furthermore, the report exhibits dynamic factors including segments, sub-segments, regional marketplaces, competition, dominant key players, and market forecasts. In addition, the market includes recent collaborations, mergers, acquisitions, and partnerships along with regulatory frameworks across different regions impacting the market trajectory. Recent technological advances and innovations influencing the global market are included in the report.

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Some Important Points Answered in this Market Report Are Given Below:

Explains an overview of the product portfolio, including product development, planning, and positioning

Explains details about key operational strategies with a focus on R&D strategies, corporate structure, localization strategies, production capabilities, and financial performance of various companies.

Detailed analysis of the market revenue over the forecasted period.

Examining various outlooks of the market with the help of Porters five forces analysis, PEST & SWOT Analysis.

Study on the segments that are anticipated to dominate the market.

Study on the regional analysis that is expected to register the highest growth over the forecast period

Key Topics Covered

Introduction

Study Deliverables

Study Assumptions

Scope of the Study

Research Methodology

Executive Summary

Opportunity Map Analysis

Market at Glance

Market Share (%) and BPS Analysis, by Region

Competitive Landscape

Heat Map Analysis

Market Presence and Specificity Analysis

Investment Analysis

Competitive Analysis

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Global Trade Finance Market Technologies such as blockchain, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT)...

Enterprise risk and compliance solutions provider Behavox experienced explosive demand for its product in 2020, as the worlds sudden pivot to remote working tested business continuity protocols and created new opportunities for employee misconduct.

This is why the companywhich offers a machine learning-powered platform that helps firms aggregate and analyze enterprise communications data, including email, messaging and voice, for risk assessment, regulatory compliance and fraud monitoringwins this AFTA for the second year in a row. The coronavirus accelerated the understanding that the workplace is no longer a place, says Erkin Adylov, Behavox founder and CEO. It has become a digital realm, and the laws of people dont apply in that realmit is a complete Wild West. And work is not going to become less digitalfirms are thinking that we need to bring the same laws that govern our day-to-day lives to that digital realm, but they need someone to organize all the data they generate.

Early in 2020, Behavox received a $100 million investment from SoftBank, itself a client. The company then signed up a number of the worlds largest banks and asset managers, and doubled its headcount, as it moved into new territories (Japan and the Nordics) and expanded its existing office in Montreal to accommodate additional data scientists and engineers.

The company also managed to complete implementations in months that normally would have taken far more time, with many customers taking advantage of the cloud-based version of the platform. One implementation, at Danske Bank, took just five months.

This year, as the company grows, it is planning to enhance its platform with Behavox Boost, a tool for modeling employee performance, and Motivate, which analyzes soft concepts like team morale and the quality of team collaboration.

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AFTAs 2020: Most Innovative Third-Party Technology Vendor (AI, Machine Learning and Analytics)Behavox - http://www.waterstechnology.com

Amit Chaudhary, a machine learning (ML) researcher from Nepal, has recently introduced a browser extension that allows users to directly access videos related to research papers published on the platform arXiv.

ArXiv has become an essential resource for new machine learning (ML) papers. Initially, in 1991, it was launched as a storage site for physics preprints. In 2001 it was named ArXiv and had since been hosted by Cornell University. ArXiv has received close to 2 million submissions across various scientific research fields.

Amit obtained publicly released videos from 2020 ML conferences. He then indexed the videos and reverse-mapped them to the relevant arXiv links through pyarxiv, a dedicated wrapper for the arXiv API. The Google Chrome extension creates a video icon next to the paper title on the arXiv abstract page, enabling users to identify and access available videos related to the paper directly.

Many research teams are creating videos to accompany their papers. These videos can act as a guide by providing demo and other valuable information on the research document. In several situations, the videos are created as an alternative to traditional in-person presentations at AI conferences. This is useful in current circumstances as almost all panels have moved to virtual forms due to the Covid-19 pandemic.

The Papers-With-Video extension enables direct video links for around 3.7k arXiv ML papers. Amit aims to figure out how to pair documents and videos related effectively but has different titles, and with this, he hopes to expand coverage to 8k videos. He has proposed community feedback and has now tweaked the extensions functionality based on user remarks and suggestions.

The browser extension is not available on the Google Chrome Web Store yet. However, one can find the extension, installation guide, and further information on GitHub.

GitHub: https://github.com/amitness/papers-with-video

Paper List: https://gist.github.com/amitness/9e5ad24ab963785daca41e2c4cfa9a82

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A Nepalese Machine Learning (ML) Researcher Introduces Papers-With-Video Browser Extension Which Allows Users To Access Videos Related To Research...

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.

Reference

Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006. https://acrabstracts.org/abstract/identification-of-a-rule-to-predict-response-to-sarilumab-in-patients-with-rheumatoid-arthritis-using-machine-learning-and-clinical-trial-data/

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Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News