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Curve gets services back online over weekend




Curve, the UK fintech which consolidates multiple cards into one, has recovered its services following the suspension of Wirecard’s UK subsidiary by the Financial Conduct Authority (FCA).

Temporarily, users could not use their Curve card to make payments directly, or through a digital wallet

The suspension occurred last week due to parent company Wirecard filing for insolvency. The German firm fell into trouble due to an accounting scandal which revealed a $2 billion hole on its balance sheet.

Curve is the first of a handful of fintechs to get services back online. The firm claims more than 1.3 million customers.

Meanwhile, ANNA Money and Pockit customers are still among those waiting to get access to their money and accounts. These fintechs have around 20,000 and 500,000 customers respectively.

What was down and why?

At around 15:00 on Friday, Curve announced its services were “temporarily suspended”.

The fintech used Wirecard to issue its Curve cards and process transactions. This meant Curve’s operation depended on Wirecard’s operational support, which the UK’s financial regulator had stopped.

As of 15:00, Curve users could not use their Curve card to make payments directly, or through a digital wallet.

The pause on services also saw Curve suspend its cashback reward programme Curve Cash, as well as its ‘Go Back In Time’ feature. This allows users to put past purchases onto different cards.

The fintech said before the weekend that refund times “may be delayed” as it was unable to process them.

Emergency weekend migration from Wirecard

Curve has moved away from Wirecard to as its new acquiring partner

Over the weekend, Curve endeavoured to fast forward a migration from Wirecard which it said had already started.

“We began our transition away from Wirecard a few months back and we are moving furiously down the path to complete this migration,” the fintech said in a blog post update on 27 June.

The fintech brought all card and e-money issuing services in-house from Mastercard. For payments processing, the start-up decided to go with

By 21:28 last night, Curve had managed to get some of its core services back online. This included Curve Fronted, the feature which allows users to make payments with their Curve debit card, using an underlying credit card.

What’s still to do?

The fintech is aiming to have Curve working again via digital wallets Apple Pay, Google Pay & Samsung Pay this week.

It is also still working on reconstructing its ‘Go Back In Time’ feature, and re-setting up Curve Send – the service which allows users to send money peer-to-peer (P2P).

But the big sticking point will be refunds. The fintech said on Saturday that this service is out of its hands and is yet to issue a further update on when it will regain control.

“It is being controlled for Wirecard by the FCA, who should advise us how they intend to manage the process,” the fintech said.

“This means that if you were expecting a refund, or have initiated one, on or before Friday 26 June 2020, we have to ask you to sit tight while we sort this out with them.”

Other fintechs still in the lurch

ANNA’s co-CEOs have suggested they may spot customers the money which is temporally inaccessible themselves

ANNA, a mobile business account provider, told customers to withdraw money “as soon as possible” on Friday.

Many customers were not able to do this in time, hitting businesses particularly hard a day after pay day.

Co-CEOs Boris Dyakonov and Eduard Panteleev ran a Q&A for customers yesterday.

A customer attending the Q&A told FinTech Futures: “They said they have talked to shareholders [and] if [the] FCA don’t have answers today they will use their own money to replace the frozen money.

“That’s how sure they are it [the money] is safe. But this could take 10-14 days.”

Other fintechs still affected by the FCA’s suspension include Pockit, Dozens, Payoneer and Soldo.

The FCA said today that Wirecard’s UK arm has made “good progress” in meeting its conditions.

But the regulator added: “We cannot lift the restrictions without reassuring ourselves that the firm has been able to satisfy all our concerns for example that all clients’ money is safe”

Read next: FCA suspends Wirecard’s UK subsidiary


Big Data

Machine learning and its impact in various facets of healthcare




By Inderpreet Kambo

Data in the healthcare settings is used for generating day-to-day insights as well as continuous improvement of the overall healthcare system. Mandatory practices such as Electronic Health Records (EHR) have already improved the traditional healthcare processes by incorporating big data to perform state-of-the-art data analytics. AI/ML tools are further destined to add value to this realm.

From analyzing radiographs, to identifying tissue abnormalities, to improving the accuracy of stroke prediction based on clinical signs, to benefiting family practitioner or internist at the bedside in supporting their clinical decisions, machine learning is providing the much needed objective opinion to improve efficiency, reliability, and accuracy in the healthcare flow.

Predictive algorithms and machine learning are as good as the training data behind these advanced models. As more data becomes available, there will be better information to build these machine-learning models. And hence we see that much of the initial machine learning successes have come from large organizations with big datasets – Google, Aurora Healthcare, NIH of England to name a few organizations which are able to harness the data.

These firms with massive amount of data facilitate the development of center of excellences (CoEs) that produce unique and powerful machine learning algorithms. Having centralized and synchronous data repositories enable deployment of these algorithms across use cases in healthcare. Below are some of the major use cases, although not comprehensive, that outlines the deployment and use of machine learning in healthcare.

Personalized Medicine

Medicine has come a long way, starting from a generalized broad-spectrum antibiotic treatment approach to disease treatment and prevention approach that takes into account individual variability in genes, environment, and lifestyle for each person.

InsightRX, for example, leverages quantitative pharmacology with machine learning to provide a customized and individualized patient’s response to various treatments. By combining clinical, pharmaceutical, and socioeconomic data with machine learning algorithms, researchers and providers are able to observe patterns in the effectiveness of particular treatments and identify the genetic variations that may be correlated with success or failure.

Healthcare Management

From automating routine front office and reporting to analyzing pharmaceutical marketing research, machine learning is making strides in multiple areas of operations and management. Founded in 2010, LeanTaaSis using machine learning to optimize hospital resources such as waiting period and operating rooms. With Goldman Sachs leading the investment backing, the company has raises over 100 million dollars worth of funding and is one of the pioneers in using machine learning for improvement in healthcare management.

Machine learning guided diagnosis

Data scientists working at Google have developed machine-learning algorithms to detect breast cancer by training the algorithm to differentiate cancer patterns from otherwise healthy surrounding tissue. The machine-learning algorithm entered vast amounts of data into its system and trained to differentiate abnormal tissue pattern from normal surrounding cells.

Studies show that these machine learning, predictive analytics and pattern recognition technology has been adjudged to have over 89 percent accuracy as compared to below 75 percent trained pathologists and medical radiologists’ accuracy.

Research and Development

Pharmaceutical firms and healthcare organizations have been spending billions of dollars in R&D to identify factors affectingpatient’s response and improve healthcare outcomes. However, machine learning has revolutionized research by using these factors inter alia to identify which patients will have better outcomes than others. From enabling early cancer detection to identifying COVID -19 patients who require ventilator support, machine learning is enhancing outcome based research across the various facets of healthcare R&D.


AI and Machine Learning continue to grow in the healthcare industry with the ever-evolving technology advancements. There have been more healthcare focused startups that deploy machine learning than it has ever been. However machine-learning models have not been implemented to the same extent in healthcare as they have been in other verticals. Firstly, machine learning is a recent technology and is far away from the state of perfection.

Whether its FDA, ICMR or EMA approval, it is a long, arduous and expensive process to test, validate and approve the technology in a healthcare setting. Secondly, data privacy and security are one of the biggest barriers of machine learning adoption in healthcare. In healthcare industry, the technologies and systems must be developed so as they comply with the respective data laws and rules of governing organizations.

Despite multitude of challenges in healthcare, a need for a breakthrough in healthcare delivery is much needed. From aging population of various countries, diminishing physician to patient ratio and higher scrutiny on accuracy of diagnosis, the need for new innovative solutions in healthcare is clear and explicit. The best opportunities for AI in healthcare is where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but where physicians retain ultimate responsibility for the patient’s care.


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Big Data

Huobi announces the establishment of Huobi DeFi Labs




Huobi announces the establishment of Huobi DeFi Labs

Huobi DeFi Labs is the platform for DeFi (Decentralized Finance) research, investment, and incubation and eco system building in DeFi space. It aims to build a better financial system in collaborations with the global crypto and DeFi community for the future.

“Huobi as the leading crypto financial services provider in Asia and worldwide, our mission is to provide the best crypto financial products and services to our users regardless it is CeFi or DeFi,” said Leon Li, founder and CEO of Huobi Group. “We are excited to join as a part of the global DeFi ecosystem and will be very honoured to work with the global community to provide the best support possible.”

The DeFi initiatives will be led by Huobi’s Chief Investment Officer Sharlyn Wu, who was a Wall Street veteran, ex UBS and also have been leading blockchain investment at China Merchant Bank International.

“Over the past two years, we have witnessed the birth and exponential growth of DeFi. The width, depth and speed of innovations are unparalleled in human history. It is exciting to see the power of permissionless economy unleashed at global scale. However, there are still many problems to be solved at theoretical and technical level.” said Sharyn Wu, Huobi’s Chief Investment Officer. “There is also a lot of investor education to do in order to bring crypto and DeFi to mainstream users. As DeFi is still in its infancy, it needs collective efforts from the global community to build and grow the space together.”

Huobi Group will allocate tens of millions of dollars to an initial investment fund, which will be managed by Huobi DeFi Labs. The team consists of 4 research and investment professionals initially.

The DeFi Labs will be focused on the following three areas:

– Research of underlying financial theories and technology

– Investment and incubation of DeFi projects

– Work with the best DeFi projects to service the entire ecosystem

DeFi and CeFi to Collectively Change the Landscapes of Traditional Finance

Sharyn Wu explains why Huobi group invests in the DeFi space and Huobi DeFi Labs’ mission:

  • DeFi brings many benefits including transparency and composability, which will improve the efficiency and governance of finance to the next level. More importantly, for the first time ever, it is possible to create a finance system without credit risk and principal agency risk.
  • This system can provide people with trust, safety and certainty, which are not present in our society today. When financial institutions, which are professional at pricing risks come in, they look at the risk parameters, they will tell that this trust-less model deserves better pricing because it removes the risks and uncertainties caused by human behaviors.
  • This will also benefit average users in the ecosystem hugely as every user regardless where they are can tap into the global liquidity pool and all the financial products worldwide through their mobile.
  • Crypto is a perfect system for finance. As blockchain technology optimizes over time, DeFi and CeFi to collective change the landscapes of traditional finance and serve the use cases they are best suited for. Huobi strives to work with the entire crypto and DeFi ecosystem to reshape the global financial systems.
  • Crypto will disrupt finance as the internet has managed to change many other industries. Today is a world that every business operates with their ledger. Society is operating at huge costs for account reconciliations and monopolies ruling out long tail. The power of millions of ledgers merging into one will enable that every user, asset and data is accessible to the entire ecosystem at literally zero costs.


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Hay diversas situaciones de la vida cotidiana en las que es necesario contratar un servicio de traducción y gracias a las nuevas tecnologías los servicios online están en auge, pero es muy importante tener en cuenta algunos aspectos esenciales para elegir un servicio que cumpla con las expectativas:

– Si la traducción es para ser presentada ante las autoridades competentes ya sea por migración, servicios legales o juzgados la agencia debe estár certificada por los organismos oficiales y contar con reconocida trayectoria.

– Cuando la traducción es inherente al campo médico, es de suma importancia que sea una empresa que cuente con un servicio de calidad ya que su trabajo puede afectar la salud de las personas, por lo tanto verificar la repútación de la agencia es indispensable.

-A la hora de solicitar una cotización hay que verificar la tarifa de traducción ya que al haber tanta competencia los precios pueden variar considerablemente.

También se puede presentar que en el plano empresarial debes establecer relaciones comerciales con socios de otras nacionalidades y por lo tanto dependerás de un servicio de traducción constante que cumpla las espectativas ya que los convenios tienen palabras que al ser traducidas de manera erronea pueden cambiar el contexto, de allí que la empresa a elegir debe contar con personal altamente calificado y certificado.

Tanto la rapidez en las entregas como de la fidelidad de las traducciones va a variar de una empresa a otra por lo tanto aconsejamos revisar la reputación y las opiniones que otros usuarios puedan aportar en las redes sociales sobre éstas empresas.

Muchas veces los documentos a ser traducidos son confidenciales o privados y no deben ser expuestos al dominio público por lo que es indispensable solicitar la confidencialidad de los mismos ya sea mediante un documento firmado por las partes en las que se garantice el buen manejo de la información suministrada, los servicios web deben contar con el protocolo https así como la encripación de las comunicaciones a la hora de la recepción de documentos.

Decidirse por un servicio en particular va a depender enormemente de la calidad en los trabajos entregados y de la tarifa de traducción, sin embargo si su requerimiento es frecuente recomendamos una relación a largo plazo con una agencia de reconocida trajectoria que garantice que su documentación está siendo tratada por un equipo de profesionales que se toman su trabajo muy en serio.

Esperamos que estas recomendaciones sean de su agrado y le permitan elegir el servicio adecuado acorde a sus requerimientos.


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