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Traveling with bitcoins

In the past 20 years, all industries have felt the impact of technological innovation. In some cases, this impact has been so great that disruption has occurred. A clear and often used example is the travel industry, where companies that did not exist 20 years ago, like AirBnB and Booking.com, are now major players.

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News Sentiment Analysis and Credit Risk

Do we still need humans to monitor and interpret qualitative data for our risk models?

Recent technological advances increase the possibility of using qualitative data in risk models to ensure a timelier recognition of threats. News articles, which can be seen as a type of unstructured data, are flooding the world every day. However, one can imagine the time it would take to manually process all this information. Recent developments in natural language processing (NLP) show some very promising results in automating that task by a computer. We assess the possibilities of these recent advances within credit risk management.

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SARON hedging strategies

Hedging of SARON exposure, Part II

On 5 March 2021, the Financial Conduct Authority (FCA) announced the official dates of the cessation and loss of representativeness of the LIBOR rates. As a result, 31 December 2021 will be the last day on which the CHF LIBOR will be published. During the last months, a multitude of informative documents were provided by the Alternative Reference Rates Committee (ARRC) and the International Swaps and Derivatives Association (ISDA). As a follow-up from the previous article Hedging of SARON exposure, Part I: Explanatory power of SARON term and actual rates, this article assesses the different compounding methodologies with respect to hedging strategy.

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Can machine learning predict the probabilities of default?

According to Moore’s law, computing power doubles up each two years. This performance increase in computing power makes machine learning increasingly efficient each year, and widely applicable. But does this also apply to credit risk issues?

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Sourcing Market Data

The provision of market data to support not only an organization’s treasury function but the wider business functions can become a time-consuming and potentially complex exercise. It is no longer just about the source of market data, questions such as integration, validation, storage, consistency and distribution within an organization need to be considered. In this article we will look at some of the considerations when deciding on how to source market data and how in-built applications can reduce risk and cost while improving automation.

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