Danske Bank

About: Danske Bank is a based out in . It is known for research contribution in the topics: Volatility (finance) & Volatility smile. The organization has 69 authors who have published 145 publications receiving 3733 citations. The organization is also known as: Danske Bank A/S.

What happened to remote usability testing?: an empirical study of three methods

Abstract: The idea of conducting usability tests remotely emerged ten years ago. Since then, it has been studied empirically, and some software organizations employ remote methods. Yet there are still few comparisons involving more than one remote method. This paper presents results from a systematic empirical comparison of three methods for remote usability testing and a conventional laboratory-based think-aloud method. The three remote methods are a remote synchronous condition, where testing is conducted in real time but the test monitor is separated spatially from the test subjects, and two remote asynchronous conditions, where the test monitor and the test subjects are separated both spatially and temporally. The results show that the remote synchronous method is virtually equivalent to the conventional method. Thereby, it has the potential to conveniently involve broader user groups in usability testing and support new development approaches. The asynchronous methods are considerably more time-consuming for the test subjects and identify fewer usability problems, yet they may still be worthwhile.

Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain

Abstract: Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an owning entity’s real-world identity is hidden behind a pseudony...

Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning

Abstract: Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity’s real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with ≈ 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and F1-score of ≈ 0.75. We discuss our novel approach of Supervised Machine Learning for uncovering Bitcoin Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.

Pitfalls in Estimating Jump-Diffusion Models

Abstract: In this paper we show that it is invalid to use standard maximum likelihood procedures in estimating jump-diffusion models. The reason is that in jump-diffusion models the log-return is equivalent to a discrete mixture of N normally distributed variables, where N goes to infinity. Thus, from the mixture-of-distributions literature we know that the likelihood function can be unbounded which causes inconsistency. In the paper we derive a method which provides consistent and asymptotically normally distributed estimator. The method is applied to some of the most actively traded New York Stock Exchange (NYSE) stocks and several stock indices. The implication of the estimated jump-diffusion models for option prices is examined.

From Monolithic to Microservices: An Experience Report from the Banking Domain

Abstract: Microservices have seen their popularity blossoming with an explosion of concrete applications in real-life software. Several companies are currently involved in a major refactoring of their back-end systems in order to improve scalability. This article presents an experience report of a real-world case study, from the banking domain, in order to demonstrate how scalability is positively affected by reimplementing a monolithic architecture into microservices. The case study is based on the FX Core system for converting from one currency to another. FX Core is a mission-critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe.