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In pursuit of the 2020 Retail Payments Strategy, the European Commission launched its proposal for a revised payment services regulation package in 2023, together with a proposed regulation on Financial Data Access (FiDA). The FiDA, if adopted, will introduce a new player, the Financial Information Service (FIS), onto the financial services marketplace. Meanwhile, the current Payment Services Directive (PSD2) and the proposed revision already includes an Account Information Service (AIS). The FIS covers a wide range of financial information whereas the AIS only offers payment account information, but an unjustified difference is that the AIS continues to obtain access to data free-of-charge while the FIS must pay for access to data. The proposals therefore create an unlevel playing-field between the services, favouring the AIS. Is this advantage enough to keep the AIS alive after introducing its superior service, the FIS? This article examines this issue, scrutinising the dysfunctionality between the rulesets and their ability to create ‘Open Finance’ as envisaged in the 2020 Retail Payments Strategy. This article concludes that Open Finance would be best achieved by adhering to the Commission’s phrase “same activity, same risk, same rule”, which would require the services to be regulated by the same regulation.

Purpose: The extant literature on populism posits that populists frequently find themselves at odds with the neoliberal economic paradigm, as evidenced by rising debt, a public-investment deficit, and substantial deficit-financed government spending. Populist decision-making is characterised by short-termism and can, therefore, be distinguished from non-populist governance through economic variables. This study introduces an innovative quantitative approach that analyses local-government financial data to classify populist economic behaviour.Design/Methodology/Approach: Adhering to the scholarly consensus that short-termism is a fundamental indication of populism, we operationalise economic populism with five financial variables, scrutinised using cointegration analysis and a probabilistic approach. The selected variables are grounded in the literature on economic populism. The underlying data are publicly available for Czechia—the country examined— and for many other states. We verify the methodology by analysing governing coalitions in major municipalities.Findings: Examination of 6,240 municipalities in post-communist Czechia between 2002 and 2021 reveals indications of economic populism in 6.2 % of cases. Result verification shows that these municipalities are frequently governed by populist parties or varied local initiatives, rather than by established non-populist parties.Academic contribution to the field: This study employs an innovative quantitative assessment of financial indicators, diverging from mainstream populism research, which is usually based on qualitative assessment of sources such as statements, narratives and historical context. Moreover, it emphasises the local level of government in the study of economic populism—a phenomenon typically assessed at the centralgovernment level.Research implications/limitations: The findings suggest that populism may represent a strategic approach for certain local governments, particularly in smaller towns and villages where limited fiscal discipline can impede development. Some identified financial patterns may stem from incompetence or lack of expertise rather than intentional populism. Additionally, many municipalities in the sample are governed by a ‘grey zone’ of local initiatives, complicating result verification. Because the methodology is novel at the local level, there are virtually no comparable studies; consequently, our findings are considered alongside various Central European studies on populism.Originality/Value: This study develops an original quantitative approach applicable at the local-government level to analyse extensive datasets. It enriches discourse on economic populism by examining the phenomenon through the lens of short-termism in financial data. Namen: Literatura o populizmu navaja, da so populisti pogosto v konfliktu z neoliberalno gospodarsko paradigmo, kar se kaže v čedalje večjem dolgu, naložbenem primanjkljaju in znatni javni porabi, ki se financira iz primanjkljaja. Odločitve populistov zaznamuje  kratkoročna usmerjenost. Zato jih lahko od nepopulističnega upravljanja ločimo z gospodarskimispremenljivkami. Pri študiji gre ta inovativen kvantitativni pristop, ki analizira finančne podatke lokalnih oblasti za razvrščanje gospodarskega populističnega vedenja.Zasnova/metodologija/pristop: V skladu z znanstvenim konsenzom, da je kratkoročnost temeljni pokazatelj populizma, operacionaliziramo gospodarski populizem s petimi finančnimi spremenljivkami, ki jih preučujemo s kointegracijsko analizo in verjetnostnim pristopom. Izbrane spremenljivke temeljijo na literaturi o gospodarskem populizmu. Podatki so javno dostopni za Češko republiko, ki je primer proučevane države, terza številne druge države. Metodologijo preverjamo z analizo koalicij, ki vladajo v večjih občinah.Ugotovitve: Analiza 6.240 občin v postkomunistični Češki med letoma 2002 in 2021 razkriva znake gospodarskega populizma v 6,2 odstotka primerov. Preverjanje rezultatov kaže, da te občine pogosto vodijo populistične stranke ali raznolike lokalne iniciative, namesto uveljavljenihnepopulističnih strank.Znanstveni prispevek: Študija uvaja inovativen kvantitativni pristop k ocenjevanju finančnih kazalnikov in se oddaljuje od prevladujočih raziskav populizma, ki večinoma temeljijo na kvalitativnih virih, kot so izjave,narativi in zgodovinski kontekst. Poleg tega v ospredje postavlja lokalno raven upravljanja pri proučevanju gospodarskega populizma. Gre za pojav, ki ga običajno analiziramo na ravni centralne oblasti.Praktične omejitve in implikacije raziskave: Rezultati nakazujejo, da je lahko populizem strateški pristop za nekatere lokalne skupnosti, zlasti v manjših mestih in vaseh, kjer pomanjkanje fiskalne discipline ovira razvoj.Nekateri zaznani finančni vzorci so lahko posledica nesposobnosti ali pomanjkanja strokovnega znanja, ne nujno namernega populizma. Številne občine v vzorcu vodi »siva cona« lokalnih iniciativ, kar otežuje preverjanje rezultatov. Ker je metodologija na lokalni ravni novost, skoraj ni primerljivih študij; zato rezultate primerjamo z različnimi srednjeevropskimi raziskavami o populizmu.Izvirnost/vrednost: Raziskava razvije izviren kvantitativni pristop, ki ga je mogoče uporabiti na ravni lokalne samouprave za analizo obsežnih podatkovnihnizov. Bogati razpravo o gospodarskem populizmu, saj pojav preučuje skozi prizmo kratkoročnosti v finančnih podatkih.

Financial cybersecurity is a complex, systemic risk challenge that includes technological and operational elements. The interconnectedness of financial systems and markets creates dynamic, high-risk environments where organizational security is greatly impacted by the level of security effectiveness of partners, counterparties, and other external organizations. The result is a high-risk environment with a growing need for cooperation between enterprises that are otherwise direct competitors. There is a new normal of continuous attack pressures that produce enterprise threats that must be met with an array of countermeasures. This book explores a range of cybersecurity topics impacting financial enterprises, including the threat and vulnerability landscape confronting the financial sector, risk assessment practices and methodologies, and cybersecurity data analytics. Governance perspectives, including executive and board considerations, are analyzed as are the appropriate control measures and executive risk reporting.-- From publisher's description.

In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KPIs over time. Based on this assumption, behavioral change indicators (BCIs) are introduced to detect and evaluate the changes in traditional KPIs in time series. Three types of BCIs are defined: absolute change indicators (BCI-A), relative change indicators (ratio indicators BCI-RE), and delta change indicators (D-BCI). The technique and advantages of using BCIs to identify unexpected deviations and assess the nature of KPI value changes in time series are discussed and illustrated in case studies. The architecture of the financial data analysis system for financial data anomaly detection is presented. The system prototype uses the Camunda business rules engine to specify KPIs and BCI thresholds. The prototype was successfully put into practice for an analysis of actual financial records (historical data).

\"From the cofounder of the longest-running bitcoin exchange comes a compelling argument for how this digital currency will transform the global economy-and how it can work for you. Bitcoin may be the best investment opportunity of our time, yet most people have yet to understand its promise. In this book, Bobby Lee, one of the earliest, most successful pioneers in the cryptocurrency space, debunks myths and dispels fears that surround bitcoin, arguing that this rational, logical system is superior to traditional monetary systems. Lee cites signs of bitcoin's widening acceptance: a growing community of users worldwide and multiple initiatives for investing in and holding bitcoin among major financial services organizations and institutional investors who control trillions in assets. Lee offers a primer on the best strategies for purchasing and investing in this digital currency. He discusses the pros and cons, and covers the more complicated method of acquiring bitcoin, mining. He predicts developments in regulation, technology, business, and society that will lead to bitcoin's price increasing 500 percent over the next two decades. In the wake of the current economic crisis, Lee calls on consumers to embrace a technology that will not only increase their wealth but make their lives easier\"-- Provided by publisher.

In this paper, we study the effects of noise on bipower variation, realized volatility (RV) and testing for co-jumps in high-frequency data under the small noise framework. We first establish asymptotic properties of bipower variation in this framework. In the presence of the small noise, RV is asymptotically biased, and the additional asymptotic conditional variance term appears in its limit distribution. We also propose consistent estimators for the asymptotic variances of RV. Second, we derive the asymptotic distribution of the test statistic proposed in (Ann. Stat. 37, 1792-1838) under the presence of small noise for testing the presence of co-jumps in a two-dimensional Itô semimartingale. In contrast to the setting in (Ann. Stat. 37, 1792-1838), we show that the additional asymptotic variance terms appear and propose consistent estimators for the asymptotic variances in order to make the test feasible. Simulation experiments show that our asymptotic results give reasonable approximations in the finite sample cases.

Financial expert, investment advisor and author James Rickards shows how generative AI is reshaping the world of finance, explaining what smart investors can do to protect their assets. AI-powered programmes like ChatGPT have become valuable tools in the financial market, and proven to be incredibly beneficial to investors looking to identify investment opportunities and risks that might be overlooked by humans. Yet there is a darker side to these products, which we are only just beginning to fully understand. In this book, Rickards shows how models like ChatGPT work, and how they can be leveraged to capitalise on markets and avoid losses by providing accurate, up-to-date financial insights. Rickards' guide is essential reading for anyone looking to navigate this tumultuous new climate.

The research on the mining algorithm of financial data association relationship mainly explores a certain kind of association relationship in depth, but it is not suitable for the attributes and characteristics of financial data itself, and there are few comprehensive analysis and application for financial data association relationship mining. In order to overcome the above problems, this paper proposes a financial data generation and prediction model based on GAN. Based on WGAN network, this paper improves the authenticity of the generated virtual samples by increasing the cyclic consistency loss term and selecting intermediate samples for the generated samples to optimize the generated model. At the same time, in the system, this paper adopts intelligent data analysis research method, mines the correlation of different dimensions of financial statement data, and presents the mining results by using the correlation visualization method, so as to realize the risk assessment and trend prediction of enterprise financial status. According to the comprehensive experimental analysis results, it can be seen that the model proposed in this paper has good performance in the authenticity analysis and prediction of financial data. Generally speaking, the model proposed in this paper provides a reliable tool for the authenticity audit of financial data, and can provide a reference for the formulation of subsequent schemes and policies through financial data prediction.翻译搜索复制