Risk-based predictive modelling for audit verification: evidence from EU-funded programmes

Problem setting. Nowadays, the issue of obtaining funding for scientific research, scientific and technical (experimental) developments is increasingly important. This practice is common, for example, in the EU. Many sources are offered: EU Framework Programs for Research and Innovation, European Structural and Investment Funds, etc. However, to understand the financing procedure itself, it is necessary to have some knowledge, including regarding EU legislation. First of all, attention should be focused on the terms, because this is the foundation, the alphabet. One of them is the beneficiary. It is found in many EU legal acts, but does not always have a fixed definition. Analysis of recent researches and publications. The main goal of the program or fund is to receive project funding. Foreign scholars focus their research in this area (for example, Mario J. Al Sayah, Pierre-Antoine Versini, Daniel Schertzer, Olatz Ukar Arrien, Marta Enciso-Santocildes). This situation is due to the fact that they have more modern and updated research infrastructures than Ukraine. Terms remain beyond their attention. However, there are isolated works with only the mention of the term in the issue without any analysis of it (for example, Glybko S., Kokhan V.). Purpose of the research is to clarify the term «beneficiary» in the EU Framework Programs for Research and Innovation, European Structural and Investment Funds. Article’s main body. An attempt is made to conduct a comparative analysis of the considered term in the EU Framework Programs for Research and Innovation («Horizon 2020», «Horizon Europe») and the European Structural and Investment Funds. Each of them has its own EU legal framework. However, only Regulation (EU) № 1303/2013 of the European Parliament and of the Council of 17 December 2013 laying down common provisions on the European Regional Development Fund, the European Social Fund, the Cohesion Fund, the European Agricultural Fund for Rural Development and the European Maritime and Fisheries Fund and laying down general provisions on the European Regional Development Fund, the European Social Fund, the Cohesion Fund and the European Maritime and Fisheries Fund and repealing Council Regulation (EC) № 1083/2006, defines the term «beneficiary». Regulation (EU) 2021/695 of the European Parliament and of the Council of 28 April 2021 establishing Horizon Europe – the Framework Programme for Research and Innovation, laying down its rules for participation and dissemination, and repealing Regulations (EU) № 1290/2013 and (EU) № 1291/2013 refers to Regulation (EU, Euratom) 2018/1046 of the European Parliament and of the Council of 18 July 2018 on the financial rules applicable to the general budget of the Union, amending Regulations (EU) № 1296/2013, (EU) № 1301/2013, (EU) № 1303/2013, (EU) № 1304/2013, (EU) № 1309/2013, (EU) № 1316/2013, (EU) No 223/2014, (EU) № 283/2014, and Decision № 541/2014/EU and repealing Regulation (EU, Euratom) No 966/2012, which defines the term «beneficiary». The EU Framework Programmes for Research and Innovation («Horizon 2020», «Horizon Europe») contain definitions of the types of beneficiary. It is the target final beneficiary and beneficiary of the Accelerator. The possibility of achieving synergy between the specified programs and the European Structural and Investment Funds is being investigated, if there are already differences regarding the definition of the term «beneficiary». Conclusions and prospects for the development. The author states the following conclusions according to the obtained results: EU legal acts do not contain a single vision for the same term «beneficiary». We believe that it is necessary to review this situation in order to achieve the synergy established in the Regulations (EU).

The European Structural and Investment Funds (ESIF) represent a valuable tool of the European Union. EU countries use the funds to finance activities focused on different areas. They are primarily aimed at reducing regional disparities between regions. The objective of this paper is to examine the effect of the European Structural and Investment Funds on economic indicators in Slovakia. To achieve this aim, the paper examines the evolution of economic indicators between the years 2007 and 2021. Secondary data on the drawing of the European Structural and Investment Funds in Slovakia in the period under review are identified. The impact is detected in the paper based on regression and correlation analysis. Established on the correlation and regression analysis outcome, we can argue that there is a dependence between the implementation of ESIF and the economic indicators, although the implementation of the ESIF has a small impact on gross domestic product and unemployment.

The intentions of the Ukrainian authorities to increase the competitiveness of regional economies based on smart specialization caused the study of the characteristics of financing projects of smart specialization of regions of the European Union. The introduction of innovations in the activities of small and medium enterprises (SMEs) to increase their competitiveness is one of the key areas of financial support for smart specialization. The aim of the study is to identify the effectiveness of financial support for smart specialization projects in the EU regions in terms of funding research, development and innovation aimed at improving the competitiveness of small and medium-sized businesses. In the course of the study, we analyzed the databases of the European Structural and Investment Funds on the amount of funding for research, development and innovation aimed at increasing the competitiveness of small and medium-sized busi nesses, by areas of funding and EU countries. We also compared the level of use of funds and the level of achievement of the goals planned in the projects. EU countries and regions used the funds planned by the European Structural and Investment Funds to finance the implementation of innovations in SMEs only by a third due to weak interaction of SMEs with research centers, as well as due to insufficient projects to improve the efficiency of SMEs through implementation results of research, development and innovation. The main emphasis in supporting small and medium-sized businesses through the implementation of research advances has been placed in the smart specialization strategies of the Netherlands, Slovakia, Poland, the United Kingdom and Germany. However, the largest amount of funding for research, development and innovation in SMEs in 2014-2020 among EU member states was planned, in addition to Poland and Germany, for Italy and Spain. The analysis showed that by 2020 the level of implementation of only certain goals of smart specialization reached 50 % or more of the planned indicators. However, in the context of the EU countries, there was no homogeneity in achieving all the goals related to the development and functioning of small and medium-sized businesses through the implementation of research, development and innovation. The efficiency of using the financial resources of the European Structural and Investment Funds for the implementation of smart specialization strategies in the EU regions was not high. It is likely that by 2023 when the projects launched in 2014-2020 will be completed, the level of achievement by the EU regions of smart specialization goals in terms of implementing research, development and innovation in SMEs will increase. Nevertheless, in our opinion, it will not be possible to achieve their full implementation.

The article is devoted to the generalization of approaches to the identification of territorial units and management of the rural areas development in the EU and the justification of the need for its use in domestic practice. It was established that several different approaches to the identification of rural territories and territorial units were tested during the nearly twenty-year preparatory period (typology based on a 1 km2 grid; typologies at the LAU 2 level, in particular the degree of urbanization, etc.; at the NUTS 3 levels, in particular urban-rural typology, etc.). which were improved and presented in the Methodological manual on territorial typologies. Today, this technique is basic in establishing the common statistical classification of territorial units (NUTS). It is summarized that cohesion policy is aimed at reducing regional disparities and promoting economic and social cohesion, as well as territorial convergence between EU member states, which has been carried out since the founding of the EU in 1957. In 1992, thefourth cohesion policy fund was created - the Cohesion Fund (FZ) - to finance projects to improve the state of the environment and transport infrastructure in member states where the GNP per capita is less than 90% of the EU average. It was revealed that the formation of the Just Transition Fund in 2021, which corresponds to the EU “green” transition paradigm, was an addition to the institutional architecture. It is argued that the European Structural and Investment Funds (ESIF) are the main financial instrument for supporting cohesion policy and include the following components: ERDF (European Regional Development Fund), ESF (European Social Fund), Cohesion Fund (FZ), European Agricultural Fund for Rural Development (EAFRD) and the European Maritime and Fisheries Fund (EMFF). The ERDF has been identified as the largest component of the ESIF, which focuses on promoting economic and social cohesion by investing in infrastructure, innovation and entrepreneurship. This Fund was found to support projects related to transport, energy, digital infrastructure, research and innovation, and the transition to a low-carbon economy. It is emphasized that this fund contributes to employment, social integration and development of skills of the population. The peculiarities of the cohesion fund, which provides financial assistance to member countries whose GNI per capita is below a certain threshold level, have been considered. It has been established that this fund supports large-scale infrastructure projects in the fields of transport, energy and the environment. At the same time, the EAFRD and EMFF funds support rural development and fisheries respectively, contributing to the overall objectives of cohesion policy. It has been proven that since 2013, the development of multi-year programs for the development of rural areas of the EU countries has been focused on six priorities of sustainable development: promoting the transfer of knowledge and innovations; increasing competitiveness; promoting the organization of the food production chain and risk management; restoration, preservation and development of ecosystems; increasing the efficiency of resource use and transition to a low-carbon economy; promotion of social integration, reduction of poverty and economic development in rural areas. The European Green Course, which aims to make Europe the first climate-neutral continent by 2050, is analyzed. As for agriculture and food, the Green Deal envisages measures to increase sustainability in land management, food production and consumption, in particular through measures that will be included in the strategic plans of the common agricultural policy. Activities and practical actions in domestic practice regarding the creation of conditions for the implementation of the European experience regarding the identification of territorial units and the readiness to participate in attracting financial allocations within the framework of current programs based on the implementation of regulatory and legal support and the ability to master the allocated financial funds in full have been determined. Key words: rural territories, territorial units, cohesion fund, green course, sustainable development priorities, structural funds.

The aim of this paper is to identify indicators at country level that could prove useful in improving the effectiveness of fraud detection in European Structural and Investment Funds.We analyse data for 454 funds, belonging to the period 2014-2020, from the 28 countries that were members of the European Union in 2014.Explanatory results suggest the convenience of tracking funds, especially in countries with higher GDP and higher transparency levels, and the lesser relevance of the number of irregularities for countries with higher GDP and those receiving larger funds.Fraud and fraud detection rates in individual funds vary significantly across states.Federal states, such as the Federal Republic of Germany, are comparatively successful in detecting fraud in EU funds.

This paper presents a meticulously conducted and extensive comparative analysis of the absorption patterns of European Structural and Investment Funds (ESIF) throughout the 2014–2020 programming period across a diverse array of European countries, with a particular emphasis on the Greek context. The primary objective of this study is to offer a comprehensive understanding of both shared trends and variations in fund absorption dynamics, thereby providing valuable insights into the intricate factors that shape the utilization of ESIF. Utilizing a robust methodological framework, this research undertakes an exhaustive examination of national and regional data to unravel the multifaceted determinants influencing absorption rates. Key determinants, including macroeconomic conditions, administrative efficiency, institutional capacity, and policy frameworks, are rigorously scrutinized to elucidate their nuanced impacts on ESIF absorption dynamics. Through this methodical approach, the study endeavors to provide nuanced insights into the efficacy of fund allocation mechanisms and the pivotal role played by socio-economic factors in steering regional development trajectories.

The programming period is a seven-year period in which the Member States of the European Union can draw funds for activities supported by the European Structural and Investment Funds. The institutional, procedural arrangements for the implementation of the European Funds within the Member States are regulated by European and national legislation. The paper aims to compare the 2014-2020 programming period and the 2021-2027 programming period in terms of the setup of processes resulting from national legislation. The comparison will be based on the comparison of Act 292/2014 on the Law on the and Law 121/2022 Coll. on contributions from European Union funds and on amendment and supplementation of certain acts. By analyzing the above-mentioned laws, we have found that from the institutional and procedural point of view, changes have been made in the setting of the new programming period. These changes could simplify the implementation of the European Structural and Investment Funds in Slovakia in the coming years.

Background Limited knowledge exists of the extent of epigenetic alterations, such as DNA methylation, in Heart Failure (HF). We conducted targeted DNA methylation sequencing to identify DNA methylation alterations in coding and non-coding RNA across different etiological sub-types of HF. Methods and results A targeted bisulfite sequence capture sequencing platform was applied to DNA extracted from cardiac interventricular septal tissue of 30 male HF patients encompassing etiologies including Hypertrophic Obstructive Cardiomyopathy (HOCM, n=12), Ischemic Cardiomyopathy (ISCM, n=9), Dilated Cardiomyopathy (DCM, n=9), and 9 control patients with non-failing hearts (NF). We detected 62,678 differentially methylated regions (DMR) in the studied HF cohort. By comparing each HF sub-group to the NF control group we identified 195 unique DMRs: 5 in HOCM, 151 in DCM, and 55 in ISCM. These translated to 4 genes/1 non-coding RNA (ncRNA) in HOCM, 131 genes/17 ncRNA in DCM, and 51 genes/5 ncRNA in ISCM. Subsequent gene/ncRNA expression analysis was assessed using qRT-PCR and revealed 6 genes: 4 hypermethylated (HEY2, MSR1, MYOM3, COX17), 2 hypomethylated (CTGF, MMP2); and 2 microRNA: 1 hypermethylated (miR-24–1), 1 hypomethylated (miR-155) with significantly up- or down-regulated expression levels consistent with the direction of methylation in the particular HF sub-group. Conclusions For the first time DNA methylation alterations and associated gene expression changes were identified in etiologically-variant pathological heart failure tissue. The methylation-sensitive and disease-associated genes/non-coding RNA identified from this study represent a unique cohort of loci that demonstrate a plausible potential as a novel diagnostic and/or therapeutic target in HF and warrant further investigation. Acknowledgement/Funding Enterprise Ireland; European Regional Development Fund under Ireland's European Structural and Investment Funds Programmes 2014-2020

In order to support economic development across all European Union regions, €351.8 billion –almost a third of the total EU budget– has been set aside for the Cohesion Policy during the 2014–2020 period. The distribution of this budget is made through five main structural and investment funds, after long and difficult negotiations among the EU member states. This paper analyzes the problem of allocating the limited resources of the European Regional Development Fund as a conflicting claims problem. Specifically, we attempt to show how the conflicting claims approach fits this actual problem, and we propose alternative ways of distributing the budget via (i) claims solutions or (ii) the imposition of bounds (guarantees) to each of the regions. By applying this approach we also show that there is a claims solution that performs better than the others by reducing inequality and promoting convergence to a greater degree. It is clear that political bargaining will always be part of the allocation process. However, having an intuitive initial proposal may help politicians to find the best agreement. To that effect, we propose the use of a claims solution as a way to find an initial proposal for future policy changes concerning the allocations of the EU structural funds.

This paper investigates the present state of investment funds across the European Union together with market developments and regulatory changes and upcoming matters that define this essential financial sector. The European investment fund sector maintained assets worth approximately €18.8 trillion during 2020 which positions it as a leading force in worldwide finance. The research investigates fund market structures by showing how UCITS funds dominate while fund domicile locations primarily exist in Luxembourg and Ireland. The article explores EU regulatory development by starting with the UCITS Directive and then moving to recent regulations including the Sustainable Finance Disclosure Regulation and European Long-Term Investment Funds Regulation. The barriers to a unified single market for investment funds consist of diverse regulatory rules and complex taxation systems. Possible future policy directions emphasize improved supervisory alignment and enhanced risk management systems and better cross-border fund distribution to support EU Capital Markets Union goals.

A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may be critical enough to impact the effectiveness of the machine learning model. In our paper, we investigate the relationship between time and predictive model maintenance. Our work incorporates severely imbalanced big data from three Medicare datasets, namely Part D, DMEPOS, and Combined, that have been used in several fraud detection studies. We build training datasets from year-groupings of 2013, 2014, 2015, 2013–2014, 2014–2015, and 2013–2015. Our test datasets are built from the 2016 data. To mitigate some of the adverse effects from the severe class imbalance in these datasets, the performance of five class ratios obtained by Random Undersampling and five learners is evaluated by the Area Under the Receiver Operating Characteristic Curve metric. The models producing the best values are as follows: Logistic Regression with the 2015 year-grouping at a 99:1 class ratio (Part D); Random Forest with the 2014-2015 year-grouping at a 75:25 class ratio (DMEPOS); and Logistic Regression with the full 2015 year-grouping (Combined). Our experimental results show that the largest training dataset (year-grouping 2013–2015) was not among the selected choices, which indicates that the 2013 data may be outdated. Moreover, we note that because the best model is different for Part D, DMEPOS, and Combined, this suggests that these three datasets may actually be sub-domains requiring unique models within the Medicare fraud detection domain.

<p class="AbstractText">Micro, small and medium-sized enterprises (SMEs) are considered to be the engine of the European economy. In EU SMEs make up more than 99% of all companies and provide around 75 million jobs. They are a key source of job creation and stimulation of entrepreneurial spirit and innovation and are therefore essential for fostering competitiveness and maintaining employment in the EU. Yet SMEs often have problems in raising funds to finance fixed investments and inventory and working capital. SMEs mostly use internal sources while the most important external sources of SMEs are bank loans, which are not easily accessible. Hence, one of the priorities of the European Commission is stimulating SMEs through EU funds and programs in order to enable SMEs development and finally to contribute to economic growth. SMEs sector also dominates the economic structure of Croatian economy and they have similar financing problems. However, Croatia in period 2014 – 2020 have at disposal a total amount of 10.676 billion of euros from European structural and investment funds, of which 8.397 billion are aimed for cohesion policy objectives and 470 million euros are aimed for the development and competitiveness of SMEs. Besides, EU programs COSME and HORIZON 2020 are intended to provide direct support to SMEs and to create a favorable environment for their development. However, even though the number of successful project applications for small and medium-sized enterprises in the total share of Croatian project applications is relatively high, fund absorption has not reached satisfactory levels. Using the sample of Split-Dalmatia County’s SMEs, this study finds out reasons for low absorption capacity from SMEs point of view: SMEs are interested in funds from European Union, but are not sufficiently familiar with their availability i.e. funds at their disposal, especially programs COSME and HORIZON 2020; SMEs state that they know how to apply for EU funds but considered the procedure as complicated and demanding regarding documentation, and that the needed knowledge and know-how for proposal is beyond their scope; SMEs are not satisfied with the cooperation with state institutions, especially Ministry of Regional Development and EU funds and Croatian Chamber of Economy. We point out four main conclusions: (1) SMEs are not enough aware of EU financing possibilities; (2) additional promotion of EU programs directly to SMEs is required; (3) additional education of SMEs regarding project proposal and documentation is needed and (4) more proactive policy of state institutions and cooperation with local authorities is expected. Finally, greater awareness and professional help might be a key in strengthening the SMEs, whose entrepreneurial spirit and innovation process is a prerequisite for economic growth and development.</p>

This Policy Brief addresses the concept of synergies arising from the two major EU funding sources (The European Structural and Investment Funds and Horizon 2020) in the context of the new Stairway to Excellence Project. This project is centred on the provision of assistance to Member States who joined in 2004, 2007 and 2013 in using innovation funding under ESIFs via the early and effective implementation of RIS3 with the aim of closing the innovation gap and promote scientific and technological excellence. This Policy Brief summarises the discussion and case studies presented at the launching conference of the Stairway to Excellence Project held in Prague in October 2014. This event offered a first opportunity to identify the key elements for building successful synergies and gave a useful insight into how synergies could be achieved in practice. A diverse set of experiences from five EU countries (Cyprus, Czech Republic, France, Spain, and the UK) and an international organisation were presented. In turn, this could be a source of inspiration for other regional and national managing authorities and the research community.

This paper focuses on the maturity of e-government services on the domain of European fund management, to explore the measurability of potential efficiency gains. The practical relevance of the topic is that the current European legislation prescribes that Member States shall offer electronic fund management services to beneficiaries to foment the efficient use of European Structural and Investment Funds. The main driver of this concept is the reduction of administrative burdens which can be achieved by the Europe-wide utilization of paperless fund management tools and by harnessing the interoperability of information systems. In the scientific discourse, the above concept is labelled as “e-Cohesion”. However, the legislation sets quite broad requirements for its implementation, so the e-Cohesion landscape and the intended efficiency gains may appreciably differ from country to country. The exploration of this “digital efficiency divide” offers a new relevant research opportunity. Therefore, this article is dealing with the measurability of efficiency of e-tools to methodologically support Member States in the realization and fine-tuning of their national e-Cohesion concepts. It is presented in this paper that the level of potential efficiency gains is connected to e-government readiness, i.e. the maturity of e-Cohesion systems. The paper, therefore, systematically reviews the relevant e-Government literature on the issue of maturity. Based on this, it aims to identify the most important models and methodological elements which address the main attributes of e-Cohesion to pave the way for further empirical research and the creation of an e-Cohesion-specific maturity model.

The need to investigate suitable alternatives to conventional fossil fuels has increased interest in several renewable energy sources, especially in widely available sources of biomass. This has made environmental and socio‐economic improvements possible. In recent years, biogas from biomass has increasingly been considered the most feasible alternative to energy from fossil fuel, as it allows both the possibility to reduce waste disposal treatment and the opportunity to produce green energy. Opuntia ficus‐indica (OFI) has been suggested as a possible biomass as it could represent a suitable resource for producing biomethane as a new frontier within the context of a circular economy. This study aims to define a methodology for evaluating the distribution of OFI biomass that is potentially available for biogas production by applying maximum entropy modeling in a case study of a province located in southern Italy. The geographic information system (GIS)‐based model that was developed made it possible to estimate the suitability of the species in the territory and to define specific indices at the municipal level by enabling the localization of the highest productive territorial areas. Municipalities were grouped into three different classes ranging from low to high potential ones. In future years, new municipalities could be part of the high‐potential group, due to global warming resulting from climate change. The results could be relevant to the intervention priorities established by the European Union related to the planning activities supported by the European Structural and Investment Funds within the Smart Specialization Strategy. In this regard, knowledge of the potential production and distribution of a species in a territory is highly valuable information for the quantification of the biomass potentially available for biogas production, prior to strategic planning for the improvement of the biogas sector. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd