Template-Type: ReDIF-Paper 1.0
Author-Name: Rustam Azimov
Author-Name-First: Rustam
Author-Name-Last: Azimov
Author-Email: abdullaev.a@uzbekinvest.uz
Author-Workplace-Name: Uzbekinvest Export-Import Insurance Company
Author-Name: Mirsadikov Abdullaevich
Author-Name-First: Mirsadikov
Author-Name-Last: Abdullaevich
Author-Email: office@uzbekinvest.uz
Author-Workplace-Name: Uzbekinvest Export-Import Insurance Company
Title: Digital transformation of the Uzbekistan insurance market: Determinants, barriers, and strategic development trajectories
Abstract: This study examines the determinants, barriers, and strategic development trajectories of digital transformation in the insurance market of Uzbekistan within the context of global InsurTech trends. The research aims to bridge the gap between the rapid quantitative growth of the sector and its insufficient qualitative development driven by digital technologies. The theoretical framework is grounded in the works of R.S. Azimov, integrating concepts of institutional hysteresis and sufficient human capital with classical theories of innovation diffusion and human capital.The empirical analysis is based on panel data from 16 leading insurance companies in Uzbekistan over the period 2020?2024. A fixed-effects econometric model is employed to test key hypotheses regarding the impact of digital investments, IT infrastructure, Big Data implementation, and human capital deficits on digitalization and financial performance. The findings reveal that while digital investments and technological adoption positively influence performance, their effectiveness is significantly constrained by shortages in qualified personnel and outdated infrastructure.A key contribution of the study is the empirical validation of Azimov?s theoretical propositions. In particular, the results confirm that investments in digital technologies generate positive financial returns only after a critical threshold of human capital is achieved. Otherwise, such investments may lead to inefficiencies. The research also identifies institutional and market-related barriers, including regulatory uncertainty and low trust in digital channels.The paper proposes three strategic development trajectories? inerti al, catch-up, and breakthrough?emphasizing the need for coordinated efforts between the state and private sector. Practical recommendations are provided to support policy formulation and strategic planning aimed at fostering sustainable digital transformation in Uzbekistan?s insurance market.
Length: 10 pages
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 1-10
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=001&rid=17142
File-Function: First version, 0000
Number: 15817142
Classification-JEL: G22, O33, C23
Keywords: Digital transformation, Insurance market, InsurTech, Uzbekistan, R.S. Azimov, Econometric modeling, Panel data
Handle: RePEc:sek:iefpro:15817142
Template-Type: ReDIF-Paper 1.0
Author-Name: Khachatur Babayan
Author-Name-First: Khachatur
Author-Name-Last: Babayan
Author-Email: khach.babayan@gmail.com
Author-Workplace-Name: Burnaby Mountain Secondary School
Title: AI and the Economics of Everyday Environmental Behavior: A Teenager?s Circular Micro-Garden System at Home
Abstract: This paper explores how artificial intelligence (AI) can support everyday environmental behavior through a narrative case study of one high-school student who transformed a small apartment balcony into a circular micro-garden system. Using kitchen scraps, recycled containers and free AI tools such as ChatGPT and QualiGPT, the student created a household loop where waste became inputs, plants reduced packaging demand and AI guided efficient decision-making. The project demonstrates how AI supports real-time environmental learning by offering personalized explanations, troubleshooting plant-health issues through image analysis and reducing uncertainty around recycling choices. Findings show that AI increased confidence, reduced resource waste and strengthened long-term thinking, which are the key principles in Environmental Economics. While small in scale, the micro-garden functioned as a miniature circular economy shaped by AI-enhanced decision-making. The study suggests that when AI meets hands-on sustainability tasks, teenagers can meaningfully participate in environmental stewardship, reducing waste and practicing household-level resource optimization.
Length: 14 pages
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 11-24
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=002&rid=16983
File-Function: First version, 0000
Number: 15816983
Classification-JEL: 
Keywords: Artificial intelligence, Circular economy, Environmental behavior, Micro-gardening, Sustainability
Handle: RePEc:sek:iefpro:15816983
Template-Type: ReDIF-Paper 1.0
Author-Name: Alessandro Berti
Author-Name-First: Alessandro
Author-Name-Last: Berti
Author-Email: alessandro.berti@uniurb.it
Author-Workplace-Name: Urbino University Carlo Bo
Author-Name: Chiara Catenacci
Author-Name-First: Chiara
Author-Name-Last: Catenacci
Author-Email: chiara.catenacci@studenti.unitn.it
Author-Workplace-Name: Trento University
Title: Loan Origination Standards and Credit Quality: Evidence from the Implementation of the EBA Guidelines in Italy
Abstract: This paper investigates the macro-financial implications of the implementation of the European Banking Authority Guidelines on Loan Origination and Monitoring (EBA-LOM), which became applicable to new lending in June 2021. The Guidelines represent a major micro-prudential reform aimed at strengthening borrower assessment, data governance, collateral valuation, and monitoring practices in European banking.Using annual Bank of Italy data for the period 2017?2025, the analysis examines the evolution of credit supply and credit quality indicators for loans granted to non-financial corporations in Italy. The empirical strategy combines descriptive analysis with interrupted time-series models and reduced-form dynamic specifications controlling for macroeconomic conditions and the ECB monetary stance.The results provide no evidence of a structural contraction in aggregate lending following the implementation of EBA-LOM. At the same time, credit quality indicators?measured by default inflow rates and the NPL ratio?continue to improve or remain stable throughout the post-implementation period. Credit dynamics appear instead to be primarily driven by macro-financial conditions, particularly the sharp monetary tightening cycle initiated by the European Central Bank in 2022.These findings suggest that strengthened underwriting and monitoring standards can improve the resilience of bank loan portfolios without undermining aggregate credit provision in bank-based financial systems. The Italian experience therefore provides relevant policy insights for the design of prudential frameworks aimed at simultaneously supporting financial stability and sustainable credit supply.
Length: 20 pages
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 25-44
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=003&rid=17126
File-Function: First version, 0000
Number: 15817126
Classification-JEL: G21, G28, E51
Keywords: EBA Guidelines on Loan Origination and Monitoring, Credit Supply, Credit Risk, Lending Standards, Non-Performing Loans (NPLs), Bank Lending Channel, Monetary Policy and Credit Dynamics, Prudential Regulation
Handle: RePEc:sek:iefpro:15817126
Template-Type: ReDIF-Paper 1.0
Author-Name: Petr Mach
Author-Name-First: Petr
Author-Name-Last: Mach
Author-Email: petrmach1975@gmail.com
Author-Workplace-Name: The University of Finance and Administration
Title: Reserve requirements as an instrument of monetary policy
Abstract: Reserve requirements belong to the portfolio of instruments of many central banks. Although many central banks do not use reserve requirements actively as an instrument of monetary policy, the changes in the reserve requirements affect the volume of deposits, of the money stock and thus of the price level. In this contribution, the effective deposit multiplier that takes into account both the optimal reserve ratio of commercial banks as well as the minimum reserve requirements is formulated and the iteration process is illustrated in which the volume of deposits converges to an equilibrium. It can be argued that once minimum reserves exist, they can be used as an efficient instrument of monetary policy by its gradual decrease leading to a smooth desirable increase in the money stock aimed at maintaining price stability.
Length: 1 page
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 45-45
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=004&rid=17033
File-Function: First version, 0000
Number: 15817033
Classification-JEL: E59, E51, E50
Keywords: Reserve requirements. Deposit multiplier. The money stock. Monetary policy.
Handle: RePEc:sek:iefpro:15817033
Template-Type: ReDIF-Paper 1.0
Author-Name: Miroslav ?ipikal
Author-Name-First: Miroslav
Author-Name-Last: ?ipikal
Author-Email: miroslav.sipikal@euba.sk
Author-Workplace-Name: University of Economics and Business
Title: Public support for Science and Technology Parks Creation in Central Europe ? success or failure?
Abstract: The innovation performance of Central European countries has been very low compared to Western Europe. However, the accession of these countries to the European Union enabled them to access the financial resources of the cohesion policy, representing a significant shift in the availability of financial resources for innovative activities. When forming public policies, countries faced a choice of where to invest these funds. One of the main tools used by the countries was the support for building university science and technology parks (STPs). The aim of this article is to examine how successful public support for the introduction of this instrument was in selected Central European countries. For the investigation, a questionnaire survey was carried out among established science parks, supplemented by interviews in selected parks as well as other relevant institutions. The results showed great differences in the functioning of these institutions in individual countries as well as within them, with a few very successful examples, but also many failures. Continuous government support, the involvement of local actors in the establishment of STPs, and a high degree of independence of STPs when deciding on their activities can be identified as factors that positively influenced the success of STPs.
Length: 13 pages
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 46-58
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=005&rid=17081
File-Function: First version, 0000
Number: 15817081
Classification-JEL: O38
Keywords: Innovation policy, Science parks, Central Europe, Universities, Public support
Handle: RePEc:sek:iefpro:15817081
Template-Type: ReDIF-Paper 1.0
Author-Name: Suela Vasil
Author-Name-First: Suela
Author-Name-Last: Vasil
Author-Email: suela.maxhelaku@fshn.edu.al
Author-Workplace-Name: Departament of informatics, Faculty of Natural Sciences, University of Tirana
Author-Name: Armela Maxhelaku
Author-Name-First: Armela
Author-Name-Last: Maxhelaku
Author-Email: armela.maxhelaku@fdut.edu.al
Author-Workplace-Name: Department of Civil Law, Faculty of Law, University of Tirana
Title: Artificial Intelligence and Application Domains in FinTech
Abstract: Since late 2022, the rapid evolution of generative artificial intelligence and large language models has significantly accelerated the integration of AI into FinTech services, including credit scoring, fraud detection, algorithmic trading, and regulatory compliance. This rapid expansion of this literature identifies the need for taxonomic mapping of AI methods to FinTech application domains. In this article we have applied PRISMA 2020 guideline to peer-reviewed articles indexed in Scopus-and published between 2024 and early 2026? Using a systematic search strategy? 388 records were identified through database searching? Out of these articles? 144 articles met the eligibility criteria and were included in the review? Data were collected using a structured a coding sheet and synthesized through the taxonomic cross-tabulation of AI categories and FinTech application domains? The results show that machine learning? deep learning and natural language processing are the most frequently applied AI models and Random Forest? Long Short-Term Memory (LSTM) and BERT are the most applied AI algorithms in FinTech applications? The FinTech domains that are most heavily deployed are credit scoring and lending? fraud detection and security and cryptocurrency and blockchain applications? This article provides an AI-FinTech taxonomy that could serve as an evidence-based reference for academics? practitioners? and policymakers for the adoption of artificial intelligence in financial services?
Length: 8 pages
Creation-Date: 0000-00
Publication-Status: Published in Proceedings of the Proceedings of the International Conference on Economics, Finance & Business, Berlin, Nov -0001, pages 59-66
File-URL: https://iises.net/proceedings/international-conference-on-economics-finance-business-berlin/table-of-content/detail?cid=158&iid=006&rid=17211
File-Function: First version, 0000
Number: 15817211
Classification-JEL: C45, G20, O33
Keywords: Artificial intelligence, FinTech, Machine learning, Deep learning, Natural language processing
Handle: RePEc:sek:iefpro:15817211