Speakers, Bios and Abstracts
- Prof. Dr. Ali Hirsa, Columbia University, US
Do we need more GANs in the Zoo of GANs? Why not? TAGAN & TTGAN for simulating financial time series - Bob Chakravorti, CEO Chakra Advisors, US
The Impact of FinTech and Big Tech Firms on the Way We Pay - Kerem Tomak, Global Chief Analytics Officer, ING, Netherlands
Building an Effective Analytics Organisation in Financial Services - Prof. Dr. Wolfgang Karl Härdle, Humboldt University Berlin, Germany
代 DAI the Digital Art Index - Prof. Dr. Branka Hadji Misheva, Bern University of Applied Sciences, Switzerland
eXplainable AI for finance: applications to credit risk and financial time series - Prof. Dr. Joerg Osterrieder, University of Twente, Netherlands
The European COST Action Fintech and Artificial Intelligence in Finance
Cooperation ING and University of Twente – Artificial Intelligence in Finance - Prof. Dr. Vijaya B Marisetty, University of Twente, Netherlands
Workshop on Blockchain for Finance
Is Blockchain Beneficial for Banking? International Evidence from Ripple Intervention in Banking Industry
Are Fintech Loans Harmful? Evidence from Users’ Experience on Loan Apps in India - Dr. Abhishta Abhishta, University of Twente, Netherlands
Workshop on Internet Measurements for Cost Accounting - Prof. Dr. Petre Lameski, SS. Cyril and Methodius University, Skopje, North Macedonia
An empirical analysis of predictive approaches on low volume stock exchanges – a case study of the Macedonian Stock Exchange - Prof. Dr. Ronald Hochreiter, WU Vienna, Austria
Direct Indexing and Bespoke Indexing using Optimization under Uncertainty - Prof. Dr. Ana Ivanisevic Hernaus, University of Zagreb, Croatia
From perceived mobility to the intention to use mobile payments: A moderated mediation analysis - Dr. Benjamin Clapham, Goethe University Frankfurt, Germany
Give them a second chance? Prediction of recurrent financial intermediary misconduct - Prof. Dr. Fethi Rabhi, University of New South Wales, Australia
A data analytics architecture for the exploratory analysis of high-frequency market data
Towards an API marketplace for an e-invoicing ecosystem - Koen Meijer, University of Twente, Netherlands
Role of culture in determining customer acceptance of neobanks - Nicole Königstein, impactvise, Switzerland
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment - Stefana Belbe, Babes-Bolyai University, Romania
Evaluating sustainability goals at European level from a spatial perspective using taxpayers` perception and sentiment analysis - Prof. Dr. Codruta Mare, Babes-Bolyai University, Romania
Evaluating sustainability goals at European level from a spatial perspective using taxpayers` perception and sentiment analysis
Financial Sentiment Analysis in Low Resource Languages
Machine Learning Algorithms to Predict Crop Insurance Purchase – Case Study on Romanian Farmers - Ivo Lemken, Leiden University, Netherlands
Realising Value from AI-Enabled Decision Systems with Fair Outcomes: An ExplorativeCase Study - Jan Pauls, University of Muenster , Germany
Realising Value from AI-Enabled Decision Systems with Fair Outcomes: An Explorative Case Study - Jane Ngaruiya, Consultant, World Bank Group, Kenya
The effect of changes in interest rate regulation on the financial performance of banks in Kenya - Dr. Jascha-Alexander Koch, Goethe University Frankfurt, Germany
A Framework to Measure Corporate Regulatory Exposure - Prof. Dr. Peter Gomber, Goethe University Frankfurt, Germany
A Framework to Measure Corporate Regulatory Exposure - Dr. Ioana Florina Coita, University of Oradea, Romania
Evaluating sustainability goals at European level from a spatial perspective using taxpayers` perception and sentiment analysis - Laurens Breda, University of Twente, Netherlands
Is Blockchain Beneficial for Banking? International Evidence from Ripple Intervention in Banking Industry - Akbar Ali, University of Hyderabad, India
Are Fintech Loans Harmful? Evidence from Users’ Experience on Loan Apps in India - Daniela Manate, Babes-Bolyai University, Romania
Machine Learning Algorithms to Predict Crop Insurance Purchase – Case Study on Romanian Farmers - Prof. Dr. Ekaterina Svetlova, University of Twente, Netherlands
AI ethics and systemic risks in finance - Stavros Pantos, University of Reading, United Kingdom
i-CAAP: designing stress tests for UK fast-growing firms and fintech - Prof. Dr. Jos van Hillegersberg, University of Twente, Netherlands
FinanceCom 2022 and the University of Twente
Cooperation ING and University of Twente – Artificial Intelligence in Finance
Closing and Outlook - Anand Autar, ING, Netherlands
Cooperation ING and University of Twente – Artificial Intelligence in Finance - Risk Goslinga, Paypal, Netherlands
Driving a digital payments paradigm for businesses and entrepreneurs
Conference Chairs
Speakers
Programme Committee and Organizers
Bob Chakravorti, CEO, Chakra Advisors
Bob, CEO of Chakra Advisors, advises senior leaders at financial institutions, central banks, investment firms, payment networks, and FinTech companies about competitive, technological and regulatory forces shaping the financial system. Some of his clients include Ripple, Visa, the World Bank and fintech startups. He also manages the Incumbents and Disruptors Blog focusing on the financial services industry. His research focuses on the impact of new entrants to financial markets with an eye towards improving efficiency and access while maintaining financial stability and resiliency. Prior to his current role, Bob was the Chief Economist at the Clearing House (TCH) where he oversaw all quantitative studies for TCH members—the largest 24 banks operating in the U.S.—focusing on analyzing proposed regulations to make the financial system safer after the crisis. He also testified in front of the U.S. Congressional Financial Services Committee regarding financial reforms addressing Too Big To Fail. Before joining TCH, Bob was an economist at the Chicago and Dallas Federal Reserve Banks focusing on financial market structure and competition. He advised the Bank of Mexico, Bank of England, De Nederlandsche Bank (the Dutch central bank), and the IMF about financial markets.
In addition, Bob is a frequent presenter at academic and industry conferences around the world. He has published over 45 articles in academic and industry journals. He received his PhD in economics from Brown University and his BA in economics and genetics from University of California, Berkeley.
The Impact of FinTech and Big Tech Firms on the Way We Pay
New technologies, new payment providers especially non-bank ones, and new regulations have impacted the way we pay for goods and services along with accessibility of financial services more broadly. Studies have shown that digital payments increase economic growth and enable greater access to credit and investment opportunities. In addition, digital payments allow governments to deliver social benefits more efficiently to its residents. In this talk, how various forces have enabled greater adoption of digital payments, what obstacles remain, which public policies have worked, and which payment segments are still resistant to digitization will be discussed. Furthermore, how data analytics tools such as machine learning tools can provide analysis of the impact of policies will be discussed. Finally, the impact of cryptocurrencies and central bank digital currencies on payments and financial services broadly will be discussed.
Kerem Tomak, Global Chief Analytics Officer, ING
Kerem Tomak is ING’s Global Chief Analytics Officer since Jan 2021. Dr. Kerem Tomak studied mathematics, economics and information systems in Turkey and the USA. He embarked on his professional career as an assistant professor at the University of Texas, Austin. Dr. Tomak brings more than 15 years of experience as a data scientist and an executive. Prior to his current appointment as the Global Chief Analytics Officer at ING Group, he was the founder and head of Big Data, Advanced Analytics and AI division at Commerzbank AG in Frankfurt. He has expertise in the areas of hybrid-multi-cloud architectures for scaling data driven products, AI/Machine learning applications in retail and financial services, digital transformation, omnichannel and cross-device attribution, price and revenue optimization, promotion effectiveness, yield optimization in digital marketing and real time analytics. He has managed mid and large-size analytics and digital marketing teams in Fortune 500 companies including Google and Yahoo and delivered large scale analytics solutions for marketing and merchandising departments for retailers like Walmart and Macy’s in the USA. His out-of-the box thinking and problem solving skills led to 4 patent awards and numerous academic publications. He is also a lecturer at the Frankfurt School of Finance and Management in the Applied Data Science Masters program as well as a sought after speaker in Big Data and AI.
Building an Effective Analytics Organisation in Financial Services
In this presentation, Kerem Tomak – ING’s global chief analytics officer – is going to share valuable insights on the optimal ways of integrating analytics to business. How can you leverage data science to achieve AI scalability in financial products and services? What is the importance of the analytics translator role and how can you help business adopt it at scale? How do we use the cloud to empower our clients and employees? What is the difference between a data driven and a fast-fail and learn culture? Kerem answers to all these questions and more.
Prof. Dr. Wolfgang Karl Härdle, Humboldt University Berlin, Germany
Wolfgang Karl Härdle completed his Dr. rer. nat. in Mathematics at Heidelberg University and received his habilitation in Economics at Bonn University. He was the founder and Director of Collaborative Research Center CRC 373 “Quantification and Simulation of Economic Processes” (1994 – 2003), Director of CRC 649 “Economic Risk” (2005 – 2016) and also of C.A.S.E. (Center for Applied Statistics and Economics) (2001 – 2014). He is currently heading the Sino-German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. He is the Ladislaus von Bortkiewicz Professor at Humboldt-Universität zu Berlin and director of the BRC the joint Blockchain Research Centre with Zurich U.
His current research focuses on modern machine learning techniques, smart data analytics and the cryptocurrency eco system. He has published more than 40 books and more than 350 papers in top statistical, econometrics and finance journals. He is highly cited, and among the top scientist registered at REPEC and has similar top notch rankings in other scales, such as the Handelsblatt ranking.
He has professional experience in financial engineering, structured product design and credit risk analysis. His recent research extends nonparametric paradigms into machine learning, decision analytics and data science for the digital economy. He is the Editor in Chief of the Springer Journal „Digital Finance“. He supervised more than 60 PhD students and has long-term research relations to research partners in the USA, Singapore, Prague, Warsaw, Paris, Cambridge, Beijing, Xiamen, Taipei among others.
代 DAI the Digital Art Index
The 代 DAI Digital Art Index has been developed to reflect the increasing activities on the the Digital Art market. Based on the most liquid exchanges, NFT data and prices are collected in cooperation with artnet.com, NYC . The NFT art market has risen sharply recently and is competing with traditional arts market. The observed transactions are analysed and an index is developed on a hedonic regression framework. We present an introduction into NFTs, explain their construction and “huberize” the hedonic regression context.
vProf. Dr. Ali Hirsa, Columbia University, US
Ali Hirsa is a professor, director of financial engineering program at IEOR, and director of Center for AI in business analytics & Financial technology at Columbia University in the City of New York. He is also Managing Partner at Sauma Capital, LLC a New York Hedge Fund and CSO at ASK2.ai. Ali has worked at both sell-side and buy-side for 25 years.
Ali’s research interests are algorithmic trading, machine learning, deep learning, data mining, optimization, computational and quantitative finance.
Ali is author of “Computational Methods in Finance”, Chapman & Hall/CRC 2012 and co-author of “An Introduction to Mathematics of Financial Derivatives”, third edition, Academic Press and is Editor-in-Chief of Journal of Investment Strategies. He has several publications and is a frequent speaker at academic and practitioner conferences.
Ali received his PhD in Applied Mathematics from University of Maryland at College Park under the supervision of Professors Howard C. Elman and Dilip B. Madan.
Do we need more GANs in the Zoo of GANs?
Why not?
TAGAN & TTGAN for simulating financial time series
Realistic simulation of financial time series is very essential and important since it extends the limited real data for training and evaluation of trading strategies. It is also challenging because of the complex statistical properties of the financial data. We introduce two new generative adversarial networks (GANs), which utilize the convolutional networks with attention and transformers. The proposed GANs are called temporal attention GAN (TAGAN) & temporal transformer GAN (TTGAN). They learn the statistical properties in a data driven manner and the attention mechanism helps to replicate the long range dependencies.
TAGAN & TTGAN are tested on the S&P 500 index and its option surface, examined by scores based on the stylized facts and are compared with the pure convolutional GAN. The attention-based GANs not only reproduce the stylized facts, but also smooth the autocorrelations of both levels and returns.
Rik Goslinga, Sr. Director Financial Services International, PayPal
Rik Goslinga is responsible for Financial Services International at PayPal. Rik studied Business Information Technology at the University of Twente in the Netherlands where he graduated in 2005. In 2009 he completed a Management program at INSEAD in Fontainebleau France.
Rik joined PayPal in 2020 to lead the Corporate Strategy in Europe and became a member of the European Leadership team. In his second role he was leading a global Product Marketing team before taking on his current role where he is responsible for Financial Services in PayPal’s markets outside of North America. Key products in his remit include Debit Cards, Google Pay and QR Code payments.
Before joining PayPal, Rik had a long tenure in strategy consulting with Kearney where he worked across the globe for many Fortune 500 companies including Mastercard and Amazon. He was a key leader in the Global Payment Practice advising key players across the payments value chain including banks, retailers, payment service providers and payment schemes. Rik lives in the Netherlands with his wife and three sons.
Driving a digital payments paradigm for businesses and entrepreneurs
Raging inflation, labor shortages, and a macroeconomic environment that hinders international trade and yet entrepreneurs in the small and medium-sized business sector see plenty of opportunities for growth. Empowering SMBs is a priority ESG topic for PayPal and is central to PayPal’s mission to democratize financial services. PayPal supports SMBs globally through access to capital and tools to drive growth. In his presentation Rik will talk about the next generation of financial services and how PayPal leverages data intelligence and its two-sided network of consumers and businesses to help SMBs grow and compete in a highly competitive world with digital giants.
Prof. Dr. Branka Hadji Misheva, Bern University of Applied Sciences
Branka Hadji Misheva is professor at Bern University of Applied Sciences, working on AI applications in finance, XAI methods, network models and fintech risk management. She holds a PhD in Economics and Management of Technology with a specific focus on network models as they apply to the operation and performance of P2P lending platforms, from the University of Pavia, Italy. At her position at ZHAW, she leads several research and innovation projects on Artificial Intelligence and Machine Learning for Credit Risk Management. She is a research author of 15 papers in the field of credit risk modeling, graph theory, predictive performance of scoring models, lead behavior in crypto markets and explainable AI models for credit risk management.
eXplainable AI for finance: applications to credit risk and financial time series
Artificial intelligence (AI) is creating one of the biggest revolution across technology-driven application fields. For the finance sector, it offers many opportunities for significant market innovation and yet broad adoption of AI systems heavily relies on our trust in their outputs. Trust in technology is enabled by understanding the rationale behind the predictions made. In other words, we need to make sure that values and domain knowledge are reflected in the algorithms’ outcomes. To this end, the concept of eXplainable AI (XAI) emerged introducing a suite of techniques attempting to explain to users how complex models arrived at a certain decision. Even though many of the classical XAI approaches can lead to valuable insights about the models’ inner workings, in most cases these techniques are not tailored for time series applications due to the presence of possibly complex and non-stationary dependence structure of the data. In this paper, we propose a generic XAI-technique for deep learning methods (DL) which preserves and exploits the natural time ordering of the data by introducing a family of so-called explainability (X-)functions. This concept bypasses severe identifiability issues, related among others to profane numerical optimization problems, and it promotes transparency by means of intuitively appealing input-output relations, ordered by time. We illustrate the generic concept based on financial time series and we derive explicit expressions for two specific X-functions for tracking potential non-linearity of the model and, by extension, for tracking non-stationarity of the data generating process. Our examples suggest that this natural extension of the original XAI-prospect, namely inferring a better understanding of the data from a better understanding of the model, might provide added value in a broad range of application fields, including risk-management and fraud detection.
Prof. Dr. Joerg Osterrieder, University of Twente, Netherlands
Joerg Osterrieder is Associate Professor of Finance and Artificial Intelligence at the University of Twente, Netherlands. He has been working in the area of financial statistics, quantitative finance, algorithmic trading, and digitisation of the finance industry for more than 15 years.
Joerg is the Action Chair of the European COST Action 19130 Fintech and Artificial Intelligence in Finance, an interdisciplinary research network combining 200+ researchers and 38 European countries as well as five international partner countries. He is the director of studies for an executive education course on “Big Data Analytics, Blockchain and Distributed Ledger”, co-director of studies for “Machine Learning and Deep Learning in Finance” and has been the main organizer of an annual research conference series on Artificial Intelligence in Industry and Finance since 2016. He is a founding associate editor of Digital Finance, an editor of Frontiers Artificial Intelligence in Finance and frequent reviewer for academic journals.
In addition, he serves as an expert reviewer for the European Commission on the “Executive Agency for Small & Medium-sized Enterprises” and the “European Innovation Council Accelerator Pilot” programmes.
Previously he worked as an executive director at Goldman Sachs and Merrill Lynch, as quantitative analyst at AHL as well as a member of the senior management at Credit Suisse Group. Joerg is now also active at the intersection of academia and industry, focusing on the transfer of research results to the financial services sector in order to implement practical solutionsProf. .
Artificial Intelligence in Finance – The European COST research network
Joerg will give an overview of the European COST Action Fintech and Artificial Intelligence in Finance. COST stands for Cooperation in Science and Technology and the is the longest running research funding agency in Europe. He is the Action Chair of this COST Action, a network of more than 200 researchers from 38 European countries and 5 international partner countries. The research is broadly focusing on Artificial Intelligence in Finance, with a specific emphasis on transparent financial markets, methods and products.
Prof. Vijaya B Marisetty, University of Twente, Netherlands
Prof. Vijaya B Marisetty has more than 20 years of experience in finance teaching and research. Before moving the University of Twente, he worked at Monash University, RMIT University, IIM Bangalore and the University of Hyderabad. He received several research grants to do research and implement blockchain application for financial inclusion. He runs a blockchain initiative called “inclusive growth chain” that tried to democratize wealth distribution through blockchain technology. He holds a PhD in finance from Monash University and Post-Doc from Wharton Business School and the Indian School of Business.
Blockchain in Finance
Blockchain is an emerging technology with high potential in Fintech industry. The workshop introduces blockchain and the business case for using blockchain in Fintech industry. The workshop will explore various stages of blockchain evolution, the role and mechanics of smart contracts, blockchain potential in banking and insurance industries. The speaker will share his own experiences of building blockchain platforms in banking and financial services industries.
dr. Abhishta Abhishta, University of Twente, Netherlands
Abhishta is an assistant professor at the Industrial Engineering and Business Information Systems group at University of Twente. His research focuses on empirically measuring the economic/financial impact of cyber attacks. In order to do so he devices/adapts data-driven economic impact assessment techniques. He looks to help organisations make well-measured investments in security.His doctoral research was funded under NWO project D3 – Distributed Denial-of-Service Defense: protecting schools and other public organizations. Other than finding (un)cool methods of finding the “devil” in financial details he is also involved in teaching and guiding students. From time to time he also delivers lectures to executives and collaborates with companies to help them with security decision making. He is also a member of behavioural data science incubator at the faculty of BMS at the university, where he contributes to solving social science problems using data science.
Internet Measurements for Cost Accounting
Cyber attacks can cause considerable financial damage to firms. Traditionally, survey based methodologies have been used to account for direct and indirect costs of these attacks. In this talk, I will discuss the short comings of some survey based methodologies and introduce novel empirical techniques that can be used to account for these costs.
Prof. Dr. Petre Lameski, North Macedonia
Petre Lameski Ph.D. is an Assistant Professor and Vice-dean for academic affairs at the SS. Cyril and Methodius University in Skopje, Faculty of Computer Science and Information Technologies. He has finished his university studies with highest honours. Petre has participated in over ten national research projects and has been lead researcher in eight
national research projects. He has participated in over six International
research projects. He has over one hundred publications in national and
international conferences and journals. Petre has received the Successful Youth award for 2020, in the category of best young scientist in natural sciences, mathematics and technology, in North Macedonia, awarded by the president of the country. For the last fifteen years, he has also been actively engaged as consultant for industrial research and development of products, applications and POCs, that leverage the latest advancements of machine learning and artificial intelligence in companies in N. Macedonia, Europe, USA and Canada. Petre’s main
research interests include deep learning, time series data analysis, transfer learning, explainable artificial intelligence and application of machine learning algorithms for solving various problems in finance, healthcare and agriculture.
Empirical analysis of predictive approaches on low volume stock exchanges – a case study of the Macedonian Stock Exchange
Prediction of future values is a challenging task, especially for stock markets that have a low number of stock listings and trades. In this work we apply several machine learning algorithms to predict the ROI and volatility of stocks in the Macedonian Stock Exchange (MSE) in order to evaluate the efficiency of machine learning algorithms for this task and select the most important indicators. We also evaluate several external factors that influence the MSE
Prof. Dr. Codruta Mare, Babes-Bolyai University, Cluj-Napoca, Romania
Codruța MARE is a Professor at the Dep. of Statistics-Forecasts-Mathematics since October 2021 and PhD coordinator in the field of Cybernetics and Statistics since October 2019 at the Faculty of Economics and Business Administration, Babes-Bolyai University, Cluj-Napoca, Romania. She started her university career in 2009, when she became a Teaching Assistant in the same department. She got her PhD in 2010 from the Trieste University, Italy, in the field of Economics (Economic Geography). In January 2019 she founded the Interdisciplinary Centre for Data Science, along with other teachers and researchers of the Babes-Bolyai University interested in Econometrics and Data Science. She is the Scientific Director of this research center. She teaches several types of Statistics and Econometrics methods, from Descriptive Statistics to Economic Forecasting and Spatial Econometrics. She has expertise in consultancy and research projects conducted both for public institutions (The World Bank, European Commission, Romanian Ministry of Structural Funds, Cluj-Napoca City Hall, etc.) and for private companies, along with delivering trainings both in data analysis and visualization, in Romania and abroad. She is a member of the Spatial Econometrics Association and of the Romanian Statisticians Club.
In respect to CA19130, she is currently involved in several research projects dealing either with financial issues and financial criminality in a cybernetic world, or with financial and insurance literacy and development. Results of her research were published in books and articles in prestigious international journals.
She has been the STSM coordinator of CA19130 since the beginning of this Action, in 2020, and starting Grant Period 2, in 2022, she is the Grant Award Coordinator of this Action.
Google Scholar: https://scholar.google.com/citations?hl=en&user=xJBU4dcAAAAJ
LinkedIn: https://www.linkedin.com/in/codruta-mare-7b6273199/
Financial Sentiment Analysis in Low Resource Languages
Sentiment analysis in finance gained importance in the last few years due to the fact that investors are humans who possess feelings. Individuals’ motivations, materialized in feelings about a subject, in the end, lead to a certain type of action. Investors` mood about future price expectations is best revealed in their free speech language when expressing their opinion about it. Models of efficient financial market evolved and added various psychological elements into account that could better explain the non-linear evolution of prices. Recent studies have shown a strong correlation between investors` mood linked to global events and the trend of the financial market. The general term “low-resource” covers a range of situations that encompass different resource conditions. It includes work related to endangered languages, as well as high-resource languages such as English in certain specialized areas and tasks. We included a short presentation on various datasets and models used in German, Spanish, French and Italian but also in Telugu, Bengali or Roman Urdu. Some models translate low resource languages into rich resource ones like English others were specially created to extract the sentiment directly from text. Applications of sentiment analysis in low resources languages that are presented in the paper refer to social media analysis, user review analysis, stock return prediction, tax fraud prediction, using chatbots for prediction of clients` emotional state, marketing tools efficiency, optimizing the price in the financial domain like insurance or banking, consumers` preferences and others. We presented some study cases in low resource languages like Turkish, Romanian, South-Slavic languages and even some European Languages.
Stavros Pantos is a PhD student in banking law and financial regulation part of the Centre for Commercial Law and Financial Regulation (CCLFR) research grouping from the School of Law of the University of Reading. Stavros’ doctoral research is focused on evaluating macroprudential measures and policy responses to Covid-19 for the European Banking sector, with research interests in financial law, risk management, fintech, climate change and sustainability. Stavros has a background in financial economics, with undergraduate studies at Lancaster University (BSc Hons Finance and Economics, MRes Finance), followed by postgraduate degrees from the University of Edinburgh (MSc Economics), University of Reading (LLM international corporate law), Copenhagen Business School (MSc cand.merc. EBA AEF) and from Durham University (MiM). Stavros is a risk management practitioner working in financial services in the UK the past 6 years, focusing on the prudential risk management, such as the Solvency II ORSA, ICAAP, stress testing and climate change scenarios.
This paper captures advances in prudential regulation and supervision for challenger banks and fintech in the UK. It presents a critical analysis of the supervisory approaches towards fintech. The aim of this analysis performed is to comment on gaps identified from existing stress and scenario tests and overall supervisory practices towards the effective prudential supervision of fintech. The focus is placed on fast-growing firms (FGFs), building on the review and findings from the analysis performed by the Prudential Regulation Authority (PRA) of the Bank of England (BoE).
Koen Meijer, University of Twente, Netherlands
Koen Meijer obtained his master’s degree with distinction in Business Administration with a specialisation in Digital Business & Analytics at the University of Twente in 2021. His thesis focused on examining the relationship between national cultures and the customer acceptance of neobanks. After finishing his studies, Koen joined Deloitte Netherlands as a Business Analyst to advise and guide companies through their complex digital transformations.
Role of culture in determining customer acceptance of neobanks
This study aims to examine the customer acceptance of neobanks and whether there are differences across national cultures. We make use of the technology acceptance model to measure customer acceptance and extend it with an additional construct, i.e. trust, because consumers are more sceptical about start-ups and digital platforms. Furthermore, we incorporate the dimensions developed by Hofstede to evaluate the national cultural effect on the modified technology acceptance model. To measure the variables of this modified technology acceptance model, we collect primary quantitative data through questionnaires, making it easier to obtain a larger sample size to include as many nationalities as possible. This model is assessed using partial least squares structural equation modelling to calculate the complex relationships with reflective constructs. Our findings indicate that the national cultural dimensions do not have a significant effect on the customer acceptance of neobanks. Furthermore, the original two independent constructs of the technology acceptance model, perceived ease of use and perceived usefulness, have a significant positive weak direct effect on the behavioural intention to use a neobank. Additionally, perceived ease of use has a significant positive strong effect on the perceived usefulness and trust. Finally, the theorised trust dimension has a significant positive weak effect on both the perceived usefulness of, and the behavioural intention to use neobanks.
Prof. Dr. Fethi Rabhi, University of New South Wales, Australia
Fethi Rabhi is a Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Australia. His main research areas are in service-oriented software engineering with a strong focus on business and industrial applications. He completed a PhD in Computer Science at the University of Sheffield in 1990 and held several academic appointments in the USA and the UK before joining UNSW in 2000. He is currently actively involved in several research projects in the area of financial software systems and big data analysis.
Towards an API marketplace for an e-invoicing ecosystem
Driven by a large number of very diverse and fast-evolving regulations, the adoption of e-invoicing is creating many challenges for solution providers, such as dealing with compliance requirements, cross-border issues, heterogeneity of standards and constant changes. Existing solutions do not represent a cost-effective and vendor-independent alternative to existing legacy systems, ERPs and databases. The proposed solution is based on leveraging cloud computing concepts, Software-as-a-Service concept, API economy, and Business Process Modelling (BPM) concepts. It allows solution providers to choose SaaS components and customise their offerings according to customer needs. Given an ecosystem of APIs available via a marketplace, it becomes possible to rapidly compose and build new applications via BPM technologies. The paper describes an implementation of this concept realised using several e[1]invoicing APIs being composed using the WASP workflow system. Some preliminary results regarding the feasibility of the proposed approach in a simple buyer-seller scenario are discussed.
Prof. Dr. Fethi Rabhi, University of New South Wales, Australia
Fethi Rabhi is a Professor in the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Australia. His main research areas are in service-oriented software engineering with a strong focus on business and industrial applications. He completed a PhD in Computer Science at the University of Sheffield in 1990 and held several academic appointments in the USA and the UK before joining UNSW in 2000. He is currently actively involved in several research projects in the area of financial software systems and big data analysis.
A data analytics architecture for the exploratory analysis of high-frequency market data
The development of cloud computing and database systems has increased the availability of high-frequency market data. An increasing number of researchers and domain experts are interested in analyzing such datasets in an ad[1]hoc manner. In spite of this, high-frequency data analysis requires a combination of domain knowledge and IT skills due to the need for data standardisation and extensive usage of computational resources. This paper proposes an architecture design for integrating data acquisition, analytics services, and visualisation to reduce the technical challenges for researchers and experts to analyze high-frequency market data. A case study demonstrates how the design can assist experts to invoke different analytics services within a consistent operational environment backed by analytics tools and resources such as a GCP’s Big Query running over a Refinitiv Tick History database and a Jupyter notebook.
Prof. Dr. Ana Ivanisevic Hernaus, University of Zagreb, Croatia
Ana Ivanisevic Hernaus is Associate Professor at the Department of Finance, Faculty of Economics and Business, University of Zagreb, Croatia. Her research interests include financial institutions and markets, sustainable finance and fintech. She was awarded 1st Prize at the international EDAMBA Thesis Competition. Her work has been published in journals such as Sustainability accounting, management and policy journal and E+M Economics and Management.
From perceived mobility to the intention to use mobile payments: A moderated mediation analysis
Mobile banking has become an important service and tool for managing personal finance in the digital era (FDIC, 2020). Despite the increasing usage, market participants still perceive the mobility value of mobile payment services differently. Their attitude towards using mobile communication technology on intelligent devices to make transactions determines the intention to use mobile payments (e.g., Himel et al., 2021; Munoz-Leiva et al., 2017). However, we are still not familiar with underlying mechanisms and boundary conditions driving such decisions. Therefore, the present study aims to provide insights about whether and under which circumstances the current customer familiarity and experience (expertise) of using mobile payments drive the intention to use electronic banking channels in the future.
Dr. Benjamin Clapham,Goethe University Frankfurt, Germany
Benjamin Clapham is a Postdoctoral Research Associate at Goethe University Frankfurt and conducts empirical research at the intersection of finance and information systems. His research focuses on regulatory and technological developments in financial markets. In particular, his research interests include market microstructure, algorithmic trading, and financial market manipulations. His work has been presented at various international conferences and has been published in leading international journals such as Journal of the Association for Information Systems (JAIS), Journal of Information Technology (JIT), Journal of Empirical Finance, Journal of Financial Research (JFR), and Financial Analysts Journal. He has been awarded with several academic awards, e.g., the dissertation award of the Frankfurt Institute for Risk Management and Regulation, the 2020 Best Paper Award of the Journal of the Association for Information Systems, and the 2021 AIS Best Information Systems Publications Award.
Give them a second chance? Prediction of recurrent financial intermediary misconduct
Financial intermediary misconduct represents a major threat for financial markets. Of particular concern is recurrent misconduct, where intermediaries harm investors for their own benefit. This not only impairs affected investors but also decreases trust and participation of investors in financial markets resulting in reduced possibilities for retirement savings and inefficiencies regarding the allocation of funds to the real economy. To solve this societal challenge, recurrent misconduct needs to be prevented. Based on a comprehensive data set, we develop predictive models to identify brokers that repeatedly commit misconduct. In line with existing theories, we show that the disciplinary history of brokers together with the linguistic style and the content of brokers’ comments to allegations provide valuable features for predictive models. Our results contribute to the literature on financial misconduct and automated fraud detection. They are valuable for investors and regulators alike assisting them to identify and prevent recurrent financial intermediary misconduct.
Jane Ngaruiya, Consultant, World Bank Group
Jane is currently a consultant with World Bank Group and independent consultant in transactions advisory. Jane has over 16 years of experience in transaction advisory-M&A, valuations, PPPs, due diligence, market and feasibility studies. Her experience is in various sectors namely: financial services, healthcare, FMCG, education, public sector.
Previously worked for: PwC, PKF, Deloitte, Bluekey Seidor, Dalberg Consulting Kenya and Coca Cola Juices
Education: PhD Candidate, Strathmore University Business School, Master of Commerce(Finance) Strathmore Business School and Bachelor of Arts, Economics, Catholic University of Eastern Africa.
Professional Qualifications: Association of Chartered Certified Accountants (ACCA)
The effect of changes in interest rate regulation on the financial performance of banks in Kenya
Purpose: The purpose of this study is to assess the effect of changes in interest rate regulation on the financial performance of banks in Kenya.
Methodology: Using a panel dataset of 78 banks in East Africa comprising 1,278 observations over the period 2004-2019, we employ difference-in-difference methodology on accounting and market value measures of financial performance. Two-step generalised method of moments, is used as the estimation technique to address the problem of endogeneity, commonly found in panel data.
Findings: The results, which are robust for endogeneity and other checks reveal that introduction of interest rate caps in Kenya significantly increased the profitability of banks. This increase can likely be attributed to increase in non-interest income and reduction in operating expenses. On the contrary, the impact on publicly listed banks was insignificant.
Implications: The study has the potential to inform policy makers in the East Africa region on the effects of interest rate regulation. High lending interest rates has seen some countries such as Kenya impose interest rate caps and subsequently repealed. Other countries such as Uganda were in the process of considering rate caps but have deferred the decision.
Originality: The study is perhaps the first to examine the effect of changes in interest rate regulation on the financial performance of countries in the East African region. The authors also employ difference-in-difference methodology and two-step generalised method of moments estimation in the study which is different from previous studies.
Key words: Interest rate regulation, bank performance, Tobin Q, difference-in-differences, Kenya, East Africa
Prof. Dr. Ekaterina Svetlova, University of Twente, Netherlands
Ekaterina Svetlova is Associate Professor in Accounting and Finance at the University of Twente in the Netherlands. Previously, she held positions as a researcher and a lecturer at the University of Leicester (UK), University of Constance (Germany), Zeppelin University (Germany) and University of Basel (Switzerland). She also gained practical experience as a portfolio manager and financial analyst in Frankfurt/Main, Germany. Her interdisciplinary research focuses on financial models and valuation studies as well as on risk management and risk reporting by firms and local and central governments. Most recently, she became interested in ethics of AI.
AI ethics and systemic risks in finance
The paper suggests that AI ethics should pay attention to morally relevant systemic effects of AI use. It draws the attention of ethicists and practitioners to systemic risks that have been neglected so far in professional AI-related codes of conduct,
industrial standards and ethical discussions more generally. The paper uses the financial industry as an example to ask: how can AI-enhanced systemic risks be ethically accounted for? Which specific issues does AI use raise for ethics that takes
systemic effects into account? The paper (1) relates the literature about AI ethics to the ethics of systemic risks to clarify the moral relevance of AI use with respect to the imposition of systemic risks, (2) proposes a theoretical framework based on
the ethics of complexity and (3) applies this framework to discuss implications for AI ethics concerned with AI-enhanced systemic risks.
Lect. Dr. Ioana Florina Coita, University of Oradea, Romania
Ioana Florina Coita is a researcher in taxation, behavioral finance, AI in finance at the Department of Accounting and Finance. She holds a PhD focusing on quantifying financial risk in companies¢investments using innovative techniques like agent-based modelling and non-linear dynamics from the University of Babes-Bolyai, Cluj-Napoca. She specialized in Law and since then started extending her research on interdisciplinary aspects of finance and law, like studying financial criminal behavior, tax evasion, crypto frauds. She is the founder of a tax research business focusing on transfer pricing and also activates in the Fintech field. She is currently working on research dealing with applications of AI in predicting behavior on financial frauds, cybercrime, tax evasion.
Evaluating sustainability goals at European level from a spatial perspective using taxpayers` perception and sentiment analysis
EU’s climate and energy targets for 2030 and the objectives of the European” Green Deal” oriented business and government investments towards sustainable projects. In order to evaluate the level of commitment inside Member States regarding accomplishment of EU’s sustainability objectives, we applied a questionnaire to people all around European Countries evaluating taxpayers` perception regarding sustainability aspects on public institutions and measures, their personal values regarding fair principles like transparency, clearer laws, predictability, efficiency in public spending, public services quality and social welfare creation. We then correlated responses with statistics at European level regarding the achievement of the EU Environmental statistics and EU Sustainable Development Goals (SDG) respectively Goal 16 promoting peaceful and inclusive societies based on respect for human rights, rule of law and good governance, transparent, effective and accountable institutions, non-discriminatory laws and policies, combat corruption, prevent violence, terrorism and crime. The result consisted in building an Index of Sustainability in Taxation for the assessment of interdependencies, factors and spatial influences between variables in order to build a map at EU level using various machine learning and spatial econometrics tools. We want to evaluate the degree of contagion and diffusion between countries using sentiment analysis for correlating the Index with social emotion from opinions regarding sustainable taxation across various cultures.
Stefana Belbe, BBU Cluj-Napoca, Romania
Stefana currently a PhD student in the Department of Mathematics-Forecasts-Statistics in the Faculty of Economics and Business Administration, BBU Cluj-Napoca, Romania, with focus on Space-Time Predictive Econometrics. She is also working as a senior data scientist in an international company dedicated to IT services, Endava , where her focus is on the development of software based on advanced analytic techniques and machine-learning models. She is a collaborating member of the Interdisciplinary Data Science Center, in BBU. Stefana has graduated from the Masters in Complex Data Analysis with a study on the Spatial Determinants of the Covid-19 Vaccination Rate in Romania and from the joint Masters in Geospatial Technologies, where she implemented an interoperable web-based platform for geospatial data for the students of the University of Muenster, Germany. Through her career and studies, Stefana has gained hands-on experience in NLP applications, software development, geospatial data analysis and visualization, (web)GIS, and predictive non-spatial, spatial and space-time modelling using Machine Learning, Spatial Statistics and Econometric tools, recently gaining insight into reinforcement learning.”
Evaluating sustainability goals at European level from a spatial perspective using taxpayers` perception and sentiment analysis
EU’s climate and energy targets for 2030 and the objectives of the European” Green Deal” oriented business and government investments towards sustainable projects. In order to evaluate the level of commitment inside Member States regarding accomplishment of EU’s sustainability objectives, we applied a questionnaire to people all around European Countries evaluating taxpayers` perception regarding sustainability aspects on public institutions and measures, their personal values regarding fair principles like transparency, clearer laws, predictability, efficiency in public spending, public services quality and social welfare creation. We then correlated responses with statistics at European level regarding the achievement of the EU Environmental statistics and EU Sustainable Development Goals (SDG) respectively Goal 16 promoting peaceful and inclusive societies based on respect for human rights, rule of law and good governance, transparent, effective and accountable institutions, non-discriminatory laws and policies, combat corruption, prevent violence, terrorism and crime. The result consisted in building an Index of Sustainability in Taxation for the assessment of interdependencies, factors and spatial influences between variables in order to build a map at EU level using various machine learning and spatial econometrics tools. We want to evaluate the degree of contagion and diffusion between countries using sentiment analysis for correlating the Index with social emotion from opinions regarding sustainable taxation across various cultures.
Nicole Koenigstein, impactVise, Switzerland
Nicole Koenigstein is a Data Scientist & Quant and Data Engineer currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and Technology and Development Lead at quantmate.ai, an innovative FinTech startup that leverages alternative data as part of its predictive modeling strategy. She is the author of Mathematics for Machine Learning with NLP and Python.
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment
The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the αt-RIM (recurrent independent mechanism). This architecture makes use of key-value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the αt-RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S&P 500 universe as well as their news sentiment score. The results suggest that the αt-RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of the-art networks such as long short-term memory models.
Ivo Lemken, Leiden University, Netherlands
Ivo Lemken is a student of the Media Technology MSc programme at Leiden University with a background in musicology and computer science. During his studies he has specialized in the use of Artificial Intelligence in creative contexts and the use of computer models as a means to study the cultural evolution of music. His work is characterized by a generalist approach to a broad range of topics as is characterized by his master thesis Stories of a Real Man: A Hybrid Autoethnography on my Maleness and Masculinity.
Jan Pauls, University of Muenster, Germany
Jan Pauls studies Information Systems at the University of Muenster with a background in Computer Science and Economics. His study focuses on Data Science and using Machine Learning to detect patterns in image data. His master thesis “Generating High-Resolution Height Maps Using Deep Neural Networks” deals with creating predictions for forest height based on satellite data to support biomass calculations.
Realising Value from AI-Enabled Decision Systems with Fair Outcomes: An ExplorativeCase Study
Fairness is a crucial concept in the context of artificial intelligence ethics and policy. It is an incremental component in existing ethical principle frameworks, especially for algorithm-enabled decisionsystems. Yet, translating fairness principles into context specific practices can be undermined by multiple unintended organisational risks.The paper presented by authors Ivo Lemken and Jan Paul argues that there is a gap between the potential and actual
realized value of AI. Therefore, their research attempts to answer howorganisations can mitigate AI risks that relate to unfair decision outcomes. They take a holistic view by analyzing the challenges throughout a typical AI product life cycle while focusing on the critical questionof how rather broadly defined fairness principles may be translated intoday-to-day practical solutions at the organizational level. The paper presented reportsan exploratory case study of a social impact microfinance organizationthat is using AI-enabled credit scoring to support the screening processto particularly financially marginalized entrepreneurs. The authors high-light the importance of considering the strategic role of the organisationwhen developing and evaluating fair algorithm- enabled decision systems.Overall, the proposed framework and the results of their study can beused to inspire the right questions that suit the context an organisationis situated in when implementing fair AI.
Dr. Jascha-Alexander Koch, Goethe University Frankfurt, Germany
Jascha-Alexander Koch is a postdoctoral research associate at Goethe University Frankfurt, Germany and a research fellow at the efl – the Data Science Institute. He engages in research studies on digital platforms and user behavior. His research focuses especially on topics around Digital Finance and FinTech.
Prof. Dr. Peter Gomber, Goethe University Frankfurt, Germany
Peter Gomber holds the Chair of e-Finance at the Goethe University Frankfurt, Germany and serves as Co-Chairman and board member of efl – the Data Science Institute. He is a member of the Exchange Council, Frankfurt Stock Exchange, and a member of the Supervisory Board of Clearstream Banking.
A Framework to Measure Corporate Regulatory Exposure
The amendment of existing and the passing of new regulations keep the corpus of regulation changing and growing dynamically. Against this background, companies face increasing costs to comply with existing and upcoming regulation. However, the high amount of regulatory texts makes it difficult for companies to identify which regulations apply to them. While regulatory technology, so-called RegTech, enables companies to comply with regulatory requirements or serves supervisory authorities to check compliance, there are no tools that enable companies to efficiently determine the relevance of a regulation in an automated manner. Therefore, this paper develops a decision support framework that makes use of techniques from natural language processing. We apply our approach to the Code of Federal Regulations in the U.S and discuss the results. As a key practical implication, our framework enables companies to retrieve regulations that speak to their business activities and may require compliance actions.