The financial services industry is at the core of modern economies. Today, it is undergoing a deep digital transformation, due to advances in radical technologies, and the advent of the open banking directive. However, most of the digitalization efforts have focused on the consumer and large enterprises segments. This has been at the expense of the small and medium size enterprises segments (SME), despite, their vital societal and economic importance.
This research project focuses on studying the potential of disruptive technologies and open banking for solving SME’s number one growth barrier, that is working capital management. The project specifically investigates working capital financing, and cross-border working capital movement.
Regarding the SME financing gap, Supply Chain Finance solutions are the most viable option. However, their current implementation uses incomplete credit risk management models, which are either supplier centric or buyer centric and thus still unfairly penalize many SMEs. An integrated SCF credit risk management needs to consider the full supply chain network. This requires access to full performance data about the whole supply chain network, as well as advanced machine-learning techniques that can process large dimensional and low sample data sets, have a strong predictive power and require less assumptions and human effort.
Furthermore, even when financing become available to SMEs, they still have to deal with their movement through an inefficient foreign exchange infrastructure. Today, a surge of alternative Blockchain foreign exchange solutions are being proposed and deployed by different vendors. However, their adoption rate is very low. This is mostly due to the lack of understanding of production-level performance metrics and trade-offs, which require transfer data of real transactions to evaluate them.
The project plans to achieve its main goal through three complementary work packages. First, it implements an integrated supply chain finance risk management model. Its algorithm uses machine-learning technique. Its input is augmented by a novel supply chain index developed from PSD2 data. Its output accuracy is evaluated against existing financing models. Second, the project assesses quantitatively the performance metrics of deployed Blockchain-based foreign exchange solutions and maps it to SME requirements. Finally, the project studies investigate how new Fintech players and banks can systematically choose the right digital business model, which falls somewhere along the spectrum of full-competition/full collaboration
The structure and contributions of this project reflect the interdisciplinary nature of the problem at hand.
1. Introduction and Background
The financial sector plays a critical role in sustaining the well-functioning of modern economies (Baily & Elliott, 2013). At the same time, it is considered one of the industries that are most prone to disruption by digitalization. This is because financial services, much like publishing, are made of information rather than concrete goods (Tasca, Pelizzon, & Perony, 2016).
As a matter of fact, we have been witnessing an increasing number of successful service providers that use innovative technologies to disrupt traditional financial services. These financial technology players (FinTech) “build and execute specific parts of the banking value chain better, cheaper, and faster than what banks currently offer” (Innopay, 2015). Example of these technologies are, machine learning and distributed ledger technologies such as Blockchain. The digitalization of the financial services will be accelerated with the advent of the PSD2 directive, which will unleash a wealth of customer data that was sitting in bank silos (European Central Bank, 2017).
Unfortunately, most of the digitalization efforts in financial services have been focused around the consumer and large enterprises segments. Small and Medium Size enterprises have been overlooked, despite their vital societal and economic role. As a matter of fact, 9 out of every 10 businesses is an SME. Together, they employ 60% of the world’s workforce, and contribute 30% to its global GDP. Furthermore, SMEs indirectly and more subtly contribute to the stability of the world’s economy, as they are an integral part of supply chains, which are driven by large incumbents. If SMEs are weak and fail to deliver their goods and services, the entire increasingly interconnected supply chain suffers. Incumbents and economies which understand this fact and use it to their advantage, do thrive. Example of this are Ikea and Apple that consider the stability and healthiness of their supply chain partners as a necessity for achieving a healthy and competitive supply chain.
Unfortunately, instead of being a growth driver, financial services provision has been highlighted as one of the major reasons why SMEs fail to scale and grow. At the same time, the ecosystem of financial services is currently undergoing a deep disruption, due to the following reasons:
· The development of robust machine learning techniques that can perform more accurate prediction credit risk management models.
· The invention of Blockchain distributed ledger technology, and its deployment at a production level.
· The advent of the PSD2 which will unlock a wealth of data that was sitting within banks’ silos.
Therefore, this project aims to study the potential of disruptive technologies and open banking for solving SME’s number one growth barrier, that is working capital management, WCM. The project specifically investigates working capital financing, thereafter, referred to simply as short-term financing or financing, and cross-border working capital movement, thereafter, simply referred to as foreign exchange. The project takes a holistic multidisciplinary approach, where both technical and business model aspects are studied.
The importance of efficient access to short term financing for SMEs cannot be overstated. Not being able to seize growth opportunities or invest in innovative projects negatively affects SMEs competitiveness and revenue. This ultimately leads to limitations of their growth and potential of venture bankruptcy. SMEs’ short-term financing becomes even more important when discussed in the context of international trade, where it would be referred to as trade finance. Addis Agenda recognizes it as an explicit enabler of the implementation of the SDGs, and estimates show that if SME foreign exchange trade increases by ten per cent, global trade would grow by one per cent. However, the International Financial Corporation (IFC) reports an SME financing gap of more than $2b in emerging markets alone.
Because traditional trade finance solutions, which are centred around letters of guarantees, are facing stricter compliance requirements and the introduction of new capital rules, there is an increasing pressure to find more innovative ways for accessing working capital.
Supply Chain Finance, SCF, solutions achieve this by unlocking the trapped working capital inside supply chains. However, Buyers and suppliers have competing financial interests. The supplier wants to be paid as early as possible and the buyer wants to pay as late as possible. Supply chain finance has bridged these conflicting interests, providing a range of financing and risk mitigation solutions designed to optimise working capital and liquidity in domestic and international supply chains. The intuition behind SCF is the strength and health of the supply chain should be reflected on how the SME’s score is measured.
SCF is a theoretical conceptualization which has been implemented under different ways. The main difference between different implementations is how the credit risk is modelled. The following are the two current widely deployed SCF implementations
Supplier-Receivables Centric: Once a supplier invoices a buyer, it has a receivable. The receivable can then be sold to a receivables finance service provider, who buys the receivable at a discount to face value in return for taking on the risk that full payment for the services or goods will not be made. There are varieties of receivables financing – such as invoice discounting, factoring and forfaiting – but all adhere to this basic principle. The risk model used in these SCF solutions give most of the weight to financial credit of the SME that is selling the invoice. This can lead to models which unfairly penalize SMEs.
Buyer‐focused payables solutions: The Buyers also have an interest in the financial standing of their suppliers. If suppliers face unreliable or slow payments from their customers and have only expensive and uncertain financing options at their disposal, the cost of the services and goods they provide may be higher than otherwise required. Therefore, the buyer is the party which triggers the SCF solution in this category. The risk model used in these SCF solutions give most of the weight to financial credit of the incumbent firm. This can lead to models which over-represent the incumbent’s score and squeeze out the bank’s profit margins. The most popular implementation of this category is reverse factoring.
Both the currently deployed SCF credit risk management models fall short of capturing the true vision of SCF. Their risk management models have a limited vision, which only considers the financial scores of the SME and/or the incumbent. This surely does not capture the strength of a supply chain, as different metrics pertaining to the all the supply chain partners need to be considered. Indeed, the core advantage of a true integrated SCF solution is the ability to separate credit risk, something that banks are quite familiar with, from business risk, something many traditional banks have very limited understanding of (Zhang, 2015). To be able to do so, financiers need to differentiate in the way they can assume risk – either credit risk (financial related), or business risks (supply chain related).
The reasons behind such a situation are manifold. First, a good performance of traditional trade financing and traditional loans’ solutions, which did not provide enough motivation to further mature and advance SCF. Second, the absence of the needed data sets regarding all the supply chain partners. Third, the absence of accurate machine learning techniques that can learn from data without relying on standard programming practices, process large dimensional and low sample data sets, have a strong predictive power and requires less assumptions and human effort than statistical modelling.
As all these have changed, a disruption of SCF in sight and further research needs to be conducted on the matter.
1.4 Foreign exchange
When short-term financing is accessed and working capital is at hand, SMEs can then move funds in order to pay their receipts and run their payroll. Within an increasingly globalized and distributed economy, SMEs often find themselves in need of performing foreign exchange. The requirements of modern trade mean that foreign exchange needs to be efficient, affordable, dependable, and traceable.
For as long as banking has been conceived, banks have been acting as a trusted third party through which payments are settled. The current infrastructure underlying current inter-bank foreign exchange has long been criticized for being slow, expensive and intractable, and banks have been criticized as the bottleneck within the system. Given its very nature, foreign exchange can go over multiple intermediaries before reaching the end beneficiary. Furthermore, this space is heavily regulated and backed by a closed infrastructure. Therefore, the foreign exchange ecosystem is expensive, slow, lacks traceability and real time visibility, is prone to inconsistencies and is characterized by a non-fixation of exchange rate until arrival of funds. The primary infrastructure for settling today’s foreign exchange is the SWIFT banking network.
For large multinationals, mitigating the deficiencies of foreign exchange and paying suppliers internationally is still a manageable problem. Large companies typically have international offices with localized treasury functions. They create local bank accounts to hold capital in different currencies to pay suppliers and employees in local countries. They can also have risk management programs and sophisticated foreign exchange arbitrage. That makes sense when a firm is doing a large volume of trade in a country or region. However, such measures are simply not at reach for SMEs.
While many practitioners have long tried to disintermediate banks and perform Peer2Peer distributed payments, the technology to achieve it was simply not available. That is until recently, when Blockchain was invented. Blockchain has finally given us the possibility perform digital banking transaction in a trustless manner, which does not require the use of banks as a trusted third party.
Blockchain has been disruptive to the foreign exchange ecosystem, to the point that it has generated a response from virtually everyone in the industry. On one hand, traditional banks have started accelerating the improvement of their traditional infrastructure. The most noteworthy effort in this space is SWIFT GPI. On the other hand, a very large number of vendors are putting forward distributed blockchain based solutions, that can do without banks. Such vendors are Ripple, IBM, Microsoft, Hyperledger and VISA.
Foreign Exchange Research Gap
As any radical innovation, there is a lot of hype surrounding Blockchain technology for foreign exchange transactions. Different reports and white papers claim the supremacy of different Blockchain solutions. And as a whole, Blockchain is also claiming supremacy over improved traditional foreign exchange infrastructures. Furthermore, many of these performance claims were made as part of a prototype version of the product, which has not stood the time of test and real world at the time of claim making. Finally, the metrics of evaluating different solutions are numerous, such as cost, speed, privacy and integrity, are not always referred to and measured within different studies.
Today, several of the above discussed infrastructure have advanced from a prototype stage to a production stage and have been deployed long enough for us to be able to objectively capture their performance. Such measurements are particularly important for the SME segment, where requirements and margins are specific.
1.5 Fintech Business Models
The financial services industry looks radically different today than it did 10 years ago. We have new entrants, Fintech start-ups and incumbents, which have long been pushing the edge of radical technology use cases, and this is only expected to accelerate thanks to PSD2. Regardless of how the SME financing credit score will be modelled or which foreign exchange infrastructure is best, the fact and matter is that financial services of tomorrow will be delivered differently, and there will be winners and losers.
New Fintech players and traditional financial institutions both operate within the same space and would need to figure out which business model works best for them going forward. These can fall anywhere on the spectrum of pure competition or pure collaboration.
For instance, some Fintech prefer to run platforms where they can focus on the technology and collaborate with banks for the financing part, while others prefer to completely disintermediate banks. Similarly, bank’s reaction might vary between ones which prefer to compete with Fintechs on every product, ones which embrace them and look into maximizing partnerships with relevant ones, and finally banks which take a no-react strategy.
Understanding the factors which can assist players make a decision about the right business model to adopt within the SME financing and foreign exchange spaces is crucial, given the significance of working capital management for SME, and for societies at large.
1.6 Project aim
The main aim of this project is to study how radical digital technologies can drive better SME working capital management solutions in a post PSD2 era. This will be achieved by focusing on the three following research questions, where each corresponds to a research paper, as outlined in section 3 of this proposal:
· How can machine learning help financial service providers develop and integrated Supply chain finance risk management model, which capture an integrated global supply chain index into its input?
· How can we quantitatively compare the performance of different Blockchain-based foreign exchange solutions against each other, and against other traditional centralized solutions, across performance metrics that are relevant to SMEs?
· For both SME financing and foreign exchange, how can new Fintech players and traditional financial institutions choose the right business models moving forward, across the spectrum of compete/collaborate?
This research project is planned for delivery at NTNU faculty of Economics and Management with Professor Per Bjarte Solibakke as main supervisor and Professor Heidi Carin Dreyer and Associate Professor Robin Trulssen Bye as assistant supervisors.
2 Literature review and Related Background
· Machine learning approaches in SCF
Supply Chain Finance (SCF) is a scientific discipline at its infancy, which has emerged in Supply Chain Management (SCM) literature and gained further acknowledgement and interest by researchers mostly due to the global financial crisis of 2008 and financial turmoil the crisis entailed (eg Klapper, L.F. and Randall, D., 2011; Wuttke et al., 2013; Coulibaly et al., 2013). Nevertheless, SCF indicates a justified refocusing of research in the interconnection and relationships between supply chain management, corporate value and financial performance and away from the myopic perspective of managing solely the cost when studying financial aspects of supply chain management.
Supply chain finance is an important tool that can help small and medium enterprises to keep their business operations smooth and bring in line the financial and physical flow of their supply chain. (Pfohl and Gomm, 2009). ‘‘Supply chain finance eco-system is made-up of third-party providers who collaborate with firms to fulfill the requirements of capital for the whole SC. SCF makes a money-related win–win situation for the buyers, suppliers, and financial institutions. The goal of SCF is to boost the working capital at the supply chain level (Hofmann, 2005) by utilizing the solutions provided by financial institutions and technology providers (Lamoureux and Evans, 2011). ’’ (Georgios L. Vousinas, 2018)
According to Keebler (2002), ‘‘closer relationships among supply chain players and an integrated supply chain have been recognized as an effective way to escalate business agility and minimize cost. The values of supply chain finance rely on relationship among SC players that typically results in high visibility upon transactions processing’’. Additionally, supply chain finance often increases trust, profitability and commitment between the supply chain players (Randall and Farris, 2009).
One of the main factors that led to the development of the SCF are rapid technological development. Technology plays a critical role through better data analyses of such data to make decisions, where artificial intelligence such as machine learning (ML), can make a significant difference. One promising FinTech innovation regarding SCF is the emergence of machine learning techniques and distributed ledger technologies (eg, Kakavand et al., 2017; Camerinelli, 2016; Peters et al., 2016).
Machine learning techniques can significantly improve the prediction accuracy of SCF credit risk evaluation such as RS-Boosting, neural networks support vector machine (SVM). Zhu et al. (2017) predicted the SMEs’ credit risk in SCF by adopting six approaches and found that RS-boosting machine learning is the best approach to measure SMEs’ credit risk among six approaches for corporate lending.
Generally, the performance of machine learning is superior to the one of traditional statistical methods (West, 2000). Machine learning technique can process small scale and high dimensions data in credit risk management issues.
Lin and Liu (2005) developed a support vector machine learning model for credit risk management with an assessment index of eight financial indicators. Tang and Tan (2010) established a machine learning algorithm for credit risk management and obtained a high accuracy. (Zhang, 2017)
· Blockchain distributed ledger technology for Foreign Exchange Payment
Foreign exchange is growing at a quick rate as more goods and services are sourced globally. Most foreign exchange between banks that do not have an established financial relationship are handled via a correspondent banking relationships that requires a number of banks.
‘‘In 1973, 239 banks from 15 countries got together to enhance foreign exchange. The banks formed a cooperative utility, the Society for Worldwide Interbank Financial Telecommunication, headquartered in Belgium. SWIFT went live with its messaging services in 1977, replacing the Telex technology that was then in widespread use, and rapidly became the reliable, trusted global partner for institutions all around the world. The main components of the original services included a messaging platform, a computer system to validate and route messages, and a set of message standards. The standards were developed to allow for a common understanding of the data across linguistic and systems boundaries and to permit the seamless, automated transmission, receipt and processing of communications exchanged between users’’ (SWIFT,2018).
The SWIFT communication technology that was built on top of the traditional infrastructure of foreign exchange has been criticized with long processing fees, high processing time and little visibility ad transparency. As a response, SWIFT launched their SWIFT GPI initiative to address some of the known limitations of the SWIFT network. Banks that adopted SWIFT GPI believed that SWIFT GPI will provide more transparent fees and end-to-end payment tracking.
However, while the sender or receiver can now see how many banks are involved in a single transaction, SWIFT GPI that is built on top of the traditional foreign exchange infrastructure does not handle the main issues of foreign exchange for SMEs: Fees and Speed. Some Banks that view SWIFT GPI as a marginal solution for foreign exchange for SMEs has leveraged distributed ledger technology as a true innovative way for making foreign exchange efficient for SMEs.
Blockchain is a decentralized distributed ledger technology, its first application was inside the bitcoin cryptocurrency, which has proved its robustness in improving the foreign exchange issues has been leveraged by banks to improve the foreign exchange er payments struggles.
Blockchain is a continuously growing set of transactions grouped as a series of blocks, that are chained and secured using cryptography. While the transactions are traditionally maintained by centralized third-parties, the Blockchain ledger maintains them in a completely distributed manner (Gupta, 2017). Blockchain technology provides an immutable and transparent link between each stage of the foreign exchange process, it provides a system that is simple to track and trace. Some of the benefits that Blockchain technology has pushed banks to implement it within their ecosystem was their motivation to achieve:
· Real-time payments: As the transacting parties are connected to the same distributed ledger technology (Blockchain technology), the transactions on a distributed ledger (DLT) will be settled instantaneously. With real-time payments, SMEs can maximise the use of cash until the last possible due date while preventing the risk of late payment defaults.
· Visibility and traceability: DLT enable end-to-end delivery confirmation, which is shared with the originator. Think of it as a courier parcel, that both the receiver and the sender receiver can track until the delivery has been made.
· Full transparency: Payment charges and FX rates used across the foreign exchange journey are made visible to customers prior to initiating the payment. Improved transparency means that SMEs improve the efficiency of supplier disputes and reconciliation issues when foreign exchange not received in full are prevented.
· Certainty: foreign exchange are one hundred percent pre-validated and hence guaranteed. Distributed ledger technology provides certainty regarding the amount of money received by the beneficiary and the location.
However, up to now there is much speculation over the success of Blockchain as a technology with regards to foreign exchange as well as its return on investment.
3 Thesis Structure and Methods
3.1 Paper 1 - "Review of Enabling Technologies and Business Models of Financing and Foreign Exchange”
FinTech is a field is a multidisciplinary field that is at the intersection of technology and business. Indeed, the latest Fintech wave of innovation is being driven by a timely combination of factors: First, the maturity of radical technologies, such as machine learning and Blockchain. emergence of novel delivery business models such as Peer2Peer, crowd-financing and consolidated platforms. These changes are accelerated by the advent of the revised open banking directive PSD2.
So as to make meaningful contribution to the Fintech space, it is important to embrace its multidisciplinary aspect, and start by reviewing all its enabling factors, the business and technical ones.
Within the technical review, more in-depth attention will be given to technologies that are most relevant to the topics discussed during the rest of this research project, which are machine learning and distributed technologies such as Blockchain.
The main aim of this paper is to answer the following research questions:
1. What are the main driving technologies of SME financing and foreign exchange?
2. What are the main driving business models and regulations for SME financing and foreign exchange?
Through answering the above question, this paper is expected to provide the foundation for the rest of this research project, which focuses on building and proposing better SME financing and foreign exchange solutions. Furthermore, this paper can act as a guidance for practitioners who want to have a consolidated review of all the multi-disciplinary aspects of financing and foreign exchange, from both the technical and business perspectives.
In this paper, a secondary research will be conducted on previously published articles. Different academic databases will be used such as ‘‘Google Scholar’’, ‘‘Research Gate’’ and ‘‘NTNU University library’’. Keyword with Boolean operators “And” or “Or” as well as inclusion and exclusion criteria will be combined. The identified articles will be sorted into different areas of technologies and business models, then summarized into tables including the journal, method and main findings.
Innovation in this area is evolving at a rapid pace, which is not always reflected in academic publications in a timely manner. It is therefore important to include other industry-oriented material such as white papers.
3.2 Paper2 - A Credit Risk Management Model for Integrated SME Supply Chain Finance: using Machine Learning.
Supply Chain Finance is the most promising solution for SMEs to get access to short-term financing. It is based on the established fact that SMEs which belong to a strong End2End supply chain are themselves stronger. The true strength of a supply chain cannot be obtained from just looking at one SME or just the leading incumbent, but at the interactions of all parts of the supply chain.
Therefore, SCF credit risk management models should capture this fact, by capturing and including an index of the whole supply chain as an input. We refer to such SCF models as integrated. Unfortunately, the current deployed SCF credit risk management models fall short of this vision. They are either receivables centric such as factoring and forfaiting. Their credit risk management focuses the SME supplier’s financial data. Alternatively, there are payables/buyer centric, such as reverse factoring. Their credit risk model focuses on the incumbent’s. Such non-integrated SCF credit risk models are not accurate enough. They either unfairly penalize SMEs, or over represent the incumbent’s credit score and unfairly squeeze the revenue margins of financial providers and make them take uncalculated risks.
There are 3 main reasons why the current implementations of SCF are non-integrated. First, financial institutions did not have access to the necessary data of all the supply chain parties, as it was locked within the silos of different banks. Second, machine intelligence techniques were not mature enough to accurately work of datasets, which are both high dimensional and small in size. Third, the understanding of SCF was not advanced enough to appreciate the vital importance of a full integrated SCF credit risk model.
However, all these factors are being disrupted rapidly. The PSD2 directive will give access to all the needed financial data. Machine learning are mature and have a strong predictive power and require less human effort, and the SCF discipline in awakening to the integrated view of SCF.
The aim of this paper is to design and implement an integrated SCF credit risk management model for SME short-term financing, using machine-learning techniques. The model should satisfy the following:
· The input should include a comprehensive supply chain index.
· The model is implemented using machine learning techniques.
· The output accuracy is compared against other deployed SCF risk management models.
1. How can we quantitatively develop an index for an integrated supply chain index, to be used as an input to credit risk management models?
2. How can advances in machine learning and PSD2 data enable an integrated SCF credit risk model?
3. How does an integrated SCF credit risk model fair over traditional SCF?
This paper uses the shipbuilding industry as a use case. This is because its manufacturing core business strictly manages its upstream and downstream vendors and can give us access to the different needed data streams. The paper will focus on the Norwegian market for its relevancy and good data documentation practices. The data will be provided by Sparebanken Møre.
The types of data streams which are needed are as follows:
· Financial: short term debt, long term debt, real time transactional data, profitability, operations ability, enterprise basic quality.
· Non-financial: supply chain partnership level (relationship durability, SME status in the supply chain), the industry development (macro environment, prospects of the industry’s development).
This paper uses a multi-phase approach.
Phase 1: Secondary research is used to survey articles published the first on ways to quantify the supply chain health. Primary research might also be conducted in the form on interviews with the shipbuilding practitioners to confirm the findings from the primary research.
Phase 2: The different data streams are collected and aggregated. PCA, discrimination analysis and correlation analysis on the variables will be applied to accurately represent the model’s input. The data will be divided into a training set and a validation set.
Phase 3: Desk work will be required in this phase. The aim is to implement machine-learning model using our SCF integrated data. Finally, the perdition accuracy results will be compared with other already credit risk management models.
3.3 Paper3 – De-Hyping SME Blockchain-Based Foreign Exchange: A quantitative comparative study of Decentralized Blockchain infrastructures Vs. SWIFT GPI.
The invention of Blockchain has finally given us the long sought-after technology to perform distributed Peer2Peer digital transactions that do not require trusted third parties. Its advent has fundamentally disrupted the foreign exchange ecosystem, as it can allow the disintermediation of traditional financial institutions, as well as provide faster more traceable transactions.
As a response to such a radical technology, we are witnessing two parallel developments: One on hand, an overwhelming number of Blockchain-based systems offered by different vendors such as Ripple, IBM, Microsoft and Visa. On the other hand, banking consortiums improving their traditional Swift infrastructure without the decentralized technology. The most noteworthy project in this space is Swift GPI.
Therefore, today’s foreign exchange space is cumbersome and hyped, with each technology being praised as the ultimate by its proponents. What further complicates this space is the many metrics across which solutions can be compared, e.g.: speed, integrity, confidentiality, cost, traceability, visibility, and integration.
Today’s banks serving SMEs or SMEs themselves, find it difficult to choose which consortium/solution that best satisfies their requirements. Traditional or Blockchain distributed? And if it is distributed, which Blockchain solution is right?
This paper performs a quantitative comparative study between the different deployed foreign exchange solutions. The comparison will be performed across all the relevant performance metrics for SMEs.
To the best of our knowledge this should be the first study of its kind within the literature, as all previous published work was of qualitative nature.
1. What are the foreign exchange performance metrics that are relevant for SMEs?
2. How do different Blockchain solutions perform against traditional Swift infrastructure across the identified metrics?
Methodology and Data
This paper follows a quantitative comparative methodology.
The initial litterateur review conducted reveals that datasets about traditional banking foreign exchange is publicly. We also would like to use data from SpareBanken Møre for relevancy. Blockchain performance data will be collected from systems, which are in production and can be readily used. These are Ripple, IBM Hyperledger, the VISA consortium backed solution. Data will be processed with a python-based framework and analysed using a time series method so as to achieve a complete comparison.
3.4 Paper4 - Digital Business Models
Regardless of which technology underlies SME financing risk or its foreign exchange infrastructure, the fact and matter is that the financial services ecosystem is forever changed. Besides traditional financial institutions such as banks, we have new fintech entrants, that are driving innovation at a fast pace. Fintech can be small start-up companies, or giant ones, such as Amazon, Alibaba and Google.
Answering such questions is important for the whole ecosystem, but more so for banks which will need to hold the burden of compliance, and who do not have the same agility and technology expertise that many FinTech companies possess.
Our motivation for this project is our management that if we want PSD2 and digitalization to truly achieve its ultimate goal of better serving customers and elevating the financial sector technology innovation, then we actually need banks to be strong and thrive beyond compliance.
The delivery of financing and foreign exchange financial services of tomorrow will be governed by the business models adopted by each and all players within this space. These business models can fall somewhere across the spectrum of pure competition or pure collaboration.
While banks have had to deal historically with competition from new entrants, there are 3 aspects which are different in this new fintech wave. First, the advent of the PSD2 directive, which gives new players not only access to previous data, but also the legal legitimacy to exist and operate within the EU market. Second, the maturity of a number of radical technologies. And third, the success of new fintech delivery models that are based on the concepts of Peer2Peer, crowd and distributed transactions.
Therefore, the scale of this disruption has made it so there is little consensus among fintech players about what would be the best business model to adopt within the competition/collaboration spectrum. In Norway for example, we have seen number of banks that decided to collaborate on the VIPPS mobile payment solution instead of launching new competing products. More globally and within the financing space, we can see some fintech wanting to focus on the technology aspect while collaborating and taking benefit of banks customer base and investment. On the other hand, we can see others wanting to completely replace banks in all their aspects.
While a number of studies have been published within the topic of financing and foreign exchange in the consumer segment, there is a research gap within the SME segment, as it is characterized with more complexity and regulations. This is especially true for markets that are new to the open banking initiative, such as Norway and Europe more generally, versus the UK which already has a more experience in the field.
This paper takes the position of banks within the Scandinavian region. It aims to provide a framework that can allow for a more systemic engagement within the Fintech financing and foreign exchange ecosystem. The framework focuses on identifying the critical evaluation criteria that influence the choice of the bank’s engagement model. These can range from short-term financial gain, to displacement of competition.
· How can banks decide on the best engagement business model within the SME financing and foreign exchange sectors?
This paper adopts a qualitative methodology. It will use a primary research method where interviews are conducted with banks that are going through a digitalization project in the SME financing and foreign exchange segments. Secondary research methods will also be used to consult the literature published on previously executed Fintech/Banks engagement models.
This paper can also benefit from Participatory observation method, where I can be immersed with a banking team working on a digitalization project, so as to more objectively capture their real decision-making process.
Baily, M. N., & Elliott, D. J. (2013). The Role of Finance in the Economy: Implications for Structural Reform of the Financial Sector. Journal of Financial Regulation, Volume 3, Issue 1 (pp. 34).
Coulibaly, B., Sapriza, H. and Zlate, A. (2013), Financial frictions, trade credit, and the 2008-09 global financial crisis, International Review of Economics & Finance, Vol. 26 No. C, pp25-38.
Hofmann, E. (2005). Supply Chain Finance: Some Conceptual Insights. In Lasch, R. /Janker, C.G. (Hrsg.): Logistik Management- Innovative Logistikkonzepte, Wiesbaden S: 203-214
Innopay (2015). Unlocking opportunities in the API economy. Retrieved from https://www.innopay.com/en/publications/unlocking-opportunities-api-economy
Kakavand, H., Kost De Sevres, N., & Chilton, B. (2017). The Blockchain Revolution: An Analysis of Regulation and Technology Related to Distributed Ledger
Klapper, L.F. and Randall, D. (2011), Financial crisis and supply-chain financing, in Chauffour, J. and Malouche, M. (Eds), Trade Finance During the Great Trade Collapse, TheWorld Bank, Washington, DC, pp73-86.
Liu M, Lin DC (2005) Commercial bank credit risk assessment model based on support vector machine. J Xiamen university (natural science edition) 44(1):29–32
Mathis FJ, Cavinato J (2010) Financing the global supply chain: growing need for management action. J Thunderbird International Business Review 52(6):467–474
Pfohl HC, Gomm M (2009) Supply chain finance: optimizing financial flows in supply chains. J Logist Res 1:149–161
Randall, WS, & Farris, M (2009) Supply chain financing: using cash-to-cash variables to strengthen the supply chain. International Journal of Physical Distribution & Logistics Management, 39 (8), pp669-689.
Silvestro R, Lustrato P (2014) Integrating financial and physical supply chains: the role of banks in enabling supply chain integration. Int J Operations & Production Management 34(3):298–324
Tang JR, Tan CH (2010) Research on the listed company credit risk assessment model based on support vector machine. J Statistics and Decision 10:65–67
Tasca, Pelizzon, & Perony, (2016). Banking Beyond Banks and Money- A Guide to Banking Services in the Twenty-First Century. Springer
West D (2000) Neural Network Credit Scoring Models. J Computers & Operations Research 27(11-12):1131–1152
Wuttke, D.A., Blome, C., Foerstl, K. & Henke, M. (2013). Managing the Innovation adoption of supply chain finance. Empirical evidence from six European case studies. Journal of Business Logistics 34 (2), pp148–166.
Zhang et al. - 2015 - A credit risk assessment model based on SVM for SME in supply chain finance.