Whitepaper: The use of AI is on the rise in the financial sector. Utilizing machine learning algorithms to make decisions and predictions based on the available data can be highly valuable. AI offers benefits to both financial service providers and its customers by improving service and reducing costs. Examples of AI use cases in the financial sector are: identity verification in client onboarding, transaction data analysis, fraud detection in claims management, anti-money laundering monitoring, price differentiation in car insurance, automated analysis of legal documents, and the processing of loan applications.
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This white paper is the result of a research project by Hogeschool Utrecht, Floryn, Researchable, and De Volksbank in the period November 2021-November 2022. The research project was a KIEM project1 granted by the Taskforce for Applied Research SIA. The goal of the research project was to identify the aspects that play a role in the implementation of the explainability of artificial intelligence (AI) systems in the Dutch financial sector. In this white paper, we present a checklist of the aspects that we derived from this research. The checklist contains checkpoints and related questions that need consideration to make explainability-related choices in different stages of the AI lifecycle. The goal of the checklist is to give designers and developers of AI systems a tool to ensure the AI system will give proper and meaningful explanations to each stakeholder.
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Explainable artificial intelligence (xAI) is seen as a solution to making AI systems less of a “black box”. It is essential to ensure transparency, fairness, and accountability – which are especially paramount in the financial sector. The aim of this study was a preliminary investigation of the perspectives of supervisory authorities and regulated entities regarding the application of xAI in the financial sector. Three use cases (consumer credit, credit risk, and anti-money laundering) were examined using semi-structured interviews at three banks and two supervisory authorities in the Netherlands. We found that for the investigated use cases a disparity exists between supervisory authorities and banks regarding the desired scope of explainability of AI systems. We argue that the financial sector could benefit from clear differentiation between technical AI (model) explainability requirements and explainability requirements of the broader AI system in relation to applicable laws and regulations.
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The focus of this project is on improving the resilience of hospitality Small and Medium Enterprises (SMEs) by enabling them to take advantage of digitalization tools and data analytics in particular. Hospitality SMEs play an important role in their local community but are vulnerable to shifts in demand. Due to a lack of resources (time, finance, and sometimes knowledge), they do not have sufficient access to data analytics tools that are typically available to larger organizations. The purpose of this project is therefore to develop a prototype infrastructure or ecosystem showcasing how Dutch hospitality SMEs can develop their data analytic capability in such a way that they increase their resilience to shifts in demand. The one year exploration period will be used to assess the feasibility of such an infrastructure and will address technological aspects (e.g. kind of technological platform), process aspects (e.g. prerequisites for collaboration such as confidentiality and safety of data), knowledge aspects (e.g. what knowledge of data analytics do SMEs need and through what medium), and organizational aspects (what kind of cooperation form is necessary and how should it be financed).Societal issueIn the Netherlands, hospitality SMEs such as hotels play an important role in local communities, providing employment opportunities, supporting financially or otherwise local social activities and sports teams (Panteia, 2023). Nevertheless, due to their high fixed cost / low variable business model, hospitality SMEs are vulnerable to shifts in consumer demand (Kokkinou, Mitas, et al., 2023; Koninklijke Horeca Nederland, 2023). This risk could be partially mitigated by using data analytics, to gain visibility over demand, and make data-driven decisions regarding allocation of marketing resources, pricing, procurement, etc…. However, this requires investments in technology, processes, and training that are oftentimes (financially) inaccessible to these small SMEs.Benefit for societyThe proposed study touches upon several key enabling technologies First, key enabling technology participation and co-creation lies at the center of this proposal. The premise is that regional hospitality SMEs can achieve more by combining their knowledge and resources. The proposed project therefore aims to give diverse stakeholders the means and opportunity to collaborate, learn from each other, and work together on a prototype collaboration. The proposed study thereby also contributes to developing knowledge with and for entrepreneurs and to digitalization of the tourism and hospitality sector.Collaborative partnersHZ University of Applied Sciences, Hotel Hulst, Hotel/Restaurant de Belgische Loodsensociëteit, Hotel Zilt, DM Hotels, Hotel Charley's, Juyo Analytics, Impuls Zeeland.
A-das-PK; een APK-straat voor rijhulpsystemen Uit recent onderzoek en vragen vanuit de autobranche blijkt een duidelijke behoefte naar goed onderhoud, reparatie en borging van de werking van Advanced Driver Assistance Systems (ADAS), vergelijkbaar met de reguliere APK. Een APK voor ADAS bestaat nog niet, maar de branche wil hier wel op te anticiperen en haar clientèle veilig laten rijden met de rijhulpsystemen. In 2022 worden 30 ADAS’s verplicht en zal de werking van deze systemen ook gedurende de levensduur van de auto gegarandeerd moeten worden. Disfunctioneren van ADAS, zowel in false positives als false negatives kan leiden tot gevaarlijke situaties door onverwacht rijgedrag van het voertuig. Zo kan onverwacht remmen door detectie van een niet bestaand object of op basis van verkeersborden op parallelwegen een kettingbotsing veroorzaken. Om te kijken welke gevolgen een APK heeft voor de autobranche wil A-das-PK voor autobedrijven kijken naar de benodigde apparatuur, opleiding en hard- en software voor een goed werkende APK-straat voor ADAS’s, zodat de kansrijke elementen in een vervolgonderzoek uitgewerkt kunnen worden.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
