Now showing 1 - 10 of 14
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Synthesizing Training Data with Generative Adversarial Networks: Towards the Design of a Data-Sharing Ecosystem Platform for Fraud Detection

2023-06-02 , Fabian Sven Karst , Mahei Li , Leimeister, Jan Marco

Financial fraud has a severe impact on the general population. While financial institutions have technological capabilities for fraud detection using intelligent AI systems, the delay until they have collected a sufficient size of fraudulent data to train their fraud detection models creates a costly vulnerability. One major challenge for quickly training data lies in the inability to share fraud detection training data with other financial institutions, due to data and privacy regulations. Thus, we create the concept for a data-sharing ecosystem platform that addresses data anonymity concerns by creating synthesized training data based on each institution’s fraud detection training data sets. We rely on the advantages of generative adversarial networks (GAN) to quickly construct a shared dataset for all ecosystem members. Applying design science research, this paper derives design knowledge based on financial fraud detection literature, data sharing between financial institutions, GANs and modular systems theory for the design of a plat-form architecture for data-sharing ecosystems.

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From Service Systems Engineering to Service Innovation - A Modeling Approach

2019-06-10 , Li, Mahei , Peters, Christoph

Due to the advent of digitization, service innovation has become even more important for both business and service research alike. Current service systems engineering approaches have employed a recombinant perspective that follows innovation mechanisms to leverage existing company resources for new service innovations. Employing these innovation mechanisms is still challenging, since there is little support on how to structure and identify these mechanisms. We propose a model-based service system engineering approach to structure existing resources into one formal model, enabling the formalization of service innovation mechanisms. The formalized service innovation mechanisms allow for a graphical illustration and enable future research to apply functions to analyze how innovation impacts entire or specific parts of service systems. Furthermore, the mathematical model enables an object-oriented value-driven perspective on service systems and is basis for graphical software tools. We contribute to literature by formalizing service innovations and its mechanisms in the context of service systems and by combining concepts of service innovation and service systems engineering. We do so by a) formalizing service innovation mechanisms and b) demonstrating the application of formal service innovations along one specific software implementation case. For practice, the service system model can with simulating the effects of service innovations.

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German Standardization Roadmap on Artificial Intelligence

2022 , German Institute for Standardisation (DIN e. V.) , German Commission for Electrical, Electronic & Information Technologies of DIN and VDE (DKE) , Ernestine Dickhaut , Mahei Li , Christoph Peters , many other authors (see chapter 11) , Wolfgang Prof. Dr. Wahlster , Christoph Winterhalter

On behalf of the German Federal Ministry of Economic Affairs and Climate Action, DIN and DKE started work on the second edition of the German Standardization Roadmap Artificial Intelligence in January 2022. With the broad participation and involvement of more than 570 experts from industry, science, the public sector and civil society, the strategic Roadmap for AI standardization was thus further developed. This work was coordinated and accompanied by a high-level coordination group for AI standardization and conformity The standardization roadmap implements a measure of the German government’s AI Strategy and thus makes a significant contribution to “AI – Made in Germany”. Standardization is part of the AI Strategy and is a strategic instrument for strengthening the innovation and competitiveness of the German and European economies. Not least for this reason, standardization plays a special role in the planned European legal framework for AI, the Artificial Intelligence Act. This Standardization Roadmap AI identifies the requirements in standardization, formulates concrete recommendations and thus creates the basis for initiating standardization work at national level, and especially at European and international level, at an early stage. In doing so, the Roadmap makes a significant contribution to the European Commission’s Artificial Intelligence Act, supporting its implementation. The Standardization Roadmap AI focuses on nine key topics, which are addressed in Chapter 4: → The Roadmap begins with the basic topics, such as terminologies and definitions, classifications and ethical issues. They are the basis for AI discussions and are thus the central core of the Roadmap. → The security/safety of AI systems plays a crucial role in widespread use of AI solutions. Only a more in-depth consideration of requirements for operational safety and information security, for example, can enable the comprehensive use of AI systems in business and society. → Another key topic, and the basis for the broad market success of AI, is testing and certification. This requires reliable quality criteria and reproducible test methods that can be used to verify the properties of AI systems. They are a key prerequisite for assessing the quality of AI-based applications and contribute significantly to explainability and traceability – two factors that build trust and acceptance. → Another challenge in the use of AI, especially for small and medium-sized enterprises, is the integration of AI technologies in organizations. The focus here is on sociotechnical aspects such as human-technology interaction, humane work design, and requirements for business structures and processes, which are all examined in the Roadmap. → The fields of application of AI are extremely diverse. AI technologies are used in almost all business and application areas and offer great potential. To cover a broad spectrum of applications, the Roadmap considers industry-specific challenges for the following five sectors in particular, in addition to the cross-cutting issues mentioned above: Industrial Automation, Mobility, Medicine, Financial Services and Energy / Environment. The present Roadmap outlines the work and discussion results for all nine key topics and provides a comprehensive overview of the status quo, requirements, and needs for action.

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Künstliche Intelligenz und menschliche Kompetenz zur Automatisierung und Personalisierung von Dienstleistungen am Beispiel des Support

2020 , Li, Mahei , Bronner, Esther , Peters, Christoph , Leimeister, Jan Marco , Bruhn, Manfred , Hadwich, Karsten

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Sense of Control for Empowerment in Hybrid Intelligence Service Systems

2021-12-15 , Eilers, Karen , Li, Mahei

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A service system modelling approach

2018 , Li, Mahei

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Nutzungszentrierte Gestaltung von HI-basierten Dienstleistungen am Beispiel des IT-Supports

2021-08-23 , Schmidt, Simon L. , Li, Mahei , Peters, Christoph , Leimeister, Jan Marco , Bruhn, Manfred , Hadwich, Karsten

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Take the Wheel - Technology-driven Change in the Energy Sector

2019 , Li, Mahei , Müller, Jennifer , Ernst, Sissy-Josefina , Peters, Christoph

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Einführung eines DSGVO-konformen Videokonferenzsystems in hessischen Schulen. Projektergebnisse des ZEVEDI Ad hoc-Vorhabens „Wechselkosten zum DSGVO-konformen VC-System des Schulportals Hessen“

2023 , Ernestine Dickhaut , Lasse Schaup , Mahei Li , Jan Marco Leimeister

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Gestaltungsdimensionen der Digitalisierung – Wie Dienstleistungssysteme den Wandel antreiben und welche Faktoren eine Rolle spielen

2020-10-03 , Grotherr, Christian , Li, Mahei , Schymanietz, Martin , Fritzsche, Albrecht , Semmann, Martin , Peters, Christoph , Böhmann, Tilo , Leimeister, Jan Marco , Möslein, Kathrin M.