Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

German and Israeli Innovation: The Best of Two Worlds

This study reviews – through desk research and expert interviews with Mittelstand companies, startups and ecosystem experts – the current status of the Israeli startup ecosystem and the Mittelstand region of North Rhine- Westphalia (NRW), Germany. As a case study, it highlights potential opportunities for collaboration and analyzes different engagement modes that might serve to connect the two regions. The potential synergies between the two economies are based on a high degree of complementarity. A comparison of NRW’s key verticals and Israel’s primary areas of innovation indicates that there is significant overlap in verticals, such as artificial intelligence (AI), the internet of things (IoT), sensors and cybersecurity. Israeli startups can offer speed, agility and new ideas, while German Mittelstand companies can contribute expertise in production and scaling, access to markets, capital and support. The differences between Mittelstand companies and startups are less pronounced than those between startups and big corporations. However, three current barriers to fruitful collaboration have been identified: 1) a lack of access, 2) a lack of transparency regarding relevant players in the market, and 3) a lack of the internal resources needed to select the right partners, often due to time constraints or a lack of internal expertise on this issue. To ensure that positive business opportunities ensue, Mittelstand companies and startups alike have to be proactive in their search for cooperation partners and draw on a range of existing engagement modes (e.g., events, communities, accelerators). The interviews and the research conducted for this study made clear that no single mode of engagement can address all the needs and challenges associated with German-Israeli collaboration

A Multi-Agent Simulation of Retail Management Practices

We apply Agent-Based Modeling and Simulation (ABMS) to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between human resource management practices and retail productivity. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents do offer potential for developing organizational capabilities in the future. Our multi-disciplinary research team has worked with a UK department store to collect data and capture perceptions about operations from actors within departments. Based on this case study work, we have built a simulator that we present in this paper. We then use the simulator to gather empirical evidence regarding two specific management practices: empowerment and employee development