KI-Readiness – Entwicklung eines KI Readiness Modells für Banken

Abstract

Artificial intelligence is the topic of the moment. More and more companies and software manufacturers are talking about AI. Since the release of ChatGPT at the latest, the term AI has been used excessively. It is currently considered certain that artificial intelligence has the potential to change the economy. But what companies need to do to benefit from this change is not well known. It is essential to assess your own company in terms of its AI capabilities. The banking sector in particular is an industry in which the development of AI will pose a major challenge. In order to overcome this challenge, a tool was developed in this thesis that enables banks to measure their own AI capabilities and derive improvements on the basis of this measurement. In order to achieve this goal, a literature review was first conducted with the aim of providing an insight into the financial industry and the banking sector. It became apparent that the industry is undergoing change, which is characterised by a decline in the number of branches and staff. In addition to these declines, there is also additional pressure from regulation and FinTechs. Furthermore, the literature was used to show how AI works fundamentally, what forms of AI there are and in which areas it is already being used in banks today. It became clear that these are currently only individual projects and that AI is not yet being used extensively. Existing models for measuring AI capability were also identified. These models were discussed in terms of their ability to be applied and, together with the other parts from the literature, form the basis for the development of a model that is adapted to the banking sector. In the practical part of the work, a model was developed based on the literature research, which builds on existing models and takes into account the specifics of the banking sector. This resulted in a questionnaire containing various categories in which the AI capability of banks can be measured. These categories are data, technology, technical and business skills as well as resources, change management, risk appetite and collaboration. This model was applied in a bank and the results analysed. A focus group was then used to discuss the validity of the model's results for the analysed bank. In addition to the validation, the focus group derived possible fields of action for improving the AI capability in the analysed bank. It was determined that the model developed was suitable for the task. In addition, the fields of action of culture, knowledge, organisational framework conditions, strategy/risk, cooperation and the commitment to AI were identified.

Date of Award	2024
Original language	German (Austria)
Awarding Institution	Johannes Kepler University Linz
Supervisor	Patrick Brandtner (Supervisor)