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Resulting from the rapid technological advancement in the field of artificial intelligence and its implementation in the business world, intelligent systems are gradually adopted in recruitment. As this development is fast evolving and recent, there is comparatively little research about artificial intelligence in conjunction with recruitment. Hence, this thesis aims at exploring the effects of intelligent algorithms on the recruitment process and the biases involved.To investigate the topic, existing literature was analysed and primary research in form of expert interviews was conducted.The thesis describes the current state of implementation, effects on recruiters and bias as well as potential drawbacks. Overall, it was identified that artificial intelligence cannot prevent bias in personnel selection.The findings imply the need to further research the topic, particularly the implications of algorithmic bias.
Artificial intelligence is a disruptive technology, offering increasingly more opportunities to companies. However, the low digital maturity of the private banking sector, makes it hard for private banks to take advantage of this opportunity. Simultaneously, customers are expecting more digital solutions, forcing companies to adapt their services.
The aim of this paper is to provide an overview, drawing conclusions about whether the implementation of AI technologies is profitable in the private banking sector.
This thesis is based on recent research about current possible applications and the respective benefits, risks and costs. Two use cases will be thoroughly analysed: the application of automated credit risk management systems and AI powered indexes. In the first case, the software NOLA 2.0 will be evaluated and used as a benchmark to highlight the positive and negative aspects deriving from AI credit risk management software. In the second case, the AI powered index AiPEXAR will be presented and compared to the most common ETF S&P 500, analysing the differences in their computation and their performance over time.
The analysis concluded that, even though the benefits substantially depend on the individual company, AI chatbots, customers' engagement, credit risk management software and banking apps are advantageous for private banks. Yet, the implementation of AI powered indexes may be precocious and therefore not yet profitable. It can also be concluded that for private banks, whose core competitive advantage lies in the expertise of the relationship managers, the digitalization of advisory may lead to unsatisfied customers.
Artificial Intelligence is becoming an increasingly important part of everyday life and is considered a matter of course by many people. Since it can be assumed that artificial intelligence will play an increasingly central role in business in the future, this paper aims to investigate the intersection between AI and Digital Sales Technologies through a systematic literature review. This thesis identified 32 relevant articles through an extensive literature search in the databases Web of Science, ScienceDirect, and SpringerLink. Through the detailed analysis of these 32 articles, the following four topic clusters could be identified: “Application Layer, Social Layer, Challenges, and Futuristic Layer”. Based on these layers, the developed research questions were answered successfully, and the following conclusions were drawn: AI is already being used in Digital Sales Technologies in numerous ways, for instance through voice assistants like Alexa. In addition, various changes for consumers and salespeople were identified, that accompany the adoption of AI in Digital Sales Technologies. Furthermore, this thesis provides an answer to which challenges this integration brings and how AI will influence Digital Sales Technologies in the future. Finally, research gaps for future research are identified based on the collected findings from the literature review.
AI in recruiting is used more and more in recruiting and for the evaluation of job interviews. Research has focused mainly on companies' side of AI implementation in recruiting. However, changes in demographics also make it important to look at it from the viewpoint of candidates. This thesis aims to explain how the perception of AI-evaluated job interviews influences the intention to apply. A survey is used as a data collection method with a sample of 105 participants. The results revealed that the perception of AI-evaluated job interviews positively influences the intention to apply in terms of organizational attractiveness, while anxiety negatively influences the intention to apply. However, in general, the positive effect is stronger. Other factors such as trust, fairness, intrinsic motivation, and novelty have no significant effect on the intention to apply.
Employers must have the necessary tools to engage in the fight for talent, which is growing increasingly competitive. The rising competitiveness of the recruiting industry today has further driven the development of the recruitment process, resulting in the introduction of artificial intelligence (AI) techniques.
In this thesis, a literature review of current applications of AI in recruitment is conducted to better understand AI’s present strengths and limitations as well as its future potential.
In particular, this thesis attempts to clarify, from a recruitment strategy perspective, how AI can be used to improve recruitment and facilitate recruiters’ daily work, with a focus on which guidelines should be in place to achieve these goals.
The results reveal a significant gap between the promise and current reality of AI applications in human resources. However, with a few adjustments and cautious implementation, AI can indeed provide recruiters with promising solutions primarily by taking over tasks such as sourcing, screening and possibly even interviewing applicants through video screening. This has the potential to improve the quality of hiring and eliminate bias in recruitment. The thesis also finds that, at present, a fully automated process without any supervision from recruiters is unrealistic, at least in the final stages of the decision-making process, due to the ongoing and crucial need for a human touch and the currently foreseen negative cultural reaction to AI in its present limited form.