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.
BUSINESS PROCESS AUTOMATION: ENHANCING EFFICIENCY AND COMPETITIVENESS IN MODERN ORGANIZATIONS
(2024)
The first section of the thesis provides a historical overview of automation, spanning from the first industrial revolution to the current era of highly advanced AI-driven technologies. It emphasizes how important Business Process Automation (BPA) is in today's hectic corporate climate when productivity and competitiveness are key factors. The main focus is on Robotic Process Automation (RPA), which is especially useful in situations with legacy systems since it effectively automates repetitive processes. This study explores the differences between terms and concepts related to automation, including business process automation (BPA), robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and business process management (BPM). Companies looking to optimize their processes face a changing landscape due to the involvement of various technologies. Using real-world case studies and industry best practices, the thesis provides a thorough examination of the effects of BPA, emphasizing the primary drivers, challenges, and benefits of BPA adoption. A mixed-methods approach integrating quantitative and qualitative research was used as the methodology. Surveys, case studies, and documentation from different organizations are included in the study, based on those who have implemented RPA at their work. This method enables a thorough analysis of BPA's effects on efficacy, productivity, and affordability. Case studies from prominent firms like Capgemini Consulting, PwC, and Deloitte are reviewed to gain insights regarding their BPA journey. Significant gains in customer satisfaction, cost savings, error avoidance, and operational efficiency are shown by this research. They also draw attention to difficulties like opposition from employees, problems with integration, and the requirement for upskilling. The thesis indicates that although big firms have similar motives for adopting BPA, the process of adopting BPA varies depending on the specific circumstances of each firm. Stakeholder engagement and change management are critical components of successful BPA programs, according to key results. The study highlights a balanced, strategic, and context-sensitive approach, offering a useful insight for companies in establishing their BPA strategy. It provides a comprehensive examination of the role that business process automation plays in modern companies, highlighting the ways in which it can radically alter corporate strategy and operations in the digital age. It gives a thorough examination of the challenges involved in putting BPA into practice and offers tactical advice to businesses hoping to use these tools to boost productivity and competitiveness.
This thesis investigates the integration of Artificial Intelligence (AI) in the recruitment processes of Small and Medium Enterprises (SMEs), highlighting both the opportunities and challenges of such technologies. It examines the potential benefits of AI in recruitment, including increased efficiency and unbiased decision-making, as well as challenges such as data privacy concerns. Through qualitative research and interviews with HR professionals, the study not only explores the current landscape of AI in recruitment but also proposes a comprehensive implementation plan for SMEs. This plan is designed to help SMEs navigate the complexities of adopting AI technologies, ensuring they can effectively leverage AI tools to enhance their recruitment outcomes and overcome the barriers to implementation.
Industry 5.0 is a new age of production that combines human-machine cooperation with cutting-edge technologies. Despite all of Industry 5.0's advantages, there are new cybersecurity threats that need to be considered in order to maintain the stability and security of networked systems. In the first section of the thesis, Industry 5.0 is introduced, along with its benefits and features. Cyber threats posed by automation, robotics, the Internet of Things, artificial intelligence, and networked systems are also noted.
The thesis delves deeply into the vulnerabilities and possible attack paths that hackers can use in Industry 5.0 settings. It looks at vulnerabilities in IoT devices, human mistakes, unprotected communication protocols, and supply chain vulnerabilities, giving a thorough grasp of the hazards involved. Case examples from the real world illustrate how cyberattacks affect Industry 5.0 systems, highlighting the necessity of strong defenses.
In order to develop recommendations for Industry 5.0 security, the thesis examines a number of cybersecurity best practices and standards, including the NIST Cybersecurity Framework and ISO 27001. It highlights how crucial it is to carry out thorough risk assessments and apply efficient risk management strategies in Industry 5.0 settings. The thesis recommends staff awareness and training, incident response strategies, and technology controls as ways to lessen cyber dangers.
The thesis also looks at innovative ways to improve Industry 5.0 cybersecurity, including secure communication protocols, blockchain for supply chain security, artificial intelligence (AI) for attack detection and response, and safe IoT device design. It also takes into account the legal and regulatory sides of cybersecurity, evaluating how well they work to handle privacy and ethical issues as well as lessen cyber threats.
To improve Industry 5.0 cybersecurity, the thesis also looks at cutting-edge technologies including blockchain for supply chain security, AI for threat detection and response, secure communication protocols, and safe IoT device design. It evaluates the efficacy of legal and regulatory measures in mitigating cyber dangers and resolving privacy and ethical issues while taking cybersecurity into account.
The thesis highlights the need of industry cooperation and information exchange in addressing Industry 5.0 cyber threats. It assesses initiatives, partnerships, and networks that support the sharing of best practices, information, and threat intelligence. In the conclusion, the thesis explores Industry 5.0's future orientations and offers academics, policymakers, and business experts’ advice on how to proactively mitigate new risks and guarantee the security of Industry 5.0 ecosystems.
Overall, the thesis contributes to a better knowledge of Industry 5.0 cyber dangers and provides practical solutions through the use of multidisciplinary research, real-world case studies, and examples. Its purpose is to encourage the secure and successful use of Industry 5.0 technology.
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.
Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.
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.
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.
This bachelor thesis addresses the topic of digitalization in the healthcare industry and the resulting integration of Artificial Intelligence into medical care. The aim of this thesis is to develop new business model ideas for an international medical device manufacturer, enabled by the integration of a digital solution into the product portfolio. Furthermore, measures for the successful implementation of the business model ideas and positioning of the organization are to be developed.
To achieve this goal, a market research on the impact of digitalization in the healthcare industry and the resulting integration of Artificial Intelligence into medical care was conducted based on the relevant literature. In addition, the resulting opportunities and risks for the specific use case were identified.
Within the scope of this thesis, the following business model ideas were identified:
- BMI 1: Individual module-based offering,
- BMI 2: Comprehensive product and service solutions,
- BMI 3: Integrated supply and patient pathway solutions,
- BMI 4: Data platform provider.
Recommendations for successful positioning include (1) strengthening organizational structures for process orientation, (2) placing the digital solution not only as a solution for the patient pathway, but also as an enabler for ambulatory procedures, (3) expanding the digital solution with secondary process applications, (4) building a skilled workforce, and (5) partnering with technology companies to manage implementation of the platform-based business model idea.
Life insurance penetration rate in Malaysia has been stagnant in the past few years although a few InsurTech companies set up in Malaysia recently. Prior researches on InsurTech fail to clarify the gap of the target customers’ and the insurance experts’ opinions on how to enhance the customer experience in online life insurance with the help of Artificial Intelligence (AI). To address this, a model is recommended based on the literature review on similar articles and survey results conducted on both target customers and insurance experts. The recommended model has four main components: official website by InsurTech companies collaborated with traditional life insurers, customer support, customer service and customer engagement.
This thesis is a study examining the potential of implementing automation solutions in the financial month-end close of TomTom Business Unit Automotive Finance. The aim of this study is to identify processes with potential for the implementation of Robotic Process Automation and/or Artificial Intelligence, to improve month-end close in the selected case company.
The theoretical framework delimits Digital Business Transformation from Digitalization and Digitization. It provides background knowledge on Robotic Process Automation and Artificial Intelligence and points out how digitalization impacts the finance function of the future. Furthermore, factors for successful implementation of automation are discussed.
The study applies the strategy of action research performed in a two-staged research examination, including the performance of interviews and the analysis of the interview results. The interview’s goal was to examine month-end close processes, gathering information about the process itself and its characteristics, to have a solid understanding on the processes for the subsequent analysis. The data analysis was conducted applying two different approaches, varying depending on which automation tool best suited the process.
The research result showed that half of the processes in month-end close of Automotive Finance have the automation potential. This automation is more related to the implementation of processes into SAP Analytics Cloud and the use of included Artificial Intelligence features than to the use of Robotic Process Automation.
This result confirms the theoretical findings on the high potential of automation in reporting and endorses the automation potential of month-end close in TomTom Business Unit Automotive Finance.
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.
In times of talent shortage and increasing competition, companies are constantly
looking for methods to recruit better fits in a more time and cost-efficient manner. One
such method, which an increasing number of companies turn towards, are so called
“Robot Recruiters”, or more specifically, artificial intelligence enhanced digital
recruiting tools. However, the impact of the associated automation and dehumanization
of parts of the recruitment process on the candidate experience, remains unclear. In order to assess the potential influence of mentioned tools, candidate experience influencing factors are elaborated, to then analyze how these factors are affected in an artificial intelligence supported recruiting process.
The analysis has shown, that AI recruiting tools do have the potential to satisfy
candidates’ needs by automating simple, yet time consuming tasks like scheduling or initial communication. However, candidates are likely to show adverse reaction to their
usage in later stages of the recruitment process, which are traditionally characterized
by personal interaction.