Abstract: Artificial intelligence in the financial industry is a rapidly growing trend, and the alternative asset management industry is no exception. This paper studied the key success factors for AI adoption in alternative investment firms. The author, who recently joined
the alternative investment industry, was able to gather insights from his network. This paper seeks to explore the opportunities and risks associated with employing AI in alternative asset management as well as the challenges related to automating manual tasks across front, middle, and back offices, considering the impact of automation implementation on employee roles and responsibilities. A literature review on the application of AI in finance, specifically in hedge funds, as well as the barriers of AI adoption in organizations is presented. A research survey resulted in 103 responses from individuals working in the industry, enabling us to draw conclusions on formulated hypotheses, supported by statistical analysis.
Thesis Bran van Wingerden
Abstract:
The advancement of Artificial Intelligence (AI) technologies increased significantly in the last few years. Moreover, the application of AI models expanded to a broader range. Hence, auditors are progressively encountering AI like systems, models and algorithms during audit and assurance projects. The growing scientific domains of eXplainable AI (XAI) and Responsible AI raise concerns around the transparency, explainability, and other ethicalities. These concerns, in combination with upcoming legislation, demand audit statements on reliability, integrity, and other aspects of AI models. Where auditing is a formal well-established practice, AI auditing is a novel practice. This research includes literature research, exploration of AI audit cases, and interviews with AI experts in order to discover relevant methods and specificalities of AI audits. Through the methodology of design science, a first formalised AI Audit Process is developed and proposed in order to provide AI auditors with a flexible reference frame to conduct customised AI audits. This research is a step towards the advancement of an AI auditing method and offers valuable insights for science and practice.
Thesis Väinö Saarinen
Abstract:
The amount of data in the world is constantly increasing, making data management more complex and demanding. Effective data utilization is crucial for in-depth analysis, logical reasoning, and decision-making processes. Data labelling is an essential part of this process, but it has traditionally been labour-intensive and resource-consuming. To manage always scarce resources more efficiently, companies are turning to data labelling tools to automate the process, enhance data management, and extract more value from their data.
This thesis aims to reason the benefits and risks associated with implementing a data labelling tool, specifically Microsoft Purview. The study employs a benefit measurement model and includes a pilot project conducted in a case company. Additionally, interviews with company professionals were conducted to provide further validation and professional insights into the benefits of data labelling.
The findings reveal several notable benefits of data labelling and data labelling tools. Firstly, labelling tools improve the quality and understanding of the data in hand, enhancing its utility. Secondly, automated labelling tools significantly accelerate the labelling process, reducing resource consumption compared to manual methods. Thirdly, data labelling offers broad advantages in data management, data governance, data loss prevention, data security and compliance management and data lifecycle management. Risks related to data labelling tool implementation includes accuracy of labelling, user adoption and engagement and beneficial resource allocation.
Thesis Irene Manetti
Abstract:
The financial audit (FA) process, traditionally based on manual procedures and reliant on professional judgment, faces challenges in the era of digitalization, due to the requirement of analyzing large volumes of complex data. This thesis investigates how deep learning (DL) can address challenges in the FA process, particularly focusing on large data volumes, manual procedures, and the subjectivity of professional judgment. Using the Task-Technology Fit (TTF) theory as a guiding framework, the study explores DL’s potential through a comprehensive research approach.
Through 13 EY expert interviews across various global locations, and a qualitative survey, the research identifies key challenges in current FA practices, and shows a fit with DL applications. DL shows promise in addressing these issues by automating tasks, managing data complexity and large data volumes, and providing auditors with data-driven recommendation.
Findings reveal that DL’s capabilities in natural language processing (NLP), computer vision, anomaly detection, recommendation systems, and big data analytics can address the identified FA challenges. Additionally, DL models are suggested for alleviating each challenge.
This study not only validates existing DL applications, but also introduces up to date FA
challenges. This thesis provides a solid foundation for future research and practical applications in the field of financial auditing. The implications of these findings suggest that adopting DL can lead to more efficient and accurate FA processes.
Thesis Eemil Häikiö
Abstract:
AI-powered dashboards are business intelligence tools that collect and present data in a comprehendible manner to enhance decision-making. The integration of artificial intelligence (AI) in data-driven decision-making (DDDM) processes is continuously increasing in modern organizations, however, current research examining AI-powered dashboards does not address the deployment of the technology. To complement prior research, this thesis aims to provide a foundation for the implementation of AI-powered dashboards by investigating the current dashboard best practices, as well as the adoption and governance of AI-powered dashboards in the context of executive decision-making. The concepts of DDDM and AI function as a foundation regarding the purpose and functionality of AI-powered dashboards, whereas IT governance provides a basis for their adoption and governance. The research methodology encompasses a review of scientific literature addressing these concepts to enable the proposal of two distinct theoretical frameworks addressing the adoption and governance of AI-powered dashboards.
The results of this study highlight three critical areas regarding AI-powered dashboards: current best practices, adoption processes, and governance. Best practices show that AI-enabled dashboards are dynamic and versatile BI tools that enhance decision-making and operational efficiency through detailed visualizations and data analysis. Concerning adoption, the study emphasizes the importance of selecting a suitable framework tailored to organizational needs, suggesting that a combination of existing models might often be necessary to integrate AI and BI within organizational environments effectively. Results addressing the governance of AI-powered dashboards emphasize the importance of BI and AI governance. Although the development of AI governance frameworks is still in its early stages compared to BI and IT governance, the findings suggest that adopting flexible and diverse governance structures enables organizations to manage the security, transparency, and accuracy risks associated with AI technologies.
Thesis Ronán Aardenburg
Abstract:
In recent years, passwordless authentication has become an increasingly popular topic in the cybersecurity industry. An increase in the amount of cyberattacks has warranted a need for better authentication methods, and passwordless is perfectly suited for this issue. As one of the first companies in the world, Accenture has implemented passwordless authentication at a large scale.
There is a lack of existing research into implementing authentication systems, especially into passwordless authentication. This thesis investigates the implementation of passwordless authentication at Accenture, through interviews with implementation team members, IT service desk members, and ‘regular’ employees, several key lessons for future implementations have been identified.
The study has concluded that clear and concise communication is a key factor in an implementation, as end-users are prone to misunderstanding or ignoring important communication. Furthermore, the IT service desk must be involved as a main stakeholder in an implementation, as they have a heavy burden to support the implementation. To address the gaps in existing research, future studies should focus on the IT service desk and conduct additional case studies to increase the knowledge on passwordless authentication.
Thesis Rens van Eggelen
Abstract:
Business Intelligence (BI) is commonly used to get value from data. It does have several limitations, though: power users serving business users is a severe bottle neck. A new approach has recently emerged to solve this bottleneck and to make business users independent: Self-Service Business Intelligence (SSBI).
Research on SSBI is slowly emerging, but the adoption is still rather slow. There are several challenges to overcome during SSBI implementations, but all research focuses on the perspectives of the organization adopting SSBI and their employees. Consultants often play a large role in these implementations, but their challenges and strategies for overcoming them is yet to be researched.
To research this, a case study design is used with the single case of consultants at Deloitte, a global leader in Data & Analytics service provision, implementing SSBI. Interviews were conducted with consultants from Deloitte Switzerland and Deloitte the Netherlands. These were then analysed using Thematic Analysis. As a result, eight categories containing a total of 23 challenges and four categories containing a total of eight strategies to overcome those challenges were identified. These were discussed with existing literature and classified as to being specific to SSBI implementations, specific to broader information systems (IS) implementations, and general consulting.
The results found that consultants do not only face SSBI-specific challenges, but also IS and consulting challenges. Furthermore, they do not only use SSBI strategies, but also strategies from IS implementations and general consulting. Although based on past observations, knowing about these challenges and strategies can help increase the success rate of SSBI implementations, as well as increase the adoption in the future.
As such, the thesis introduced a new unit of analysis to the literature of SSBI
implementations. As SSBI consultants face similar challenges and use similar strategies as other consultants, this research does not only shine light on the complexity of SSBI implementations, but also possibly enriches BI implementations and more general IS implementations. This does require future research to validate the findings.
Thesis Alina Verneret
Abstract:
This research focuses on assessing the potential benefits related to implementing a text mining tool, inside the processes handling customer feedback analysis. This study will adopt both the perspectives of customers, and business, related to the processes of customer satisfaction reviews and analysis.
The study has confirmed the following artifacts:
1. Text mining can leverage the use of customer unstructured text data.
2. Text mining can help optimizing some internal processes.
3. Co-creation of value can create new sources of knowledge flows across
the organization and enhance the customer experience.
Text mining has the potential to subsequently optimize both the processes, and the customer experience as a whole.
Apply now for IMMIT 2022-2024
You can still apply for IMMIT! However be quick to complete the application procedure and to guarantee an admission interview this month.
With regards,
Dr. Emiel Caron
IMMIT cohort 15 & ITEM meeting
Last April Teachers visited the 15th cohort of students that attended the IMMIT program