The purpose and objective of this thesis is to examine the degree of impact WFM is facing due to implementation of AI-based tools within the banking and finance industry. To do this the author will select and classify under the chapter ‘Literature Review’ how, in which departments, and to what degree, banks and other financial institutions have implemented AI tools within their organization. Secondly, the author will conduct interviews with executive leaders as well as with AI researchers and experts, and analyze the data received.
For this study, the author focuses on AI’s impact on WFM within the banking and finance industry. The purpose of the following questions is to refine the present knowledge gap within the banking and finance industry regarding the WFM impact of AI. The author will emphasize via a literature review and interviews exactly how AI-based technology tools have been implemented in the banking and finance industry. To do so, three research questions have been chosen and will be further analyzed throughout this study paper. The first question focuses on WFM and HR teams. It predicts how many people and what kind of qualifications will be deployed. As well as where and when they will be deployed.
There are many consequences of banks and other financial institutions implementing more AI technology. In his book, Competing in the Age of AI, Iansiti and Lakhani mention that it is critical for leaders to understand the choice of model along with “navigating the ethics of digital scale”. The author emphasizes that leaders must be able to build a strong organization of safety, security and sustainability. Firms spend billions of dollars on new AI related technologies and innovations. Despite this, banks and other financial institutions face three main issues. The first challenge is an outdated operating model. The second challenge is the lack of a fitting talent strategy. Both challenges are interconnected to each other. Likewise, as a third challenge, Workforce Management (WFM), the core process that boosts performance levels and competency for an organization, has been reformed and disrupted by the introduction of AI.
Table of Contents
1 Introduction
1.1 Problem Definition
1.2 Purpose of this Study
1.3 Research Questions
2 Background Information
2.1 What is Artificial Intelligence?
2.2 The Importance of Artificial Intelligence
2.3 AI Branches
2.3.1 Machine Learning
2.3.2 Deep Learning
2.3.3 Data Mining
2.4 Workforce Management
2.4.1 Analysis of Work
2.4.2 Forecasting and Scheduling
2.4.3 Designing Jobs
2.4.4 Talent Acquisition
2.4.5 Time, Attendance and Absence Management
2.4.6 Reducing Expenses
2.4.7 Improving Customer Satisfaction
3 Literature Review
3.1 Changing the Nature of Work
3.1.1 Image Recognition and Optical Character Recognition
3.1.2 Facial Recognition
3.1.3 Natural Language Processing
3.2 Banking Challenges in Workforce Management and Opportunities with AI
3.2.1 Key Opportunities for AI within Banking and Financial Sectors
3.2.2 Automation
3.2.3 Customization
3.2.4 Decision-Making Improvement
3.3 FinTechs and TechFins
3.3.1 Main Purpose
3.3.2 Capabilities
3.3.3 Fintechs and Payments
3.3.4 Fintechs and Wealth Management
3.3.5 Fintechs and Digital Banking
3.3.6 Techfins and Payments
3.3.7 Techfins and Wealth Management
4 Methodology
4.1 Selection of Research Methods
4.1.1 Qualitative vs. Quantitative Research Method
4.1.2 Inductive vs. Deductive Approach
4.2 Research Design
4.2.1 Qualitative Interviews
4.2.2 Construction of the Interview Guide
4.2.3 Selection of Interviewees
5 Research Results
5.1 Creation of Themes and Codes
5.2 Interview Analysis and Results
5.3 Summary of Findings
Research Objectives and Themes
This thesis examines the impact of artificial intelligence on workforce management (WFM) within the banking and finance industry. It addresses the knowledge gap regarding how AI-driven tools disrupt traditional operational models, talent acquisition strategies, and the specific qualifications required for future WFM teams.
- Evolution of workforce management processes through AI implementation
- Challenges and opportunities for financial institutions in the digital age
- Transformation of job roles and the need for human-machine collaboration
- Strategic role of AI in talent acquisition and recruitment processes
- Impact of FinTechs and TechFins on banking competitive landscapes
Auszug aus dem Buch
3.1 Changing the Nature of Work
In the book Competing in The Age of AI, Karim Lakhani and Marco Iansiti stress the importance of leadership in a digital world. Workforce management challenges start at management level. As AI changes the very nature of organizations, operating models must be enhanced. Both authors mention that this can be achieved by leveraging existing strengths in new ways to support new strategies. By increasing the portion of processes and tasks that deliver customer value in a digitized format, we increase in the ability of accessible enterprise to deliver an extraordinary scope of products and services (compare Iansiti, M., Lakhani, K. R., 2020, pp. 215–218).
Lakhani and Iansiti also state that the enormous datasets needed to fuel AI are vulnerable to cyberattacks. As data, analytics and AI all connect to digital networks, leaders must be aware of how digital capabilities can be used and misused. Customer privacy is threatened when sensitive information is at risk. Managers must understand that the choice of business models is critical. Directing the ethics of digital scope, and scale as well as understanding these systems has become a worldwide management imperative. Most of the customers that use their services and technologies depend on them to conduct aspects of their daily lives. As a result, errors in the system can have terrible consequences for customers, and result in reputational damage to the company. AI systems have the tools and capabilities to continuously increase operating activities and influence workforce managerial decisions. AI transforms managerial tasks and creates new assets and capabilities across an organization. Rather than making people redundant, AI creates new management opportunities. The authors mention that transformation starts from the top.
Summary of Chapters
1 Introduction: Provides an overview of the data-driven history of the banking industry and identifies the technological revolution initiated by big data and AI in workforce management.
2 Background Information: Defines fundamental AI concepts, including machine learning and deep learning, and details core workforce management processes.
3 Literature Review: Discusses the shifting landscape of professional roles, banking challenges, and the emergence of FinTechs and TechFins.
4 Methodology: Outlines the qualitative research approach, the selection of interviewees, and the construction of the interview guide.
5 Research Results: Displays findings from the interviews conducted with industry experts, categorized into themes through computer-assisted analysis.
Keywords
Artificial Intelligence, Workforce Management, Banking, Finance, Machine Learning, Deep Learning, Data Mining, Talent Acquisition, FinTech, TechFin, Digital Transformation, Human-Machine Collaboration, Recruitment, Automation, Data Analytics
Frequently Asked Questions
What is the primary focus of this study?
The research investigates the influence and integration of Artificial Intelligence (AI) technologies into Workforce Management (WFM) within the global banking and finance sector.
What are the central themes discussed in the thesis?
The core themes include the automation of traditional tasks, the necessity for reskilling employees, the strategic transition from operational to insight-driven roles, and the competitive disruption posed by FinTechs.
What is the core research question?
The study aims to determine the impact of AI on WFM and HR teams, identify required candidate skill sets, and evaluate which specific banking functions are currently being automated by AI.
Which scientific methodology was utilized?
The author employed a qualitative research method, conducting semi-structured interviews with executive leaders and AI specialists, supplemented by an extensive literature review.
What does the main body of the work cover?
The work covers historical and technical foundations of AI, the evolution of WFM processes, existing literature on digital transformation, and a detailed analysis of interview results using MAXQDA.
Which keywords characterize this work?
Key terms include Artificial Intelligence, Workforce Management, FinTech, Talent Acquisition, Digital Transformation, and Human-Machine Collaboration.
How do FinTechs differ from traditional banking institutions according to the findings?
The text suggests that FinTechs act as agile software-driven disruptors that leverage technology to lower costs and improve customer experiences, often moving faster than regulated legacy banks.
What role does the Board of Directors play in AI adoption?
Experts emphasized that it is vital to have board members with technical or AI backgrounds to adequately assess risks and guide firms through the competitive era of technology.
What are the primary concerns regarding AI and recruitment?
Respondents expressed concern that relying solely on algorithms for talent acquisition might introduce human biases or overlook candidates who lack specific keywords but possess essential "soft" or creative skills.
- Arbeit zitieren
- Sofia Papadopoulou (Autor:in), 2020, The Impact of Artificial Intelligence on Workforce Management within the Banking and Finance Industry, München, GRIN Verlag, https://www.grin.com/document/1012863
