The Impact of Artificial Intelligence on Workforce Management within the Banking and Finance Industry

Inhaltsverzeichnis (Table of Contents)

• Introduction
• Problem Definition
• Purpose of this Study
• Research Questions
• Background Information
• What is Artificial Intelligence?
• The Importance of Artificial Intelligence
• AI Branches
• Machine Learning
• Deep Learning
• Data Mining
• Workforce Management
• Analysis of Work
• Forecasting and Scheduling
• Designing Jobs
• Talent Acquisition
• Time, Attendance and Absence Management
• Reducing Expenses
• Improving Customer Satisfaction
• Literature Review
• Changing the Nature of Work
• Image Recognition and Optical Character Recognition
• Facial Recognition
• Natural Language Processing
• Banking Challenges in Workforce Management and Opportunities with AI
• Key Opportunities for AI within Banking and Financial Sectors
• Automation
• Customization
• Decision-Making Improvement
• FinTechs and TechFins
• Main Purpose
• Capabilities
• Fintechs and Payments
• Fintechs and Wealth Management
• Fintechs and Digital Banking
• Techfins and Payments
• Techfins and Wealth Management
• Methodology
• Selection of Research Methods
• Qualitative vs. Quantitative Research Method
• Inductive vs. Deductive Approach
• Research Design
• Qualitative Interviews
• Construction of the Interview Guide
• Selection of Interviewees
• Research Results
• Creation of Themes and Codes
• Interview Analysis and Results
• Summary of Findings

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This Master's thesis examines the impact of Artificial Intelligence (AI) on workforce management within the banking and finance industry. The study aims to understand how AI is changing the nature of work in this sector and to identify key opportunities and challenges associated with its adoption.

• The impact of AI on workforce management in the banking and finance industry
• The key opportunities and challenges of AI adoption in banking and finance
• The role of AI in transforming the nature of work in financial services
• The changing landscape of workforce management in the face of AI advancements
• The impact of AI on talent acquisition, training, and development in the banking and finance sector

Zusammenfassung der Kapitel (Chapter Summaries)

The introduction defines the research problem and the study's purpose, outlining the research questions that will be addressed. Chapter 2 provides background information on AI, its importance, and various branches, including Machine Learning, Deep Learning, and Data Mining. It also explores the concept of workforce management and its key aspects such as analysis of work, forecasting and scheduling, job design, talent acquisition, time and attendance management, expense reduction, and customer satisfaction improvement.

Chapter 3 presents a comprehensive literature review, focusing on the changing nature of work due to AI advancements, particularly in the banking and financial sectors. It explores key opportunities for AI within these sectors, including automation, customization, and decision-making improvement. This chapter also examines the role of FinTechs and TechFins, analyzing their main purpose, capabilities, and impact on various financial services, including payments, wealth management, and digital banking.

Chapter 4 delves into the methodology employed in the study. It discusses the selection of research methods, including the use of qualitative interviews. The chapter explains the rationale for choosing a qualitative approach and outlines the process of constructing the interview guide and selecting interviewees.

Chapter 5 presents the research results, focusing on the themes and codes generated from the qualitative data analysis. It provides detailed insights into the findings of the interview analysis and summarizes the key findings of the study.

Schlüsselwörter (Keywords)

This research focuses on the impact of Artificial Intelligence (AI) on workforce management within the banking and finance industry. Key areas of focus include AI branches like Machine Learning, Deep Learning, and Data Mining. Other essential keywords encompass workforce management, automation, customization, decision-making improvement, FinTechs, TechFins, and the changing nature of work in financial services.