Advanced Technologies in Finance: How Machine Learning is Changing the Financial Sector

In recent years, the financial sector has seen major changes as a result of digitalization and technological innovations. One of the most significant developments is the integration of artificial intelligence (AI), in particular machine learning (ML), into the business processes of financial institutions. These technologies offer the ability to efficiently process large amounts of data and make informed decisions that both increase operational efficiency and improve the customer experience.

There are numerous use cases for machine learning in the financial sector. From fraud detection to risk assessment and stock price prediction, ML models offer versatile applications that often outperform traditional approaches. This bachelor thesis examines how machine learning is transforming the financial sector by analyzing different ML algorithms in specific application areas.

Goals

The main objective of this thesis is to investigate the different ML algorithms in specific application areas and to highlight their advantages over traditional methods. The specific objectives of the thesis include:

• Detailed description of use cases: Three main use cases of ML in the financial sector are analyzed: Risk Assessment, Fraud Detection and Stock Market Prediction. These areas are examined with regard to their efficiency and innovation potential.
• Identifying the benefits of ML: The thesis highlights the key benefits of ML technologies, such as higher efficiency, more accurate decision making and improvement of financial services. These positive aspects are intended to show how ML is transforming the financial industry and benefiting both companies and customers.
• Highlighting the challenges and limitations: A key component of the thesis is to examine the challenges of implementing ML models. These include aspects such as data management, model interpretability, ethics and fairness.
• Analysis of future developments: The thesis provides an outlook on future trends and developments in ML and examines how these technologies could further change the financial industry.

Methodology

A semi-systematic literature search was carried out for the study in order to identify relevant scientific papers on the selected use cases. The literature search included the use of various databases such as Google Scholar, Elsevier and Springer Link. The following methodological framework was used:

• Literature search: scientific articles, textbooks and case studies were analyzed to collect the most relevant information on the respective use cases. Methods such as the backward and forward snowballing technique were used to comprehensively identify relevant literature.
• Qualitative content analysis: The collected data was evaluated using qualitative content analysis. The algorithms were evaluated in terms of predictive performance, ease of use and computational complexity.
• Comparison and evaluation of the algorithms: The different ML algorithms were systematically compared to assess their suitability for the respective use cases. Both the technical and regulatory challenges were taken into account.

Results

The analysis of the bachelor's thesis impressively shows how ML can transform the financial sector. In the area of risk assessment, ML models offer significant advantages over traditional methods. Algorithms such as decision trees and neural networks enable more accurate and faster analyses, which leads to better risk assessment and therefore more informed business decisions. Another important area of application is fraud detection. Here, ML models show high efficiency in identifying fraudulent activities. In particular, supervised learning algorithms, such as support vector machines and random forests, are characterized by high accuracy and make a significant contribution to minimizing financial losses due to fraud.

One particularly challenging area is the prediction of share prices. Despite the inherent complexity and volatility of the stock market, ML models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have achieved promising results. These algorithms are able to recognize patterns in historical data and make predictions about future price movements, which is of great interest to investors.

In addition to the technical successes, the work also identifies numerous challenges in the implementation of ML in the financial sector. These include managing large amounts of data, ensuring the models are interpretable and complying with regulatory requirements. In particular, the need to make models transparent and comprehensible is of central importance in order to gain the trust of users and regulators.