APPROACHES FOR MACHINE LEARNING IN FINANCE

Machine learning and artificial intelligence are big topics in the financial services sector these days. Financial institutions (FIs) are looking to more powerful analytical approaches in order to manage and mine increasing amounts of regulatory reporting data and unstructured data, for purposes of compliance and risk management (applying machine learning as " RegTech ") or in order to compete effectively with other FIs and FinTechs. This article aims to give an introduction to the machine learning field and discusses several application cases within financial institutions, based on discussions with IIF members and technology ventures: credit risk modeling, detection of credit card fraud and money laundering, and surveillance of conduct breaches at FIs. Two tentative conclusions emerge on the added value of applying machine learning in the financial services sector. First, FinTech/RegTech the ability of machine learning methods to analyze very large amounts of data, while offering a high granularity and depth of predictive analysis, can improve analytical capabilities across risk management and compliance areas in FIs. Examples are the detection of complex illicit transaction patterns on payment systems and more accurate credit risk modeling. Second , the application of machine learning approaches within the financial services sector is highly context-dependent. Ample , high-quality data for training or analysis are not always available in FIs. More importantly, the predictive power and granularity of analysis of several approaches can come at the cost of increased model complexity and a lack of explanatory insight. This is an issue particularly where analytics are applied in a regulatory context, and a supervisor or compliance team will want to audit and understand the applied model.

IEEE Access

Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attractive for personal and corporate finance applications. Using ML applications in finance raises ethical issues that need to be carefully examined. We engage a group of experts in finance and ethics to evaluate the relationship between ethical principles of finance and ML. The paper compares the experts' findings with the results obtained using natural language processing (NLP) transformer models, given their ability to capture the semantic text similarity. The results reveal that the finance principles of integrity and fairness have the most significant relationships with ML ethics. The study includes a use case with SHapley Additive exPlanations (SHAP) and Microsoft Responsible AI Widgets explainability tools for error analysis and visualization of ML models. It analyzes credit card approval data and demonstrates that the explainability tools can address ethical issues in fintech, and improve transparency, thereby increasing the overall trustworthiness of ML models. The results show that both humans and machines could err in approving credit card requests despite using their best judgment based on the available information. Hence, human-machine collaboration could contribute to improved decision-making in finance. We propose a conceptual framework for addressing ethical challenges in fintech such as bias, discrimination, differential pricing, conflict of interest, and data protection. INDEX TERMS Ethics, machine learning, explainability, finance, fintech, financial services. I. INTRODUCTION 19 Machine learning (ML) systems have been implemented 20 by financial institutions across various financial services. 21 ML algorithms are applied to personal finance (through chat-22 bots powered with natural language processing or person-23 alized insights for wealth management), consumer finance 24

IAEME PUBLICATION, 2024

The adoption of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed risk management practices in financial services. This paper examines the impact of AI and ML on enhancing risk management through more precise credit risk evaluations, automated fraud detection, and advanced investment strategies. By analyzing case studies from JPMorgan Chase, ZestFinance, and Betterment, the study illustrates how these technologies improve accuracy, efficiency, and decisionmaking in financial operations. However, challenges such as data integrity, algorithmic transparency, and ethical concerns must be addressed. The paper concludes with insights into future developments and the need for effective strategies to overcome these challenges and fully capitalize on the benefits of AI and ML in managing financial risk.

iJournals: International Journal of Software & Hardware Research in Engineering (IJSHRE), 2023

The paper discusses the integration of Artificial Intelligence and Machine Learning in the financial sector for improved risk management and regulatory compliance. The paper explores how AI and ML enhance risk identification, assessment, quantification, and mitigation processes, providing accurate credit and market risk evaluations using real life case studies. Furthermore, the paper also investigates how such systems can streamline compliance procedures, automate reporting, monitoring, as well as auditing tasks pragmatically and address ethical and privacy challenges regarding these systems.

Artificial Intelligence (AI) and Data Science have revolutionized the FinTech and banking sectors by enhancing fraud detection, improving customer experiences, automating credit risk assessments, and strengthening cybersecurity. Machine Learning (ML) models, natural language processing (NLP), and blockchain integration have enabled financial institutions to operate efficiently, securely, and accurately. This research investigates AI-driven solutions for banking automation, explores the role of predictive analytics in credit risk evaluation, and assesses AI-powered fraud prevention frameworks. Through experimental case studies, this study demonstrates the impact of AI on financial services, achieving a 92% accuracy rate in fraud detection, a 40% improvement in banking automation efficiency, and an 80% reduction in manual data processing costs. AI-powered FinTech systems developed risk assessment systems at high levels because they analyze large datasets in real time. Loan defaults diminish when the credit scoring system speeds up because machine learning algorithms generate risks better than standard assessment methods. AI-powered virtual assistants supply individualized financial guidance to clients whose performance feedback emerges after monitoring virtual assistance operations. Financial institutions achieve significant operational enhancements resulting in better customer satisfaction through AI that actively updates operations within this industry to improve results. Financial institutions leverage anomaly detection methods in AI technology to conduct banking security protection while doing real-time fraud detection. The financial security systems provide absolute defense coverage to institutions while building secure bank operations. Financial organizations leverage deep learning tools to process transaction data that helps identify fraudulent conduct before detrimental future fraud incidents occur. Security system machine learning supports two security objectives through risk identification by protecting customer information and safeguarding financial resources. Changes in the financial sector occur through blockchain technology connected with AI which builds security platforms through simplified regulatory access for financial transparency enhancement.

Artificial Intelligence, 2021

International Journal of Electrical and Computer Engineering (IJECE), 2024

This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.

Machine Learning and Fintech, 2021

In the last few decades, the banking and financial services have been adopting Artificial Intelligence (AI) to aid in accomplishing tasks that have always been done in that domain either manually or with traditional computing. The reason was speed, saving money, efficiency, avoiding errors and automating repetitive tasks. In addition, AI also allowed the emergence of new solutions and services and the possibility of solving problems that were impossible hitherto. On the other hand, AI also enabled the birth of new players in the finance area, not following the traditional concepts but competing directly with banks. That prompted some to forecast the end of banks some time ago. This report addresses what Artificial Intelligence brought to Banking and Finance. The problems it solves, its applications and problems and how it shaped today’s financial technology, or Fintech as this is commonly referred to. First, we will take a look at some history and context. How it was before and the differences to what Fintech is today. We will also discuss the current challenges and threats and new services. Then we will move on to the main applications divided into three main areas. There are several possible ways to divide Fintech. One possibility is to divide by specific applications, but there are so many. Another is dividing by AI techniques and mention the application. However, many applications of the same technique exist and many techniques can be used to achieve any given main application. The division offered in this report was based on the one presented on [1], which split Fintech into ”front office (conversational banking), middle office (anti-fraud) and back office (underwriting).” In the end, we will peek at what may be coming next, building on what already exists and emerging technologies. Then we wrap things up with the pros and cons. The report closes with a conclusion, summarising what has been explained. To better understand some of the concepts and solutions, there are three annexes. The first shows a possible inter-dependency relationship map between AI and the applications made possible. The second annexe has some images to show how some of the use cases work. A final annexe deals solely with several examples for one application alone.

2021

Finance is the department in charge of handling the company's funds and preparing how they will be invested on different properties. This makes finance the key to decision makings in any firm's investment approach. On the other hand, machine learning (ML) technology is a branch of artificial intelligence (AI), that has the potential to revolutionize data science by enabling a deeper understanding of data patterns, and make decisions with minimal human intervention. Thus, augmenting ML with financial technology (FinTech); a recent emerging field of research in data science; will lead to optimized, dynamic and more robust investment decisions. This paper is a comprehensive survey that will detail the challenges and opportunities facing ML, financial data, and FinTech industry, taking into consideration an industry viewpoint of some challenges to result in a smart financial services to meet industry needs. This will provide researchers a clear vision for futuristic research in the field of ML and FinTech as challenges will be transformed into research opportunities. That has been said, this paper presents two major contributions. The primary contribution is the presentation of a fully fledged survey covering all major aspects of FinTech. The other contribution is the proposal of a recommendation manifest to solve most of these challenges and play a role of a directive pipeline for future researchers.

2021

The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the emergence of smarter algorithms for solving complex and challenging problems, organizations have found it possible to harness a humongous volume of data in realizing solutions that have far-reaching business values. This introductory chapter highlights some of the challenges and barriers that organizations in the financial services sector at the present encounter in adopting machine learning and artificial intelligence-based models and applications in their day-to-day operations.