Query: AI and fintech

title: Special Issue on AI and FinTech: The Challenge Ahead
abstract: The articles in the special section focus on artificial intelligence (AI) and Fintech. Finance has been one of the most active domains with increasingly bigger data, faster innovations, and more prosperous applications of artificial intelligence (AI) and data science, including algorithmic trading, cryptocurrency, blockchains, P2P lending, digital and mobile payments, digital assets, crowdfunding, roboadvising, regtech transformation, etc. AI and data science are driving new generation financial technology (FinTech) which profoundly disrupts existing theories of money, investment, credit, market, and regulation, and furthermore empowers new generation financial innovations, products, services, operations, processes, and ecosystems. We hope that this special issue on AI and FinTech will collect the latest advancements in FinTech driven by the advanced AI and data science that model complex interactions, relations, and their dynamics; their impact on big financial data analytics and complex financial behaviors across financial markets, products, systems, and networks; and their driving force behind smart financial innovations, services, markets, operations, processes, products, regulation, and risk management.

title: Special Issue on AI and FinTech: The Challenge Ahead

title: Towards a responsible use of artificial intelligence (AI) and fintech in modern banking

title: Explainable AI in Fintech Risk Management
abstract: The paper proposes an explainable AI model that can be used in fintech risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model employs Shapley values, so that AI predictions are interpreted according to the underlying explanatory variables. The empirical analysis of 15,000 small and medium companies asking for peer to peer lending credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behavior.

title: Anti discrimination Laws, AI, and Gender Bias: A Case Study in Non mortgage Fintech Lending
abstract: We study the impact of the existing anti discrimination laws in different countries on gender bias in the non mortgage consumer fintech lending setting.  Building on the study of discrimination in operations, financial economics, and computer science, our paper investigates the impact and drivers of discrimination in machine learning models, trained on alternative data, and provides technically and legally permissible approaches for firms to reduce discrimination, whilst managing profitability.  We train statistical and machine learning (ML) models on a large and realistically rich publicly available dataset and measure the impact on model discrimination, predictive quality, and firm profitability. We use ML explainability techniques to understand the drivers of ML discrimination.  We find that laws which prohibit the use of gender (e.g. those in the US) substantially increase discrimination, and slightly decrease firm profitability. We observe ML models are less discriminatory, of better predictive quality, and more profitable compared to traditional statistical models like logistic regression. Unlike omitted variable bias which drives discrimination in statistical models, ML discrimination is driven by changes in feature engineering and feature selection when gender is excluded. We observe that down sampling the training data to rebalance gender, gender aware hyperparameter tuning, and up sampling the training data to rebalance gender, all reduce discrimination, with varying trade offs in predictive quality and firm profitability. Probabilistic gender proxy modeling (imputing the gender of applicants) further reduces discrimination, with negligible impact to predictive quality or firm profitability.  Consequently, a rethink is required of the anti discrimination laws, specifically with respect to the collection and use of protected attributes for machine learning models. Firms should be able to collect protected attributes to, at minimum, measure discrimination, and ideally, take steps to reduce it. However, increased data access should come with greater accountability and responsibility for firms.

title: The Future of Finance: The Impact of FinTech, AI, and Crypto on Financial Services

title: Fintech Empowerment: Data Science, AI, and Machine Learning

title: AI in FinTech: A Research Agenda
abstract: Smart FinTech has emerged as a new area that synthesizes and transforms AI and finance, and broadly data science, machine learning, economics, etc. Smart FinTech also transforms and drives new economic and financial businesses, services and systems, and plays an increasingly important role in economy, technology and society transformation. This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech associated data and repositories, FinTech driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech.

title: AI/ML applications and the potential transformation of Fintech and Finserv sectors
abstract: This paper discusses the potential transformation of Fintech and Finserv sectors in light of the increased use and deployment of Artificial Intelligence (AI)/Machine Learning (ML) applications. By drawing on quick scan of available literature, the paper provides an overview of aspects of Fintech and Finserv sectors which are likely to change and the aspects of these sectors which are likely to remain unchanged. The paper highlights the trend towards long term structural changes to the financial economy, potential growth in the use of personalised AI platforms in conjunction with the rise of RegTech (including algorithmic regulation) of Fintech and Finserv sectors. The findings suggest scope for further comprehensive assessment of the nature of transformation in Fintech and Finserv sectors due to AI/ML.

title: AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech
abstract: Artificial Intelligence has become increasingly important for organizations. Tech leading organizations are embracing the forthcoming artificial intelligence revolution. Intelligent systems are replacing and cooperating with traditional software components. Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems. This study aims to understand the processes by which artificial intelligence based systems are developed and how state of the art lifecycle models fit the current needs of the industry. We conducted an exploratory case study at ING, a global bank with a strong European base. We interviewed 17 people with different roles and from different departments within the organization. We have found that the following stages have been overlooked by previous lifecycle models: data collection, feasibility study, documentation, model monitoring, and model risk assessment. Our work shows that the real challenges of applying Machine Learning go much beyond sophisticated learning algorithms more focus is needed on the entire lifecycle. In particular, regardless of the existing development tools for Machine Learning, we observe that they are still not meeting the particularities of this field.