Machine learning-driven credit risk: a systemic review

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models

Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer (P2P) lending markets to survive, they need to employ credit-risk management practices such that an investor base is profitable in the long run. Traditionally, credit-risk management relies on credit scoring that predicts loans’ probability of default. In this paper, we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans. To validate our profit scoring models with traditional credit scoring models, we use data from a European P2P lending market, Bondora, and also a random sample of loans from the Lending Club P2P lending market. We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following: logistic and linear regression, lasso, ridge, elastic net, random forest, and neural networks. We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans. More specifically, as opposed to credit scoring models, returns across all loans are 24.0% (Bondora) and 15.5% (Lending Club) higher, whereas accuracy is 6.7% (Bondora) and 3.1% (Lending Club) higher for the proposed profit scoring models. Moreover, our results are not driven by manual selection as profit scoring models suggest investing in more loans. Finally, even if we consider data sampling bias, we found that the set of superior models consists almost exclusively of profit scoring models. Thus, our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models

Essays on Peer to Peer Lending

The Peer-to-Peer (P2P) lending model has become increasingly popular in China in recent years. In 2012, there are only 298 P2P platforms operating in China and loan volume is 22.9 billion RMB while in the first half of 2018, there are 1881 P2P platforms and trading volume has reached 7.33 trillion RMB. Although both number of platforms and transaction volume have increased significantly, severe asymmetric information still discourages participants. This doctoral thesis uses three empirical chapters to investigate the P2P lending market in China. Drawing on Message framing and signaling theory, we first examines the extent to which message framing is associated with funding outcomes receive in the context of P2P lending and whether positive message framing reinforces the positive impact of credit ratings on funding outcomes. Using a Heckman two stage model, we find that the use of positively framed messages is positively associated with positive funding outcomes. Besides, positive message framing enhances the positive impact of the credit ratings (an example of costly signals) on funding outcomes. The results contribute to the literature on the effectiveness of cheap signals in the context of Internet-based interactions while highlighting complementarities between different types of signals in P2P lending. We then investigate the role of psychological distancing and language intensity in P2P funding performance. We bridge the P2P lending literature and psycholinguistics literature and set out to explain how psychological distancing manifested by linguistic styles can influence lenders’ decision on P2P funding campaign. We argue find that linguistic styles related to psychological distancing have a negative impact onare negatively related to P2P funding success. Moreover, the language intensity tends to strengthen the negative relationship between psychological distancing and funding success. Our empirical results provide general support for the argument. This finding is consistent with psycholinguistics literature which suggests that psychological distancing is associated with negative interpersonal outcome (Simmons et al, 2005; Revenstorf et al, 1984). Specifically, the number of “you” and the number of negations used in borrowers’ description are negatively related to the willingness of the lender to support the funding campaign. The intensive language negatively strengths the relationship between the funding performance and number of “you” but does not apply to number of negations. Lastly, we investigate the funding performance of the financial excluded borrower in a large P2P lending platform. The association of financial technology (fintech) and financial exclusion has attracted attention since rapid growth of fintech innovation. Using loan-level data from a lending Chinese P2P company, we find there is a negative indirect effect of financial exclusion on funding success through credit score. In a moderated mediation analysis, we also find new business model such as offline authentication and education qualification positively moderates the linkage between the financial excluded and credit score and therefore negative indirect effect of financial exclusion on funding success is overturned when the excluded borrower has conducted offline authentication and obtained higher education qualification. In the end, we examine the determinants of offline authentication decision. We find the borrowers in a city with better financial infrastructure are more willing to conduct authentication. However, the financial excluded borrowers are less likely to conduct offline authentication

Sustainability, Digital Transformation and Fintech: The New Challenges of the Banking Industry

In the current competitive scenario, the banking industry must contend with multiple challenges tied to regulations, legacy systems, disruptive models/technologies, new competitors, and a restive customer base, while simultaneously pursuing new strategies for sustainable growth. Banking institutions that can address these emerging challenges and opportunities to effectively balance long-term goals with short-term performance pressures could be aptly rewarded. This book comprises a selection of papers addressing some of these relevant issues concerning the current challenges and opportunities for international banking institutions. Papers in this collection focus on the digital transformation of the banking industry and its effect on sustainability, the emergence of new competitors such as FinTech companies, the role of mobile banking in the industry, the connections between sustainability and financial performance, and other general sustainability and corporate social responsibility (CSR) topics related to the banking industry. The book is a Special Issue of the MDPI journal Sustainability, which has been sponsored by the Santander Financial Institute (SANFI), a Spanish research and training institution created as a collaboration between Santander Bank and the University of Cantabria. SANFI works to identify, develop, support, and promote knowledge, study, talent, and innovation in the financial sector

Análisis de determinantes y gestión de riesgos en crowdfunding de préstamos entre pares

El objetivo de este estudio fue analizar la tendencia en investigación sobre los determinantes y gestión de riesgos en crowdfunding de préstamos P2P, con el fin de ampliar el conocimiento a inversionistas, empresarios y formuladores de políticas sobre esta financiación disruptiva. Se utilizó un enfoque cualitativo con descripción de publicaciones enfocadas al riesgo crediticio. Seguidamente, se realizó un análisis bibliométrico de la producción científica en las bases WOS y Scopus. El análisis bibliométrico se realizó con las plataformas VOSviewer y RStudio (librerías Bibliometrix y Biblioshiny), en el que se identificaron cuatro clústeres temáticos actuales de investigación que enfocan la producción de conocimiento. A partir del análisis descriptivo se realizó una aproximación teórica con los hallazgos más relevantes. Este estudio concluye que el crowdfunding de préstamos P2P es emergente en Latinoamérica y requiere atención en el riesgo crediticio presente en los prestatarios y en la plataforma en línea, con factores que limitan al inversionista en la identificación de riesgos e interpretación de modelos que los predicen y evalúan, lo cual los expone a altas probabilidades de incumplimiento de pago por parte de los prestatarios. Por ende, es necesario fortalecer la normatividad en el contexto de los países donde se desarrolla, a fin de generar credibilidad y confianza en este mercado disruptivo

Advances in Crowdfunding: Research and Practice

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A Hybrid Simulation Framework of Consumer-to-Consumer Ecommerce Space

In the past decade, ecommerce transformed the business models of many organizations. Information Technology leveled the playing field for new participants, who were capable of causing disruptive changes in every industry. Web 2.0 or Social Web further redefined ways users enlist for services. It is now easy to be influenced to make choices of services based on recommendations of friends and popularity amongst peers. This research proposes a simulation framework to investigate how actions of stakeholders at this level of complexity affect system performance as well as the dynamics that exist between different models using concepts from the fields of operations engineering, engineering management, and multi-model simulation. Viewing this complex model from a systems perspective calls for the integration of different levels of behaviors. Complex interactions exist among stakeholders, the environment and available technology. The presence of continuous and discrete behaviors coupled with stochastic and deterministic behaviors present challenges for using standalone simulation tools to simulate the business model. We propose a framework that takes into account dynamic system complexity and risk from a hybrid paradigm. The SCOR model is employed to map the business processes and it is implemented using agent based simulation and system dynamics. By combining system dynamics at the strategy level with agent based models of consumer behaviors, an accurate yet efficient representation of the business model that makes for sound basis of decision making can be achieved to maximize stakeholders\u27 utility

Advances in Crowdfunding

This open access book presents a comprehensive and up-to-date collection of knowledge on the state of crowdfunding research and practice. It considers crowdfunding models and their different manifestations across a variety of geographies and sectors, and explores the perspectives of fundraisers, backers, platforms, and regulators. Gathering insights from a wide range of influential researchers in the field, the book balances concepts, theory, and case studies. Going beyond previous research on crowdfunding, the contributors also investigate issues of community, sustainability, education, and ethics. A vital resource for anyone researching crowdfunding, this book offers readers a deep understanding of the characteristics, business models, user-relations, and behavioural patterns of crowdfunding