Search Results (54)

European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic mispricing. We employ a twofold econometric analysis on a dataset of consumer loans from a Lithuanian peer-to-peer platform. After data preparation for the regression, the sample consists of 9707 loans. First, logistic regression is used to model actual default risk, controlling for credit rating, age, loan amount, and education. Second, Ordinary Least Squares (OLS) regression is used to model the interest rate set by the platform. The Logit model finds that gender is a highly significant predictor of default (p < 0.001), with male borrowers associated with a higher probability of default. Conversely, the OLS model finds that gender is not a statistically significant factor in loan pricing (p = 0.263), confirming the platform’s compliance with EU law. The findings empirically demonstrate the regulatory paradox: the legally compliant, gender-blind pricing model fails to account for a significant risk differential. This leads to systematic risk mispricing and an implicit cross-subsidy from lower-risk female borrowers to higher-risk male counterparts, highlighting a critical tension between regulatory intent and outcome fairness. The analysis is limited to observed loan-level characteristics; it does not incorporate household composition or the internal structure of the platform’s proprietary scoring model. Full article

Blockchain technology is now a useful tool that FinTech organizations use to increase transparency, optimize activities, and seize new possibilities. This research explores blockchain applications within the FinTech sector. This study systematically explores blockchain applications within the FinTech sector by 164 peer-reviewed articles, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review identifies eleven applications, such as smart contracts, financial inclusion, crowdfunding, digital identity, trade finance, regulatory compliance, insurance, asset management, investment, banking, and lending. A mixed-method strategy, combining quantitative and qualitative content analysis, was applied to examine the adoption and impact of blockchain across these subdomains. It further discusses current challenges such as regulatory ambiguity, interoperability limitations, and cybersecurity threats. This paper provides a consolidated framework of blockchain’s actual application in FinTech subdomains and identifies the main gaps in the existing literature. These results have practical implications for practitioners, researchers, and policymakers who seek to harness blockchain for achieving financial innovation and inclusive growth. Full article

Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., $70.77\%$ on Lending Club, $93.25\%$ on Australia, $77.85\%$ on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search’s accuracy while reducing runtime by up to $75.7$-fold (e.g., $3.19$ vs. $241.47$ min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below $0.4\%$ under $\pm 10\%$ perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation $> 0.95, p<0.01$) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions. Full article

Background/Objectives: Schizophrenia Spectrum Disorders (SSDs) carry a debilitating burden of disease which, even after pharmacological and psychological treatment are optimized, remains difficult to fully target. New online-delivered and user-led interventions may provide an appropriate, cost-effective answer to this problem. This study aims to retrieve the currently gathered findings on the efficacy of these interventions across several outcomes, such as symptom severity, social cognition, functioning and others. Methods: A systematic review of the current available literature was conducted. Of 29 potentially relevant articles, 26 were included and assigned at least one of four intervention types: Web-Based Therapy (WBT), Web-Based Psycho-Education (WBP), Online Peer Support (OPS) and Prompt-Based Intervention (PBI). Results: The findings were grouped based on outcome. Of 24 studies evaluating the effects of symptom severity, 14 have achieved statistically significant results, and 10 have not. WBT (such as online-delivered Cognitive Behavioral Therapy, Acceptance and Commitment Therapy, social cognition training and Mindfulness Training) seemed to be the most effective at targeting symptoms. Of 14 studies evaluating functioning, seven achieved significant results, four involving a form of social or neurocognitive training, suggesting a potential pathway towards functional improvements through interventions targeting cognition and motivation. Regarding social cognition, all seven studies measuring the effects of an intervention on this outcome produced significant results, indicating that this outcome lends itself well to remote, online administration. This may be linked with the nature of social cognition exercises, as they are commonly administered through a digital medium (such as pictures, videos and auditory exercises), a delivery method that suits the online-user led model very well. Conclusions: Online user-led interventions show promise as a new way to tackle functional deficits in SSD patients and achieve these improvements through targeting social cognition, a hard-to-reach component of the burden of SSDs which seems to be successfully targetable in a remote, user-led fashion. Symptomatic improvements can also be achievable, through the combination of these interventions with treatment as usual. Full article

Purpose: Our study investigates the combined effects of financial technologies (fintech) and the digital economy on sustainable development, considering geopolitical risks as a moderating factor. Origin: While sustainable development is a global imperative, the integrated roles of digital transformation and fintech remain insufficiently explored. Our research addresses this gap by analyzing their impacts on socioeconomic advancement and environmental sustainability across diverse contexts. Methodology: Employing panel data from 30 developed and developing countries between 1990 and 2023, we assess sustainable development using the Environmental Performance Index (EPI) and the Human Development Index (HDI). Independent variables include proxies for the digital economy (e.g., internet usage, mobile subscriptions, and high-tech exports) and fintech (e.g., digital payments, digital currency, and peer-to-peer lending). The Geopolitical Risk Index (GPRI) is used to evaluate the effect of political instability. We apply generalized least squares (GLS) and fixed-effects estimation (within) to ensure robustness. Findings: Our results indicate that digital transformation and fintech significantly foster socioeconomic development and environmental performance, even amidst geopolitical instability. Key variables such as digital payments and internet access show substantial positive impacts, providing valuable insights for policymakers aiming to enhance resilience and sustainability. Contributions: Our article offers a comprehensive evaluation of how the digital economy and fintech jointly influence sustainable development under geopolitical risks, providing a nuanced understanding for policymakers and researchers. Full article

In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a single scoring approach may bias the loan evaluation conclusion. In this paper, we propose a bivariate model based on the integration of two scoring approaches. We first formulate the loan evaluation task as a multi-target problem, in which loan_status (i.e., default or not default) is used as the discrete outcome for the credit risk measure while the annualized rate of return (ARR) is used as the continuous outcome for the profitability measure. Then to solve the multi-target problem, we design a novel loss function based on the assumption that the discrete outcome follows a Bernoulli distribution, and the continuous outcome is normally distributed conditional on the discrete output. The effectiveness of the proposed model is examined using the real-world P2P data from the Lending Club. Results indicate that our approach outperforms the sole scoring methods by identifying loans with higher profit and lower default risk. Therefore, the proposed method can serve as an alternative for loan evaluation. Full article

In this paper, we analyzed the role of credit consortia in supporting SMEs of the Italian region of Sardinia around and during the SARS-CoV-2 pandemic crisis. Credit consortia (or credit guarantee schemes) are financial companies whose institutional role is to support small firms needing bank lending who are individually weak in the bank–firm relationship. Credit consortia are particularly relevant in Italy, where they mitigate credit restrictions for SMEs by supplying guarantees to the bank, allowing for partial coverage of potential losses, providing peer-monitoring activity, and collectively negotiating more favorable interest rates and other conditions with banks. During the SARS-CoV-2 pandemic, credit consortia had a crucial role in supporting Sardinian SMEs with guarantees and obtaining government financial support. The evolution of Sardinian companies’ financial structures during the SARS-CoV-2 pandemic shows that the confidi-supported firms have low capitalization and are financially fragile yet capable of good returns. The liquidity provided by the government during the pandemic loosened these constraints, boosting the available liquidity, which translated, in short, into higher investment and higher sales. The demographics of Sardinian companies in 2019–2022 and the volumes of loans and savings showed a strengthening of debt capital payments, increased collections, and a progressive improvement of the Sardinian companies’ net financial positions. Full article

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates. Full article

As the global financial ecosystem undergoes a paradigm shift toward sustainability, platforms emerge as instrumental intermediaries, connecting diverse stakeholders, facilitating information flow, and catalyzing impactful investments. This paper analyses the evolving landscape of sustainable finance and investigates the role of platforms in fostering its growth. Sustainable finance platform-based enablers were determined using a systematic literature review and bibliometric techniques on a sample of papers retrieved from the SCOPUS database, and included crowdfunding platforms, impact investment platforms, peer-to-peer (P2P) lending platforms, blockchain-based financing platforms, and ESG data platforms. The analysis showed that platform-based solutions act as accelerators of sustainable finance mobilization, by enhancing transparency of the processes, and by improving dissemination and accessibility of the funds needed. Thus, platform-based solutions help a broader set of stakeholders direct the potential of platforms to accelerate the transition toward a more sustainable and inclusive global financial system. Full article

Peer-to-peer (P2P) lending has gained significant traction in the financial landscape, particularly in developing economies such as Indonesia, where access to traditional banking services remains a challenge for many. The aim of this study is to investigate the application of value engineering as a decision support system for choosing and evaluating P2P lending platforms, using Indonesia as a case study. P2P lending is a relatively new service in the digital economy for lending money to individuals through online financial intermediaries, where borrowers and lenders often have no prior relationship. Value engineering, a systematic approach to improving the value of a product or service, can be a valuable tool in the context of P2P lending. Through applying value engineering principles, P2P lending platforms can identify and prioritize the key factors that influence lending decisions, such as risk, return, and data privacy, to enhance the overall value proposition for both borrowers and lenders. Both value engineering and P2P lending are technoeconomic systems that aim to enhance the overall value and efficiency of a system or process, albeit through different approaches. This study presents a comprehensive framework for applying value engineering as a decision support system for P2P lending in Indonesia. The findings reveal that the value engineering index developed in this study effectively differentiates between P2P lending platforms based on their performance. Specifically, platforms with a high-value index were found to offer competitive interest rates, lower fees, and superior risk management, as evidenced by their non-performing loan (NPL) rates. In contrast, platforms with a low-value index were associated with higher NPLs and less favorable terms for stakeholders. These insights provide practical guidance for P2P lending platforms, regulators, and consumers; highlight the importance of a value engineering approach in optimizing platform selection; and enhance the P2P lending ecosystem’s sustainability in Indonesia. Full article

E-biking is alluring for its various physical, environmental, and financial benefits and the ability to travel farther and faster, and being physically easier to ride than astride an analog (traditional) bicycle. E-bikes are also a source of controversy, especially in places where analog bicycles have been allowed but e-bikes represent a “slippery slope” of technology permissions and/or in situations where the function of e-bikes may increase concerns about safety. Despite an increase in use and conversation about such use, academic literature focused on e-bikes’ social aspects remains sparse. The objective of this work is to describe the existing literature on the characteristics of social aspects of e-biking, particularly in leisure contexts. Analyzing the literature on e-bike social research is crucial considering e-bikes’ rapid rise in popularity and potential effects on access, inclusion, leisure, and sustainability. As e-bike prevalence and use increases worldwide, it is important to understand what topics characterize the existing e-bike literature, and, particularly in leisure-focused studies, to ascertain where studies may lend insight toward aims of inclusive and sustainable access, and related policy considerations. The Integrated Recreation Amenities Framework (IRAF) provides a conceptual framework for considering this question, as it focuses on the topical, spatial, and temporal scales of outdoor leisure-related activities toward sustainable conditions and explicitly provides an opportunity for emergent and case-specific factors to be considered alongside established ones. In this work, we explore the following: (1) How are e-bikes discussed across disciplines? and (2) How are e-bikes discussed in leisure-focused articles? Using a scoping review approach, we analyzed a corpus of 279 peer-reviewed articles relevant to the social aspects of e-bikes. Primarily using the IRAF for conceptual framing, our results center the geographies and contexts, topical areas, interdisciplinarity, and emergent additional social considerations of e-biking in general and in leisure-specific studies. The results enable us to connect interdisciplinary topic discussions and suggest where foundational and connective studies are warranted. This can inform decision making related to e-bike adoption, encourage multi-scalar thinking, and extend interdisciplinary research. Full article

The aim of this study is to investigate the impact of delegated monitoring by a group leader and peer monitoring by group members on loan defaults in a group-based lending program in Vietnam. The data used in the study were collected from a questionnaire survey of 675 participants involved in a group-based lending program conducted from August to October 2022 in the Mekong River Delta, Vietnam. This group-based lending program employs a unique monitoring system that involves hiring the group leader to supervise the group and encouraging group members to monitor each other. The empirical findings derived from the Probit model indicated that delegated monitoring significantly reduces loan defaults, but there was no evidence supporting the effectiveness of peer monitoring within the group. Additionally, under the delegated monitoring scheme, commissions and group size plays an important role in decreasing loan defaults. The implication of the findings is that the Vietnam Bank for Social Policies (VBSP) could maintain large group sizes to provide incentives for group leaders through commissions to enhance repayment rates. Full article

Lenders writing loans in the peer-to-peer market carry risk with the anticipation of an expected return. In the current implementation, many lenders do not have an exit strategy beyond holding the loan for the full repayment term. Many would-be lenders are deterred by the risk of being stuck with an illiquid investment without a method for adjusting to overall economic conditions. This risk is a limiting factor for the overall number of loan transactions. This risk prevents funding for many applicants in need, while simultaneously steering capital towards other more liquid and mature markets. The underdeveloped valuation methods used presently in the peer-to-peer lending space present an opportunity for establishing a model for assigning value to loans. We provide a novel application of an established model for pricing peer-to-peer loans based on multiple factors common in all loans. The method can be used to give a value to a peer-to-peer loan which enables transactions. These transactions can potentially encourage participation and overall maturity in the secondary peer-to-peer loan trading market. We apply established valuation algorithms to peer-to-peer loans to provide a method for lenders to employ, enabling note trading in the secondary market. Full article

Understanding borrowers’ behavior is essential in making lending decisions, strengthening financial inclusion, and alleviating poverty. This research adopts a bibliometric approach to provide an overview of the borrower’s behavior relative to the selected literature. Bibliometric analysis quantifies the impact and quality of scientific production. This study reviewed 989 articles obtained from SCOPUS and published from 1987 to 2023. Data were cleaned, formatted, and analyzed using VOS viewer (1.6.19) and the R-Bibliometrix package. The research established an increased interest in borrowers’ behavior among scholars. Nonetheless, it is overshadowed by studies in lending behavior, microfinance, banking, peer-to-peer lending, and fintech. The scholarly focus is mainly on the supply side of the credit industry with little regard to demand-side dynamics, such as borrowers’ decision-making processes, which can affect the performance of credit facilities. This study recommends that further studies on credit facility demand-side dynamics should be carried out to understand the drivers of borrowers’ decisions. Full article

Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%. Full article