International Journal of Financial Engineering

Credit scoring is used by institutions in managing the credit risk associated with their borrowers. In addition to the traditional variables used in credit scoring, recent developments in machine learning have allowed the inclusion of data sources, such as ongoing payment behaviors and patterns, creating more robust and dynamic models. While such studies have more commonly been done for credit card data, there is limited research in the context of microfinance. Investigating this niche, this study focuses on uncovering how delinquency can be characterized and whether delinquency states exist within microfinance loans using a dataset from a microfinance institution (MFI) in the Philippines. Further, Markov chains and transition state matrices from the dataset were used to predict repayment sequences, and factors such as borrower characteristics, loan information and repayment behaviors were used in predicting the final outcome of the loans. The findings show that Markov chains can predict repayment sequences using a transition state matrix based on the full term of the loan. This model outperformed other models created from transition state matrices based on the first 8, 12, and 17 weeks of the loan term, indicating the repayment performance in the first weeks is not representative of the full term and cannot be used to accurately predict future repayment patterns. The equivalent second-order Markov chains were also examined which resulted in better predictions generally. Meanwhile, in predicting the final outcome of the loans, random forest models outperformed decision tree models using an accuracy and modified accuracy metric for evaluation. Across all models assessed in this study, behavioral characteristics and payment patterns consistently fared higher in terms of feature importance than borrower and loan characteristics. Particular to tiering delinquency states, the combination of average days overdue and delinquency streak appeared to be fitting resulting in delinquency tiers Onset Delinquency and Significant Delinquency. The ability to predict repayments aims to provide MFIs with better oversight of their loan portfolios. With an emphasis on the early detection of delinquency, predictive models using relevant features, such as those identified in this study, may allow the implementation mitigating actions at specific time periods to improve overall repayment rates among borrowers of MFIs.

Credit Valuation Adjustment is a balance sheet item which is nowadays subject to active risk management by specialized traders. However, the most important risk factors, which are the default intensities of the counterparties, affect in a nondifferentiable way the most general Monte Carlo estimator of the adjustment, through simulation of default times. Thus the computation of first- and second-order (pure and mixed) sensitivities with respect to inputs affecting these risk factors cannot rely on direct path-wise differentiation, while any approach involving finite differences is slow and shows very high statistical noise. We present ad hoc estimators which have empirically a much smaller variance while offering very low runtime overheads over the baseline computation of the price adjustment, regardless of the number of sensitivities of interest, by leveraging adjoint (i.e., backward) algorithmic differentiation in their implementation. These estimators allow for a generic copula-based dependence structure among the default events, and can be applied beyond our main application, to payoffs depending on more than two of them. We also discuss the conversion of the so-obtained sensitivities to model parameters (e.g., default intensities) into sensitivities to the market quotes used in calibration (e.g., Credit Default Swap spreads) by generalizations of an existing implicit-function based first-order algorithm.

The default of corporate bonds can result in large financial losses as well as irreparable harm to investors’ trust and the economy as a whole, which implies that the identification of corporate bond default must be done promptly and properly. Current studies mainly rely on accounting and/or macroeconomic data and use the credit rank (CR) to disclose the credit status of corporate bonds in the default prediction task. However, the textual data of credit rating reports (CRRs) contain richer and more comprehensive information and are neglected in related work. In this paper, we propose a novel framework that draws on the unstructured data in CRR to predict the default of corporate bonds. We extract the rating opinion sentences (categorized as positive and negative) from the collected CRR files and use latent Dirichlet allocation (LDA) models to mine topic information. The bilateral topic information of positive and negative opinions can reflect the anti-risk ability and potential risk of corporate bonds, respectively, based on which the constructed topic features are used for default prediction. Results on real-world Chinese corporate bonds dataset show that the bilateral topic information of CRR can significantly improve the predicting power of models (LR, SVM, KNN and MLP) under three performance metrics (AUC, KS and H-measure). By analyzing the ranking of topic features using SHAP value, the proposed framework can explain the factors that affect bond defaults, which can provide a basis for the decision-making of investment behavior.

In this paper, we study the optimal strategy arisen from the excess-of-loss reinsurance and asset allocation in defaultable markets under a general stochastic model. By developing the corresponding dynamic programming approach, we establish the optimal investment approach through two sub-problems: a pre-default case and a post-default case, respectively, characterized by the obtained Hamilton–Jacobi–Bellman (HJB) equations. We show the existence of a classical solution to the pre-default case via super-sub solution techniques and give an explicit characterization of the optimal reinsurance and investment policy that maximizes the common used utility associated with the terminal wealth. Verification theorem is further established to show the uniqueness of the corresponding solution of HJB equation that is critical to the desired optimal solution.

Using third-level industry data of CSI 300, we introduce the Lasso approach into the high-dimensional CARE to model the tail dependence network among the financial sector and real sectors and measure the industry systemic risk. We find that the realized systemic risks between industries are affected by individual risk exposures and marginal systemic risk contributions. Real estate’s marginal systemic risk contribution is the highest in real sectors, and the financial sector’s integrated finance industry shows vital marginal systemic risk contribution and network centrality. Auto parts, construction materials, metals and mining, construction engineering, electrical equipment, and means of transportation in real sectors strongly correlate with other industries. The capital markets industry has a tail dependence with 20 different industries.

In a dual risk model, the premiums are considered as the costs and the claims are regarded as the profits. The surplus can be interpreted as the wealth of a venture capital, whose profits depend on research and development. In most of the existing literature of dual risk models, the profits follow the compound Poisson model and the cost is constant. In this paper, we develop a state-dependent dual risk model, in which the arrival rate of the profits and the costs depend on the current state of the wealth process. Ruin probabilities are obtained in closed-forms. Further properties and results will also be discussed.

In the current regulatory environment, banks are required to quantify credit risk by means of default probabilities, loss rates conditional on default and expected exposures for a number of purposes: regulatory capital calculation, loan loss provisioning and stress testing. The nature of each credit risk parameter might be different for each application, e.g., forward looking default probabilities are needed for loan loss provisioning while regulatory capital is based on long-term averages. These different requirements for each purpose create a substantial burden especially for small and medium-sized banks. This paper describes a simple framework that allows the consistent calculation of credit risk parameters for all risk applications. It assumes that a bank is using a scorecard based on loan-level data where the data history might only span a couple of years. These data are combined with a macroeconomic model in a suitable way to derive risk parameters compliant with all regulatory requirements.

This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.

Based on the data of 30 provinces in China from 2013 to 2021, this paper constructs indicators of the level of development of the digital economy, and explores the impact of the digital economy on the high-quality development of the economy based on the perspective of regional heterogeneity, using static and dynamic panel models. It is concluded that (1) the digital economy promotes high-quality development of the economy, and the eastern part of the country is particularly significant; and (2) the digital economy can promote high-quality development of the economy by promoting the innovation capacity of provinces and cities and reducing their carbon emissions. Finally, we put forward the recommendations of “vigorously developing the digital economy; improving the infrastructure construction and talent training in each region; establishing a regional coordination and linkage mechanism; and improving the development strategy of the digital economy”, so as to promote the high-quality development of the economy.

This study makes an attempt to evaluate the impact of first and second waves of COVID-19 on the major global stock markets. Further, the study also analyzed the effect of COVID-19 on the sectoral indices of India during the first and second waves. In order to achieve the objective, the event study methodology has been used and abnormal returns are computed around the event dates. The findings of the study reveal that major stock markets in the world adversely affected during the first wave of COVID-19 and the highest abnormal loss was observed in Toronto Stock Exchange (7.55%) on the event date. The impact of second wave of COVID-19 was not as severe as first wave and highest abnormal loss (2.37%) was observed in Moscow Exchange of Russia. The Indian sectoral indices also showed high volatility during both the waves of COVID-19.

This study comprehensively explores the intricate dynamics between Microfinance (MF) scheme implementation and socio-economic development through rigorous empirical analysis. The focal point is to provide a well-structured, stakeholder-specific mitigation plan, empowering microfinance institutions and customers for judicious investment planning, catalyzing socio-economic development and ensuring scheme implementation efficacy. Socioeconomic development is examined specifically through the Pradhan Mantri Mudra Yojana, a prominent microfinance scheme in India that encompasses financial inclusion, employment generation, women empowerment and sustainable development. Indicators within each domain are meticulously categorized into push and pull classes, influencing customer engagement with MF schemes. Noteworthy indicators exert forces that repel or attract customers, creating a nuanced understanding. The Microfinance Scheme Effectiveness Index, computed through fuzzy logic theory, facilitates empirical assessment across states, stratifying them into levels of effective scheme implementation. Addressing ineffectiveness requires bespoke, state-specific development plans, necessitating a multi-dimensional approach involving diverse stakeholders. The study advocates for policy interventions linked to pivotal aspects such as women’s education, equal pay rights, start-up support, women representation promotion, customer training, awareness campaigns and enhanced governmental policies for structured customer involvement. In conclusion, despite robust planning systems, the effectiveness of scheme implementation faces challenges in a dynamic environment, requiring an iterative and adaptive monitoring approach.

This study examines the relations between leverage and investment and the relations between leverage and firm value during the COVID-19 period using data from Chinese listed companies. We find that the COVID-19 pandemic has strengthened the inhibition of leverage on corporate investment and firm value, while alleviated the constraint of leverage on corporate cash holdings. Furthermore, the pandemic-induced negative relationships are stronger for non-SOEs, firms holding less cash, multinational firms and firms in severe epidemic areas. Overall, our results are consistent with the risk-aversion theory. Higher economic uncertainty increases the firms’ risk aversion and eventually strengthens the negative relations between leverage and investment.