Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market

In this paper, we contribute to the topic of the non-performing loans (NPLs) business proftability on the secondary market by developing machine learning-based due diligence. In particular, a loan became non-performing when the borrower is unlikely to pay, and we use the ability of the ML algorithms to model complex relationships between predictors and outcome variables, we set up an ad hoc dependent random forest regressor algorithm for projecting the recovery rate of a portfolio of the secured NPLs. Indeed the proftability of the transactions under consideration depends on forecast models of the amount of net repayments expected from receivables and related collection times. Finally, the evaluation approach we provide helps to reduce the ”lemon discount” by pricing the risky component of informational asymmetry between better-informed banks and potential investors in particular for higher quality, collateralised NPLs

China’s Missing Pigs: Correcting China’s Hog Inventory Data Using a Machine Learning Approach

Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, Support Vector Regression has superior forecasting performance in small sample applications. In this article, we introduce Support Vector Regression via an application to China’s hog market. Since 2014, China’s hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use Support Vector Regression to predict the true inventory based on the price-inventory relationship before 2014. We show that, in this application with a small sample size, Support Vector Regression out-performs neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data

Time matters: How default resolution times impact final loss rates

Using access to a unique bank loss database, we find positive dependencies of default resolution times (DRTs) of defaulted bank loan contracts and final loan loss rates (losses given default, LGDs). Due to this interconnection, LGD predictions made at the time of default and during resolution are subject to censoring. Pure (standard) LGD models are not able to capture effects of censoring. Accordingly, their LGD predictions may be biased and underestimate loss rates of defaulted loans. In this paper, we develop a Bayesian hierarchical modelling framework for DRTs and LGDs. In comparison to previous approaches, we derive final DRT estimates for loans in default which enables consistent LGD predictions conditional on the time in default. Furthermore, adequate unconditional LGD predictions can be derived. The proposed method is applicable to duration processes in general where the final outcomes depend on the duration of the process and are affected by censoring. By this means, we avoid bias of parameter estimates to ensure adequate predictions

Disrupting Finance

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry

Disrupting Finance : FinTech and Strategy in the 21st Century

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry

Credit risk modelling for private firms under distressed economic and financial conditions: evidence from Zimbabwe.

Doctoral Degree. University of KwaZulu-Natal, Durban.Since the outburst of the recent 2007 - 2008 global financial and economic crisis, modelling of credit risk for private non-financial firms under economic and financial stress has been receiving a lot of regulatory and scientific attention the world over. Nevertheless, the quandary is that there seems to be no well-defined estimation procedures and industry consensus on how to incorporate economic downturn conditions in private firm credit risk models, which have led to the introduction of diverse default probability, exposure at default and rate of recovery prediction methodologies. Moreover, there is no consensus on which predictor variables have the most significant impact on private firm credit risk under downturn conditions. This study strives to design forecasting models in order to estimate key credit risk components (default probability, recovery rate and exposure at default) for private nonfinancial firms under downturn conditions in a developing economy. The main aim of the thesis is to identify and interpret the drivers of probability of default, recovery rate and credit conversion factor. In the first part, the study reviews literature using a scoping review framework in order to identify the reasons and motives for research, emerging trends and research gaps in modelling bankruptcy risk for private nonfinancial corporations in developing economies. The second part of the thesis creates stepwise logit models to detect the default probability for privately-owned non-financial corporates under downturn conditions in a developing country. In the third section of the study, stepwise logit models are designed to separately forecast probability of default for audited and unaudited privately-traded non-financial corporations under downturn conditions in a developing economy. The fourth part of the thesis develops stepwise Ordinary Least Squares regression models to predict workout recovery rates for defaulted bank loans for private non-financial corporates under downturn conditions in a developing market. In the fifth section of the study, stepwise Ordinary Least Squares regression models are developed to estimate the credit conversion factor to precisely predict, at the account level, the exposure at default for defaulted private nonfinancial corporations having credit lines under downturn conditions in a developing economy. To fit the models, the study adopts unique real-world data sets pooled from an anonymised major Zimbabwean commercial bank. This study finds that the forecasting of probability of bankruptcy for private non-financial corporates in developing economies is an appropriate discipline that has not been properly studied and has some distinctive and unexplored zones due to its complexity and the diverse business ethos of private firms. The thesis discovers that accounting information is imperative in predicting the default probability, rate of recovery and exposure at default for Zimbabwean private non-financial corporations under downturn conditions. Further, the study reveals evidence indicating that the forecasting results of the designed credit risk models are improved by incorporating macroeconomic variables. The incorporation of macroeconomic factors is vital since it enables stress testing and provides a way of modelling the default probability, recovery rate and exposure at default under downturn conditions. In light of these findings, it is recommended that firm and/or loan features, accounting information and macroeconomic factors should be adopted when predicting credit risk parameters for private non-financial corporates under downturn conditions in a developing country