This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.

The state delivery of affordable and sustainable housing continues to be a complicated challenge in Africa, and there is a need to encourage private sector participation. As a result, this study examines the risks associated with private sector participation in affordable housing and supporting infrastructure investment and the strategies towards mitigating the risks from an Afrocentric perspective. The evidence from a systematic literature review was coupled with the opinion of an international expert panel to address the paper’s aim and provide recommendations for developing improved housing and supporting infrastructure in Sub-Saharan Africa. The review outcomes and the qualitative data from the panel discussion were analysed using thematic analysis. The results revealed that market dynamics, land supply and acquisition constraints, cost of construction materials, unsupportive policies, and technical and financial factors constitute risks to affordable housing in the region. Mitigation strategies include leveraging joint efforts, strengths, and resource bases, increasing access to land and finance for private sector participation, developing a supportive government framework to promote an enabling environment for easy access to land acquisition and development finance, local production of building materials, research and technology adoption. In line with the United Nations (UN) Agenda 2030 targets and principles, reforms are required across the housing value chain, involving the private sector and community. Application of the study’s recommendations could minimise the risks of affordable housing delivery and enhance private sector participation.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.