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Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.

This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.

This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.

The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.

Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).

This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

Bankruptcy prediction has attracted a great deal of research in the data mining/machine learning community, due to its significance in the world of accounting, finance, and investment. This chapter examines the influence of different dimension reduction techniques on decision tree model applied to the bankruptcy prediction problem. The studied techniques are principal component analysis (PCA), sliced inversed regression (SIR), sliced average variance estimation (SAVE), and factor analysis (FA). To focus on the impact of the dimension reduction techniques, we chose only to use decision tree as our predictive model and “undersampling” as the solution to the issue of data imbalance. Our computation shows that the choice of dimension reduction technique greatly affects the performances of predictive models and that one could use dimension reduction techniques to improve the predictive power of the decision tree model. Also, in this study, we propose a method to estimate the true dimension of the data.

This paper discusses dynamic financial approaches to solvency analysis in non‐life insurance companies by explaining cash flow simulation models which are based on the planning of their typical cash inflows and outflows. Posits that these models take into account patterns of loss reserve run‐offs and asset cash flows by implementing several hypotheses that also include expectations about external economic conditions such as inflation rates and interest rates. Acknowledges the cash inflows and outflows have been planned over a period of time to evaluate how positive net cash flow (liquidity) leads to the increase in assets over liabilities (solvency).

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.

Legal solvency tests play a crucial role in high‐stakes financial transactions. This article presents a brief introduction to legal solvency tests that play important roles in bankruptcy and corporate law. The author then proceeds to analyze these tests from the perspective of financial economics, and argues that optimal solvency tests should be context‐dependent.

We propose a method for forecasting bank solvency that quantifies bank solvency as the probability that a bank will have more than 0.25 of the cash to total asset ratio. Predictor variables include the ratio of loans secured by farmland to total loans, the ratio of loans to farmers to total loans, and the ratio of commercial and industrial loans to total loans. Loans secured by farmland to total loans significantly predicted the potential for insolvency. To a secondary extent, commercial and industrial loans significantly predicted bank failure. This result was validated with predicted probabilities significantly explaining cash to total assets.

Credit scoring datasets are generally unbalanced. The number of repaid loans is higher than that of defaulted ones. Therefore, the classification of these data is biased toward the majority class, which practically means that it tends to attribute a mistaken “good borrower” status even to “very risky borrowers”. In addition to the use of statistics and machine learning classifiers, this paper aims to explore the relevance and performance of sampling models combined with statistical prediction and artificial intelligence techniques to predict and quantify the default probability based on real-world credit data.

A real database from a Tunisian commercial bank was used and unbalanced data issues were addressed by the random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). Performance was evaluated in terms of the confusion matrix and the receiver operating characteristic curve.

The results indicated that the combination of intelligent and statistical techniques and re-sampling approaches are promising for the default rate management and provide accurate credit risk estimates.

This paper empirically investigates the effectiveness of ROS and SMOTE in combination with logistic regression, artificial neural networks and support vector machines. The authors address the role of sampling strategies in the Tunisian credit market and its impact on credit risk. These sampling strategies may help financial institutions to reduce the erroneous classification costs in comparison with the unbalanced original data and may serve as a means for improving the bank’s performance and competitiveness.

This study aims to develop benchmarks of the financial performance of contractors and a decision support tool for evaluation, selection and appointment of contractors. The financial benchmarks allow contractors to know where they are relative to the best-performing contractors, and they can then take steps to improve their own performance. The decision support tool helps clients to decide which contractor should be awarded the project.

Financial data between 2013 and 2015 of 44 Singapore-based contractors were acquired from a Singaporean public agency. Benchmarks for Z-score and financial ratios were developed. A decision tree for evaluating contractors was constructed.

This study found that between 57% and 64% of contractors stayed in the financially healthy zone from 2013 to 2015. Ratios related to financial liabilities are relatively bad compared with international standards.

The limitation is that the data is obtained from a cross-sectional survey of contractors’ financial performance in Singapore over a three-year period. Regarding the finding that ratios relating to financial liabilities are weak, the implication is that contractors need to reduce their financial liabilities to achieve a good solvency profile. Contractors may use the benchmarks to check their financial performances relative to that of their competitors. To reduce financial risks, project clients may use these benchmarks to examine contractors’ financial performance.

This study provides benchmarks for contractors and clients to examine the financial performance of contractors in Singapore. A decision tree is provided to aid clients in making decisions on which contractors to appoint.

The purpose of this paper is to benchmark the solvency status of Indian general insurance firms.

The paper collects, compiles and analyses the key financial, operational and business data of eight Indian insurance firms. The authors first decide on initial firm‐specific economic variables and use data of last five years from IRDA Reports and Company Annual Reports. The NAIC IRIS ratios method was used to obtain an initial risk classification. This was used as a proxy of insolvency risk. Linear regression and logit techniques were thereafter applied to estimate the significant factors (direction‐wise and magnitude‐wise) which influence insurer solvency.

The results suggest that the factors that most significantly influence Indian non‐life insurers are lines of business, the firm's market share, the premium growth rate, the underwriting performance and the claims incurred. Further, the factors which have the strongest effect are market share, change in inflation rate, firm size, lines of business and claims incurred.

The sample of Indian general insurers used is limited with regard to the time span. No holdout sample was used and the entire data set was subjected to statistical analysis. These somewhat limit the findings and implications.

The paper provides insurers with easy‐to‐use operational and marketing indicators to benchmark their solvency risk. It will lead to competitive goal setting for continuous improvement. Estimation of appropriate market/economic parameters can be a useful input for regulators. A few suggested indicators are new.

Previous studies of insurance companies have focused on developed economies (USA, Europe) or the Asian Markets (China and Japan). This paper determines a set of marketing, financial and operational variables to predict benchmark financial strength of general insurance firms in India. It incorporates qualitative inputs from practising managers and industry experts before carrying out quantitative modeling and analysis.

During periods of recession or rapid expansion the need to monitor closely the performance of a business is essential and there are financial techniques available to do this. One such method, Multi Discriminant Analysis, has received considerable attention in accounting circles where opinions differ regarding its success. In this analysis eight companies from the leisure sector have been selected specifically to demonstrate varying levels of success. Financial data taken from the published accounts are then analysed using two widely recognised models. This preliminary research provides evidence that this technique has the potential for predicting failure in the leisure sector. Further research is now required to determine the effective lead time for such predictions and eventually to provide a model specifically for the service industries.