Results for autocorrelation for South Asia countries
Wooldridge test for autocorrelation in panel data in developing countries | |
---|---|
Model ROA | Model ROE |
= 111.092 | = 7.447 |
Prob > = 0.0000 | Prob > = 0.0138 |
Descriptive statistics
Variables | Mean | Maximum | Minimum | Std. dev. | Observations |
---|---|---|---|---|---|
ROA | 0.986 | 10.408 | −6.234 | 1.905 | 190 |
ROE | 7.964 | 100.158 | −268.759 | 39.175 | 190 |
NPL | 7.206 | 64.058 | 0.271 | 9.659 | 190 |
CAR | 13.885 | 39.130 | 1.050 | 4.183 | 190 |
CER | 27.920 | 68.696 | 13.050 | 9.808 | 190 |
ALR | 8.723 | 14.701 | 5.542 | 1.615 | 190 |
LR | 68.016 | 107.179 | 25.027 | 14.177 | 190 |
YES | 3.899 | 5.008 | 2.318 | 0.589 | 190 |
YES | 49.131 | 111 | 5 | 34.171 | 190 |
YES | 9.449 | 20.92 | 2.540 | 3.891 | 190 |
Correlation figures
Variable | ROA | ROE | NPL | CAR | CER | LR | ALR | SIZE | AGE | INF |
---|---|---|---|---|---|---|---|---|---|---|
ROA | 1 | |||||||||
ROE | 0.757 | 1 | ||||||||
NPL | −0.225 | −0.378 | 1 | |||||||
CAR | 0.184 | 0.156 | −0.284 | 1 | ||||||
CER | −0.170 | −0.195 | 0.058 | 0.407 | 1 | |||||
LR | 0.026 | 0.010 | −0.305 | −0.121 | −0.402 | 1 | ||||
ALR | 0.140 | 0.020 | 0.143 | 0.091 | 0.133 | −0.237 | 1 | |||
YES | 0.298 | 0.305 | −0.213 | −0.025 | −0.335 | 0.457 | −0.441 | 1 | ||
YES | −0.007 | 0.019 | −0.182 | −0.063 | −0.310 | 0.213 | −0.197 | 0.099 | 1 | |
YES | −0.013 | −0.171 | 0.163 | −0.019 | 0.024 | 0.152 | 0.520 | −0.162 | −0.135 | 1 |
ROA model (pooled regression and fixed effect GMM result)
Pooled regression | Generalized method of moments | |||||||
---|---|---|---|---|---|---|---|---|
Variable | Coefficient | Std. Error | -Statistic | Prob. | Coefficient | Std. Error | -Statistic | Prob. |
C | −5.449 | 1.754 | −3.105 | 0.002* | −7.098 | 1.959 | −3.621 | 0.000 |
NPL | −0.034 | 0.014 | −2.434 | 0.015** | −0.032 | 0.015 | −2.088 | 0.038** |
CAR | 0.080 | 0.033 | 2.417 | 0.016** | 0.085 | 0.036 | 2.329 | 0.021** |
CER | −0.043 | 0.015 | −2.824 | 0.005* | −0.048 | 0.016 | −2.972 | 0.003* |
ALR | 0.409 | 0.099 | 4.131 | 0.000* | 0.492 | 0.112 | 4.392 | 0.000* |
LR | −0.025 | 0.011 | −2.296 | 0.022** | −0.027 | 0.013 | −2.125 | 0.035** |
YES | 1.371 | 0.254 | 5.383 | 0.000* | 1.649 | 0.292 | 5.645 | 0.000* |
YES | −0.002 | 0.003 | −0.588 | 0.557 | −0.000 | 0.004 | −0.026 | 0.978 |
YES | −0.031 | 0.039 | −0.805 | 0.421 | −0.032 | 0.061 | −0.525 | 0.599 |
0.282 | 0.372 | |||||||
Adjusted | 0.250 | 0.358 | ||||||
S.E. of regression | 1.650 | 1.672 | ||||||
Durbin–Watson stat | 1.834 | 1.980 | ||||||
Hause test ( ) | 50.960 | |||||||
-value( ) | 0.000 |
Pooled regression | Generalized method of moments | |||||||
---|---|---|---|---|---|---|---|---|
Variable | Coefficient | Std. Error | -Statistic | Prob. | Coefficient | Std. Error | -Statistic | Prob. |
C | −16.229 | 39.885 | −0.407 | 0.685 | −26.740 | 44.139 | −0.606 | 0.546 |
NPL | −1.418 | 0.327 | −4.337 | 0.000* | −1.379 | 0.354 | −3.893 | 0.000* |
CAR | 1.261 | 0.713 | 1.769 | 0.079*** | 1.315 | 0.774 | 1.699 | 0.091*** |
CER | −1.035 | 0.350 | −2.953 | 0.004* | −1.032 | 0.375 | −2.754 | 0.007* |
LR | −0.464 | 0.232 | −2.000 | 0.047** | −0.463 | 0.262 | −1.768 | 0.079*** |
ALR | 3.981 | 2.037 | 1.954 | 0.052*** | 4.331 | 2.238 | 1.936 | 0.055*** |
YES | 16.309 | 6.355 | 2.566 | 0.011** | 18.481 | 7.001 | 2.640 | 0.009* |
YES | −0.113 | 0.105 | −1.082 | 0.281 | −0.097 | 0.118 | −0.824 | 0.411 |
YES | −1.623 | 0.734 | −2.211 | 0.028** | −1.867 | 0.857 | −2.178 | 0.031** |
0.234 | 0.265 | |||||||
Adjusted | 0.231 | 0.249 | ||||||
S.E. of regression | 30.442 | 29.378 | ||||||
Durbin–Watson stat | 1.340 | 1.511 | ||||||
Hause test ( ) | 18.183 | |||||||
-value ( ) | 0.000 |
South Asian countries (ROA) Variable Fixed Random Prob
South Asian countries (ROE) | |||
---|---|---|---|
Variable | Fixed | Random | Prob. |
NPLs | −1.381440 | −0.918537 | 0.9468 |
ALR | −1.535941 | 0.727034 | 0.1002 |
CAR | 0.008182 | −0.096010 | 0.6395 |
CER | −0.328610 | −0.232542 | 0.5979 |
LR | −0.012691 | −0.067185 | 0.0082 |
SG | 0.029615 | 0.088314 | 0.5599 |
SIZE | −12.082609 | 0.382527 | 0.1095 |
INFL | −0.390149 | 0.087808 | 0.8251 |
AGE | −0.687654 | −0.007677 | 0.2538 |
Extra tables and figures in the Google drop box and available at: https://www.dropbox.com/sh/dro0gkowf3t542r/AAC3QQ5lKQTpLdke7UNxRUEea?dl=0
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The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.
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Black box Artificial Intelligence (AI) is not suitable in regulated financial services. To overcome this problem, Explainable AI models, which provide details or reasons to make the functioning of AI clear or easy to understand, are necessary.
To develop such models, we first need to understand what “Explainable” means. Recently, some important insitutional definitions have been provided. For example, Bracke et al. ( 2019 ) states that “Explanations can answer different kinds of questions about a model's operation depending on the stakeholder they are addressed to and Croxson et al. ( 2019 )” ‘interpretability’ will be the focus will be the focus—generally taken to mean that an interested stakeholder can comprehend the main drivers of a model-driven decision".
Explainability means that an interested stakeholder can comprehend the main drivers of a model-driven decision; FSB ( 2017 ) suggests that “lack of interpretability and auditability of AI and Machine Learning (ML) methods could become a macro-level risk”; Croxson et al. ( 2019 ) establishes that “in some cases, the law itself may dictate a degree of explainability.”
The European GDPR EU ( 2016 ) regulation states that “the existence of automated decision-making should carry meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.” Under the GDPR regulation, the data subject is therefore, under certain circumstances, entitled to receive meaningful information about the logic of automated decision-making.
Finally, the European Commission High-Level Expert Group on AI presented the Ethics Guidelines for Trustworthy Artificial Intelligence in April 2019. Such guidelines put forward a set of seven key requirements that AI systems should meet in order to be deemed trustworthy. Among them three relate to the concept of “eXplainable Artificial Intelligence (XAI)” , and are the following.
Human agency and oversight: decisions must be informed, and there must be a human-in-the-loop oversight.
Transparency: AI systems and their decisions should be explained in a manner adapted to the concerned stakeholder. Humans need to be aware that they are interacting with an AI system.
Accountability: AI systems should develop mechanisms for responsibility and accountability, auditability, assessment of algorithms, data and design processes.
Following the need to explain AI models, stated by legislators and regulators of different countries, many established and startup companies have started to embrace Explainable AI models. In addition, more and more people are searching information about what “Explainable Artificial Intelligence” means.
In this respect, Fig. 1 represents the evolution of Google searches for explainable AI related terms.
From a mathematical viewpoint, it is well known that “simple” statistical learning models, such as linear and logistic regression models, provide a high interpretability but, possibly, a limited predictive accuracy. On the other hand, “complex” machine learning models, such as neural networks and tree models, provide a high predictive accuracy at the expense of a limited interpretability.
To solve this trade-off, we propose to boost machine learning models, that are highly accurate, with a novel methodology, that can explain their predictive output. Our proposed methodology acts in the post processing phase of the analysis, rather than in the preprocessing part. It is agnostic (technologically neutral) as it is applied to the predictive output, regardless of which model generated it: a linear regression, a classification tree or a neural network model.
The machine learning procedure proposed in the paper processes the outcomes of any other arbitrary machine learning model. It provides more insight, control and transparency to a trained, potentially black box machine learning model. It utilises a model-agnostic method aiming at identifying the decision-making criteria of an AI system in the form of variable importance (individual input variable contributions).
A key concept of our model is the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. To the best of our knowledge this is the only explainable AI approach rooted in an economic foundation. It offers a breakdown of variable contributions so that every data point (e.g. a credit or loan customer in a portfolio) is not only represented by input features (the input of the machine learning model) but also by variable contributions to the prediction of the trained machine learning model.
More precisely, our proposed methodology is based on the combination of network analysis with Shapley values [see Lundberg and Lee ( 2017 ), Joseph ( 2019 ), and references therein]. Shapley values were originally introduced by Shapley ( 1953 ) as a solution concept in cooperative game theory. They correspond to the average of the marginal contributions of the players associated with all their possible orders. The advantage of Shapley values, over alternative XAI models, is that they can be exploited to measure the contribution of each explanatory variable for each point prediction of a machine learning model, regardless of the underlying model itself [see, e.g. Lundberg and Lee ( 2017 )]. In other words, Shapley based XAI models combine generality of application (they are model agnostic) with the personalisation of their results (they can explain any single point prediction).
Our original contribution is to improve Shapley values, improving the interpretation of the predictive output of a machine learning model by means of correlation network models. To exemplify our proposal, we consider one area of the financial industry in which Artificial Intelligence methods are increasingly being applied: credit risk management [see for instance the review by Giudici ( 2018 )].
Correlation networks, also known as similarity networks, have been introduced by Mantegna and Stanley ( 1999 ) to show how time series of asset prices can be clustered in groups on the basis of their correlation matrix. Correlation patterns between companies can similarly be extracted from cross-sectional features, based on balance sheet data, and they can be used in credit risk modelling. To account for such similarities we can rely on centrality measures, following Giudici et al. ( 2019 ) , who have shown that the inclusion of centrality measures in credit scoring models does improve their predictive utility. Here we propose a different use of similarity networks. Instead of applying network models in a pre-processing phase, as in Giudici et al. ( 2019 ) , who extract from them additional features to be included in a statistical learning model, we use them in a post-processing phase, to interpret the predictive output from a highly performing machine learning model. In this way we achieve both predictive accuracy and explainability.
We apply our proposed method to predict the credit risk of a large sample of small and medium enterprises. The obtained empirical evidence shows that, while improving the predictive accuracy with respect to a standard logistic regression model, we improve, the interpretability (explainability) of the results.
The rest of the paper is organized as follows: Sect. 2 introduces the proposed methodology. Section 3 shows the results of the analysis in the credit risk context. Section 4 concludes and presents possible future research developments.
2.1 statistical learning of credit risk.
Credit risk models are usually employed to estimate the expected financial loss that a credit institution (such as a bank or a peer-to-peer lender) suffers, if a borrower defaults to pay back a loan. The most important component of a credit risk model is the probability of default, which is usually estimated statistically employing credit scoring models.
Borrowers could be individuals, companies, or other credit institutions. Here we focus, without loss of generality, on small and medium enterprises, whose financial data are publicly available in the form of yearly balance sheets.
For each company, n , define a response variable \(Y_{n}\) to indicate whether it has defaulted on its loans or not, i.e. \(Y_{n}=1\) if company defaults, \(Y_{n}=0\) otherwise. And let \(X_{n}\) indicate a vector of explanatory variables. Credit scoring models assume that the response variable \(Y_{n}\) may be affected (“caused”) by the explanatory variables \(X_{n}\) .
The most commonly employed model of credit scoring is the logistic regression model. It assumes that
where \(p_{n}\) is the probability of default for company n ; \({\mathbf {x}}_{n}=(x_{i,1},\ldots ,x_{i,J})\) is a J -dimensional vector containing the values that the J explanatory variables assume for company n ; the parameter \(\alpha \) represents an intercept; \(\beta _{j}\) is the j th regression coefficient.
Once the parameters \(\alpha \) and \(\beta _{j}\) are estimated using the available data, It the probability of default can be estimated, inverting the logistic regression model, from:
Alternatively, credit risk can be measured with Machine Learning (ML) models, able to extract non-linear relations among the financial information contained in the balance sheets. In a standard data science life cycle, models are chosen to optimise the predictive accuracy. In highly regulated sectors, like finance or medicine, models should be chosen balancing accuracy with explainability (Murdoch et al. 2019 ). We improve the choice selecting models based on their predictive accuracy, and employing a posteriori an algorithm that achieves explanability. This does not limit the choice of the best performing models.
To exemplify our approach we consider, without loss of generality, the Extreme Gradient Boost model, one of the most popular and fast machine learning algorithms [see e.g. Chen and Guestrin ( 2016 )].
Extreme Gradient Boosting (XGBoost) is a supervised model based on the combination of tree models with Gradient Boosting. Gradient Boosting is an optimisation technique able to support different learning tasks, such as classification, ranking and prediction. A tree model is a supervised classification model that searches for the partition of the explanatory variables that best classify a response (supervisor) variable. Extreme Gradient Boosting improves tree models strengthening their classification performance, as shown by Chen and Guestrin ( 2016 ). The same authors also show that XGBoost is faster than tree model algorithms.
In practice, a tree classification algorithm is applied successively to “training” samples of the data set. In each iteration, a sample of observations is drawn from the available data, using sampling weights which change over time, weighting more the observations with the worst fit. Once a sequence of trees is fit, and classifications made, a weighted majority vote is taken. For a more detailed description of the algorithm see, for instance (Friedman et al. 2000 ).
Once a default probability estimation model is chosen, it should be measured in terms of predictive accuracy, and compared with other models, so to select the best one. The most common approach to measure predictive accuracy of credit scoring models is to randomly split the available data in two parts: a “train” and a “test” set; build the model using data in the train set, and compare the predictions the model obtains on the test set, \(\hat{Y_n}\) , with the actual values of \(Y_n\) .
To obtain \(\hat{Y_n}\) the estimated default probability is rounded into a “default” or “non default”, depending on whether a threshold is passed or not. For a given threshold T , one can then count the frequency of the four possible outputs, namely: False Positives (FP): companies predicted to default, that do not; True Positives (TP): companies predicted to default, which do; False Negatives (FN): companies predicted not to default, which do; True Negatives (TN): companies predicted not to default, which do not.
The misclassification rate of a model can be computed as:
and it characterizes the proportion of wrong predictions among the total number of cases.
The misclassification rate depends on the chosen threshold and it is not, therefore, a generally agreed measure of predictive accuracy. A common practice is to use the Receiver Operating Characteristics (ROC) curve, which plots the false positive rate (FPR) on the Y axis against the true positive rate (TPR) on the X axis, for a range of threshold values (usually percentile values). FPR and TPR are then calculated as follows:
The ideal ROC curve coincides with the Y axis, a situation which cannot be realistically achieved. The best model will be the one closest to it. The ROC curve is usually summarised with the Area Under the ROC curve value (AUROC), a number between 0 and 1. The higher the AUROC, the better the model.
We now explain how to exploit the information contained in the explanatory variables to localise and cluster the position of each individual (company) in the sample. This information, coupled with the predicted default probabilities, allows a very insightful explanation of the determinant of each individual’s creditworthiness. In our specific context, information on the explanatory variables is derived from the financial statements of borrowing companies, collected in a vector \({\mathbf {x}}_{n}\) , representing the financial composition of the balance sheet of institution n .
We propose to calculate the Shapley value associated with each company. In this way we provide an agnostic tool that can interpret in a technologically neutral way the output from a highly accurate machine learning model. As suggested in Joseph ( 2019 ), the Shapley values of a model can be used as a tool to transfer predictive inferences into a linear space, opening a wide possibility of applying to them a variety of multivariate statistical methods.
We develop our Shapley approach using the SHAP Lundberg and Lee ( 2017 ) computational framework, which allows to estimate Shapley values expressing predictions as linear combinations of binary variables that describe whether each single variable is included or not in the model.
More formally, the explanation model \(g(x')\) for the prediction f ( x ) is constructed by an additive feature attribution method, which decomposes the prediction into a linear function of the binary variables \(z' \in \{0,1\}^M\) and the quantities \(\phi _i \in {\mathbb {R}}\) :
In other terms, \(g'(z')\approx f(h_x (z'))\) is a local approximation of the predictions where the local function \(h_x (x')=x\) maps the simplified variables \(x'\) into x , \(z'\approx x\) and M is the number of the selected input variables.
Indeed, Lundberg and Lee ( 2017 ) prove that the only additive feature attribution method that satisfies the properties of local accuracy , missingness and consistency is obtained attributing to each feature \(x'_i\) an effect \(\phi _i\) called Shapley value, defined as
where f is the trained model, x the vector of inputs (features), \(x'\) the vector of the M selected input features. The quantity \(f_x(z') - f_x(z' {\setminus } i) \) is the contribution of a variable i and expresses, for each single prediction, the deviation of Shapley values from their mean.
In other words, a Shapley value represents a unique quantity able to construct an explanatory model that locally linearly approximate the original model, for a specific input x ,( local accuracy ). With the property that, whenever a feature is locally zero, the Shapley value is zero ( missingness ) and if in a second model the contribution of a feature is higher, so will be its Shapley value ( consistency ).
Once Shapley values are calculated, we propose to employ similarity networks, defining a metric that provides the relative distance between companies by applying the Euclidean distance between each pair \(({\mathbf {x}}_{i},{\mathbf {x}}_{j})\) of company predicted vectors, as in Giudici et al. ( 2019 ).
We then derive the Minimal Spanning Tree (MST) representation of the companies, employing the correlation network method suggested by Mantegna and Stanley ( 1999 ). The MST is a tree without cycles of a complex network, that joins pairs of vertices with the minimum total “distance”.
The choice is motivated by the consideration that, to represent all pairwise correlations between N companies in a graph, we need \(N*(N-1)/2\) edges, a number that quickly grows, making the corresponding graph not understandable. The Minimal Spanning Tree simplifies the graph into a tree of \(N-1\) edges, which takes \(N-1\) steps to be completed. At each step, it joins the two companies that are closest, in terms of the Euclidean distance between the corresponding explanatory variables.
In our Shapley value context, the similarity of variable contributions is expressed as a symmetric matrix of dimension n × n , where n Is the number of data points in the (train) data set. Each entry of the matrix measures how similar or distant a pair of data points is in terms of variable contributions. The MST representation associates to each point its closest neighbour. To generate the MST we have used the EMST Dual-Tree Boruvka algorithm, and its implementation in the R package “emstreeR”.
The same matrix can also be used, in a second step, for a further merging of the nodes, through cluster analysis. This extra step can reveal segmentations of data points with very similar variable contributions, corresponding to similar credit scoring decision making.
We test our proposed model to data supplied by European External Credit Assessment Institution (ECAI) that specializes in credit scoring for P2P platforms focused on SME commercial lending. The data is described by Giudici et al. ( 2019 ) to which we refer for further details. In summary, the analysis relies on a dataset composed of official financial information (balance-sheet variables) on 15,045 SMEs, mostly based in Southern Europe, for the year 2015. The information about the status (0 = active, 1 = defaulted) of each company one year later (2016) is also provided. The proportion of defaulted companies within this dataset is 10.9%.
Using this data, Giudici et al. ( 2019 ) have constructed logistic regression scoring models that aim at estimating the probability of default of each company, using the available financial data from the balance sheets and, in addition, network centrality measures that are obtained from similarity networks.
Here we aim to improve the predictive performance of the model and, for this purpose, we run an XGBoost tree algorithm [see e.g. Chen and Guestrin ( 2016 )]. To explain the results from the model, typically highly predictive, we employ similarity network models, in a post-processing step. In particular, we employ the cluster dendrogram representation that corresponds to the application of the Minimum Spanning Tree algorithm.
We first split the data in a training set (80%) and a test set (20%), using random sampling without replacement.
We then estimate the XGBoost model on the training set, apply the obtained model to the test set and compare it with the best logistic regression model. The ROC curves of the two models are contained in Fig. 1 below.
Receiver Operating Characteristic (ROC) curves for the logistic credit risk model and for the XGBoost model. In blue, we show the results related to the logistic models while in red we show the results related to the XGBoost model
From Fig. 1 note that the XGBoost clearly improves predictive accuracy. Indeed the comparison of the Area Under the ROC curve (AUROC) for the two models indicate an increase from 0.81 (best logistic regression model) to 0.93 (best XGBoost model).
We then calculate the Shapley value explanations of the companies in the test set, using the values of their explanatory variables. In particular, we use TreeSHAP method (Lundberg et al. 2020 ) [see e.g. Murdoch et al. ( 2019 ); Molnar 2019 )] in combination with XGBoost. The Minimal Spanning Tree (a single linkage cluster) is used to simplify and interpret the structure present among Shapley values. We can also "colour" the MST graph in terms of the associated response variables values: default, not default.
Figures 2 and 3 present the MST representation. While in Fig. 3 company nodes are colored according to the cluster to which they belong, in Fig. 4 they are colored according to their status: not defaulted (grey); defaulted (red).
Minimal Spanning Tree representation of the borrowing companies. Companies are colored according to their cluster of belonging
In Fig. 2 , nodes are colored according to the cluster in which they are classified. The figure shows that clusters are quite scattered along the correlation network.
To construct the colored communities in Fig. 2 , we used the algorithm implemented in the R package “igraph” that directly optimizes a modularity score. The algorithm is very efficient and easily scales to very large networks (Clauset et al. 2004 ).
In Fig. 3 , nodes are colored in a simpler binary way: whether the corresponding company has defaulted or not.
Minimal Spanning Tree representation of the borrowing companies. Clustering has been performed using the standardized Euclidean distance between institutions. Companies are colored according to their default status: red = defaulted; grey = not defaulted
From Fig. 3 note that default nodes appear grouped together in the MST representation, particularly along the bottom left branch. In general, defaulted institutions occupy precise portion of the network, usually to the leafs of the tree, and form clusters. This suggests that those companies form communities, characterised by similar predictor variables’ importances. It also suggests that not defaulted companies that are close to default ones have a high risk of becoming defaulted as well, being the importance of their predictor variables very similar to those of the defaulted companies.
To better explain the explainability of our results, in Fig. 4 we provide the interpretation of the estimated credit scoring of four companies: two that actually defaulted and two that did not.
Contribution of each explanatory variable to the Shapley’s decomposition of four predicted default probabilities, for two defaulted and two non defaulted companies. The more red the color the higher the negative importance, and the more blue the color the higher the positive importance
Figure 4 clearly shows the advantage of our explainable model. It can indicate which variables contribute more to the prediction of default. Not only in general, as is typically done by statistical and machine learning models, but differently and specifically for each company in the test set. Indeed, Fig. 4 clearly shows how the explanations are different (“personalised”) for each of the four considered companies.
The most important variables, for the two non defaulted companies (left boxes) regard: profits before taxes plus interests paid, and earnings before income tax and depreciation (EBITDA), which are common to both; trade receivables, for company 1; total assets, for company 2.
Economically, a high proficiency decreases the probability of default, for both companies; whereas a high stock of outstanding invoices, not yet paid, or a large stock of assets, helps reducing the same probability.
On the other hand, Fig. 4 shows that the most important variables, for the two defaulted companies (right boxes) concern: total assets, for both companies; shareholders funds plus non current liabilities, for company 3; profits before taxes plus interests paid, for company 4.
In other words, lower total assets coupled, in one case, with limited shareholder funds and, in the other, with low proficiency, increase the probability of default of these two companies.
The above results are consistent with previous analysis of the same data: both Giudici et al. ( 2019 ) select, as most important variables in several models, the return on equity, related to both EBITDA and profit before taxes plus interests paid; the leverage, related to total assets and shareholders’ funds; and the solvency ratio, related to trade payables.
We remark that Fig. 4 contains a “local” explanation of the predictive power of the explanatory variables, and it is the most important contribution of Shapley value theory. If we average Shapley values across all observations we get an “overall” or “global” explanation, similar to what already available in the statistical and machine learning literature. Figure 5 below provides the global explanation in our context: the ten most important explanatory variables, over the whole sample.
Mean contribution of each explanatory variable to the Shapley’s decomposition. The more red the color the higher the negative importance, and the more blue the color the higher the positive importance
From Fig. 5 note that total assets to total liabilities (the leverage) is the most important variable, followed by the EBITDA, along with profit before taxes plus interest paid, measures of operational efficiency; and by trade receivables, related to solvency, in line with the previous comments.
The need to leverage the high predictive accuracy brought by sophisticated machine learning models, making them interpretable, has motivated us to introduce an agnostic, post-processing methodology, based on correlation network models. The model can explain, from a substantial viewpoint, any single prediction in terms of the Shapley value contribution of each explanatory variables.
For the implementation of our model, we have used TreeSHAP, a consistent and accurate method, available in open-source packages. TreeSHAP is a fast algorithm that can compute SHapley Additive exPlanation for trees in polynomial time instead of the classical exponential runtime. For the xgboost part of our model we have used NVIDIA GPUs to considerably speed up the computations. In this way, the TreeSHAP method can quickly extract the information from the xgboost model.
Our research has important policy implications for policy makers and regulators who are in their attempt to protect the consumers of artificial intelligence services. While artificial intelligence effectively improve the convenience and accessibility of financial services, they also trigger new risks. Our research suggests that network based explainable AI models can effectively advance the understanding of the determinants of financial risks and, specifically, of credit risks. The same models can be applied to forecast the probability of default, which is critical for risk monitoring and prevention.
Future research should extend the proposed methodology to other datasets and, in particular, to imbalanced ones, for which the occurrence of defaults tends to be rare, even more than what observed for the analysed data. The presence of rare events may inflate the predictive accuracy of such events [as shown in Bracke et al. ( 2019 )]. Indeed, Thomas and Crook ( 1997 ) suggests to deal with this problem via oversampling and it would be interesting to see what this implies in the proposed correlation network Shapley value context.
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This research has received funding from the European Union’s Horizon 2020 research and innovation program “FIN-TECH: A Financial supervision and Technology compliance training programme” under the Grant Agreement No 825215 (Topic: ICT-35-2018, Type of action: CSA), and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.750961. Firamis acknowledges the NVIDIA Inception DACH program for the computational GPU resources. In addition, the Authors thank ModeFinance, a European ECAI, for the data; the partners of the FIN-TECH European project, for useful comments and discussions. The authors also thank the Guest Editor, and two anonymous referees, for the useful comments and suggestions.
Open access funding provided by Università degli Studi di Pavia within the CRUI-CARE Agreement.
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Niklas Bussmann, Dimitri Marinelli and Jochen Papenbrock have been, or are, employed by the company FIRAMIS. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The paper is the result of a close collaboration between all four authors. However, JP is the main reference for use case identification, method and process ideation and conception as well as fast and controllable implementation, whereas PG is the main reference for statistical modelling, literature benchmarking and paper writing.
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Bussmann, N., Giudici, P., Marinelli, D. et al. Explainable Machine Learning in Credit Risk Management. Comput Econ 57 , 203–216 (2021). https://doi.org/10.1007/s10614-020-10042-0
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Credit management is the process of granting credit, terms and conditions definition, compliance with credit policy, and then payment on the due date. The core business for financial institutions is to improve revenues and profit by facilitating sales and reducing loss and financial risks. This research is to assess the effectiveness of credit management principles and their impact on loan performance for a specific type of loan “microcredit”. The case study is microcredit in East Africa especially provided by commercial banks in partnership with mobile network operators. The purpose of the research will be mainly to assess the applicability of credit management principles to achieve better performance in microlending. This research's target sample is East Africa commercial banks in conjunction with mobile operators providing online microcredit to mobile money subscribers and commercial banks ‘customers.
Keywords: credit management, microcredit, loan performance, commercial bank, mobile network operator
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This study aims to identify risk management strategies undertaken by the commercial banks of Balochistan, Pakistan, to mitigate or eliminate credit risk. The findings of the study are significant as commercial banks will understand the effectiveness of various risk management strategies and may apply them for minimizing credit risk. This explanatory study analyses the opinions of the employees of selected commercial banks about which strategies are useful for mitigating credit risk. Quantitative data was collected from 250 employees of commercial banks to perform multiple regression analyses, which were used for the analysis. The results identified four areas of impact on credit risk management (CRM): corporate governance exerts the greatest impact, followed by diversification, which plays a significant role, hedging and, finally, the bank’s Capital Adequacy Ratio. This study highlights these four risk management strategies, which are critical for commercial banks to resolve their credit risk.
Credit risk causes economic downturn as banks fail due to default risk from clients, which has had a negative impact on the economic development of many nations around the world (Reinhart & Rogoff, 2008 ). By definition, credit risk describes the risk of default by a borrower who fails to repay the money borrowed. The term hedging signals the protection of a business’s investments by limiting its level of risk, for example, by purchasing an insurance policy. Diversification is the allocation of financial resources in variety of different investments and has also long been understood to minimize such risk. The capital adequacy ratio is a measure of a bank’s capital maintained to absorb its outlying risks. Since there is a lot of competition among banks to attract customers, therefore, it has triggered several innovations in banking services (Aruwa & Musa, 2014 ). Regulators also require banks to improve internal governance practices in order to ensure transparency and ethical standards to keep the customers satisfied with their products and services. Ambiguity in banks’ terms and conditions will make it difficult for customers to select financial products appropriate for their needs, whereas clear terms and conditions allow customers to be more satisfied with the bank’s performance (Ho & Yusoff, 2009 ). Customers expect the financial institutions to have strong policies that can safeguard their interests and protect them. Therefore, poor understanding of effective credit risk and the acceptable risk management strategies by bank managers poses a threat to the commercial banks advancement and customers’ interest.
One critical success factor for financial institutions lies in their realization of the importance of credit risk and devising solid strategies – such as hedging, diversification and managing their capital adequacy ratio – to avoid shortcomings that could lead to operational catastrophe. Credit risks faced by banks have fundamental impact on the performance because, even few large customers default on loans would cause huge problems for it. The objective of the Credit Risk Management (CRM) process is to maximize the cost-adjusted rate of return of a particular bank by maintaining exposure to credit risk acceptable to its shareholders. Banks have to navigate the credit risk associated with the overall portfolio as well as external risks that may be due to macroeconomic factors in the economy. Banks must also compare the credit risk relationships with other risks. Another specific case of credit risk applies to the method of trying to settle banking transactions. Until and unless both parties settle their payments in a timely manner, bank suffers from opportunity loss. Corporate governance may also have large effect on the risk management strategies used by the bank for reducing credit risks. Research suggests that it is imperative that banks engage in prior planning in order to avoid future problems (Andrews, 1980 ).
Majority of commercial banks provide several services that could help them mitigate or manage risk. For example, hedging has been used to reduce the level of risk involved in transactions by keeping specific conditions that would allow different parties to exchange goods or services at a flexible date and time (Harrison & Pliska, 1981 ). The significance of effective risk management strategies have been highlighted by many researchers and practitioners over time to assist banks and other financial institutions. CRM became an obvious necessity for commercial banks, especially after the 2008 global financial crisis, in which it was primarily subprime mortgages that caused a liquidity crisis (Al-Tamimi, 2008 ). According to Al-Tamimi ( 2008 ), ensuring the efficient practice of risk management may not be expensive but the implementation should be done in a timely manner in order to ensure smooth banking operations.
A financial institution, just like a constituent part of any other major economic sector, aims to meet incurred expenses, increase the return on invested capital and maximize the wealth of its shareholders. In their pursuance of these objectives, the financial system has to offer effective risk management strategies to financial institutions like banks against credit risk (Hakim & Neaime, 2005 ).
In 2008, across the world, the credit crisis began as a result of mass issuing of sub-prime mortgages to individuals in the United States leading to defaults, which caused outwardly-rippling problems for financial institutions all across the world. Sub-prime mortgages and other loans with less restrictions can generate remarkable losses including corporate failure and bankruptcy for financial institution (Brown & Moles, 2014 ). These credit decisions have a pivotal role in firms’ profitability. The decision to over-extend credit to high-risk customers may increase short-term profitability for individual banks, though in aggregate, this lending behavior was seen to become a major challenge to the risk management structures of the economy as a whole. Therefore, managing risk is the most important element of a bank’s operations. This phenomenon is equally applicable to banks across the globe, including banks in Pakistan.
Due to unstable and volatile nature of the political and financial environment in Pakistan, banks are affected by many types of risk, including risks to foreign exchange rates, liquidity, operations, credit and interest rates. Pakistan’s financial institutions are generally risk-averse, especially towards car financing and mortgage loans in which chances of huge losses are higher (Shafiq & Nasr, 2010 ). Balochistan is the least developed part with largest geographical area in Pakistan. There are limited opportunities for small businesses and majority of businesses are run in informal form with poor documentation. Majority of commercial banks face problems like loan documents verification and loan processing. Therefore, the adoption of proper risk management strategies can help understand and mitigate the credit risk faced by commercial banks of Balochistan.
This study aims to identify the different risk management strategies that can influence the management of credit risk by commercial banks. We expect to determine if these strategies contribute both to the reduction of credit risk as well as the efficient performance in fulfilling customer needs.
This study aims to provide a basis for guidance for the commercial banks of Balochistan to adopt long-term performance-improving risk management strategies (Campbell, 2007 ). The model for the study shows the impact of risk management strategies, including hedging, diversification, the capital adequacy ratio and corporate governance. The research will also examine the impact of each risk management strategy individually in order to understand the importance of each strategy. To the best of authors’ knowledge, there is no study on credit risk management on Balochistan using the described parameters. The findings of this study are intended to contribute positively to society by demonstrating that the banks of Balochistan can develop effective strategies to improve their CRM. Additionally, policy makers can identify and generate appropriate policies to govern bank behavior in order to minimize risk.
Credit risk is considered as the chance of loss that will occur when the loan or any other line of credit by a particular debtor is not repaid (Campbell, 2007 ). Since 2008, financial experts around the world have researched and analyzed the primary factors underpinning the credit crisis to identify problematic behavior and effective solutions that can help financial institutions avoid catastrophe in the future. Long ago, the Basel Committee on Banking Supervision Footnote 1 (1999) has also identified credit risk as potential threat to banking sector and developed certain banking regulations that must be maintained by the banks around the world. Owojori, Akintoye, and Adidu ( 2011 ) stated that there are legislative inadequacies in financial system especially in banking system that are effective as well as lack of uniform credit information sharing amongst banks. Thus, it urges to the fact that banks need to emphasize on better risk management strategies which may protect them in the long run.
Abiola and Olausi ( 2014 ) emphasized on the establishment of a separate credit unit at banks with professional staff for credit/loan officers and field officers. It is important as they perform variety of functions from project appraisals through credit disbursement, loan monitoring to loans collection. Therefore, a comprehensive human resource policy related their selection, training, placement, job evaluation, discipline, and remuneration need to be in placed to avoid any inefficiencies related to loan management and credit defaults.
Ho and Yusoff ( 2009 ) focused on researching Malaysian financial institutions and their management of credit risk. The study involved a sample of 15 foreign and domestic financial institutions from which the data was collected through questionnaires. The findings demonstrated that the diversification of loan services leads to risk improvement, though it requires training employees and the commitment of employees to ensure that the financial institution will meet the requirements for best practice lending.
Brown and Wang ( 2002 ) conducted study about the challenges faced by Australian financial institutions due to credit risk over the period January 1986 to August 1993. The Australian financial institutions were not able to provide a wide variety of alternatives to their clients that led to higher risks as there was a lack of diversification in their services. The research suggested that corporate governance practices allow firms to adopt appropriate rules, policies, and procedures to ensure that the rights of all the stakeholders are fulfilled. Hedging Footnote 2 is used by financial institutions to minimize the risk associated with the transactions conducted with the bank customers as it allows the bank to minimize the risk by offering flexible offers that allows customer to make their decisions effectively (Dupire, 1992 ).
The work of Karoui and Huang ( 1997 ) indicates that the super hedging strategy Footnote 3 could be implemented to achieve a surplus downside market risk as it possesses a duality of both the super hedging and open hedging approaches. The prices of options can increase due to the volatility of the asset prices. If the prices of the financial instrument are fluctuating, then the price of the options contract might also be influenced as the buyers or sellers will be deriving their profit from the price of the financial security (Hobson, 1998 ).
Several factors are associated with the pricing of securities as these factors support the financial decisions that must be made by the investors. The loans that the bank provides to the borrower are highly dependent on the conditions of the market. Decision-making for mitigation and management of credit risk is very important for banks (Li, Kou, & Peng, 2016 ). A highly volatile security market will influence the prices and interest rates of the securities being exchanged in such a market. Financial markets are affected by the macroeconomic variables that influence the prices of the securities being exchanged. Hedging allows firms and their managers to incorporate policies that will maximize the value of the company as clients have a wide array of alternatives that allow them to make their decisions in an effective manner. The derivatives such as options, futures, forwards and swaps that are used by firms increase their financial stability by allowing the customers to have sufficient information that improves their decision making in different circumstances. This enables managers to adopt practices that will benefit their organizations. Hedging allows businesses to support a higher debt load due to its flexible nature and ability to minimize risk, which increases the value of the company as it can actually meet the needs of more customers with a comparatively lower level of risk (Graham & Rogers, 2002 ). Similarly, Levitt ( 2004 ) explained that hedging enables firms to extend its activities because the risk inherent to providing funds is reduced in such transactions, allowing more flexibility to all involved parties.
Banks are able to maintain a particular level of reserved cash for the sake of managing the day to day operations that is decided based on the allocated capital adequacy ratio. This enables the bank to maintain a balance of cash that is sufficient to meet the needs of the customers. Managers can use the bank’s available cash flow to meet short-term cash requirement needs, which are based on the concept of capital adequacy ratio. This gives certainty to some funds that banks must maintain in order to address unforeseen circumstances. The selective hedging concept has been used by firms for the sake of making investments that are based on a certain part of their portfolio that pose the most threat and not the entire portfolio of the financial instruments (Stulz, 1996 ). The emphasis is on utilizing hedging at the right time for the specific customer that a company believes should be entering into a contract with flexible terms and conditions. It is a viable option for banks to use hedging to avoid customers’ dissatisfaction for those who do not meet the firm’s loan eligibility criteria. Zhang, Kou & Peng, ( 2019 ) proposed a consensus model that considers the cost and degree of consensus in the group decision making process. With a certain degree of consensus the generalized soft cost consensus model was developed by defining the generalized aggregation operator and consensus level function. The cost is properly reviewed from the perspective of the individual experts and the moderator. Economic significance of the two soft consensus cost models is also assessed. The usability of the model for the real-world context is checked by applying it to a loan consensus scenario that is based on online data from a lending platform. Group decision making is critical for changing the opinions of everyone to arrive at a synchronized strategy for minimizing the risks of the bank with the help of hedging (Zhang, Kou, & Peng, 2019 ).
Kou, Chao, Peng, Alsaadi & Herrera-Viedma, ( 2019 ) identified that financial systemic risk is a major issue in financial systems and economics. Machine learning methods are employed by researchers that are trying to respond to systemic risks with the help of financial market data. Machine learning methods are used for understanding the outbreak and contagion of the systemic risk for improving the current regulations of the financial market and industry. The paper studies the research and methodologies on measurement of financial systemic risk with the help of big data analysis, sentiment analysis and network analysis. Machine learning methods are used along with systematic financial risk management for controlling the overall risks faced by the banks that are related to hedging of the financial instruments of the bank (Kou, Chao, Peng, Alsaadi, & Herrera-Viedma, 2019 ).
Provision of financial assistance to customers that require the funds for business activity can prove profitable for the bank (Datta, Rajagopalan, & Rasheed, 1991 ). If the principle and interest of the loan is repaid in a timely manner that would help the banks ensure smooth flow of their operations, and the economic activities in the society are improved as the standard of living of people also improves with such financial assistance that is provided by commercial banks (Keats, 1990 ). As banks enter into such contracts with several customers, the level of the its incurred risk increases; management likewise becomes more complex with a more diverse group of customers (Kargi, 2011 ). Non-Performing Loans (NPL) represent the credit that a bank believes is causing a loss, and includes loan defaults, which are typically categorized by their expectation of recovery as “standard,” “doubtful” or “lost” (Kolapo, Ayeni, & Oke, 2012 ). The lost category focusing on the inability of the bank to recover particular products restricts a bank from reaching the set targets thus causing a bank to fail in attaining the objectives of profitability that have been set. The incurrence of a large amount of high-risk debt is often difficult for banks to manage unless the managers have undertaken appropriate strategies for mitigating the risk in addition to enhancing their financial performance. The existence of NPLs prompted central global banks to enter into the 1988 Basel Accord, also known as Basel I (later superseded in 2004 by Basel II), which maintained that banks must maintain a particular amount of capital in order to meet their operational needs (Van Greuning & Brajovic Bratanovic, 2009 ). This on-hand capital requirement, also called the capital adequacy ratio, is beneficial as it allows banks to more easily manage potential, sudden financial losses (Keats, 1990 ).
Kithinji ( 2010 ) provides specific evidence that the management of credit risk does not influence the profitability of banks in Kenya. In fact, the Kargi ( 2011 ) study on Nigerian banks from 2004 to 2008 revealed a healthy relationship between appropriate CRM (Credit Risk Management) and bank performance. Poudel ( 2012 ) emphasized the significant role played by CRM in the improvement of financial performance of banks in Nepal between 2001 and 2011. Strict requirements of maintaining higher capital that is around 14.3% of the cash balance as reserve in the banks of Nepal was found to have resulted in better bank performance by producing more profit.
Heffernan ( 1996 ) stated that CRM is crucial for predicting proper bank financial performance. A bank’s inability to recoup its outstanding loans reduces its ability to engage in other profitable transactions A loss both of principle as well as interest (including time value) means also a loss in opportunities to expand and pursue other profitable operations (Berríos, 2013 ).
Banks that avoid risk management face several challenges, including their own survival in the current highly competitive financial environment. To compete successfully with other commercial financial institutions, banks rely on a diversification of products and financial services to improve portfolio performance, including attracting more customers. Diversified services allow customers to select the most appropriate financial assistance in light of their individual needs. Along with diversification of the financial services, banks need to manage the credit risk involved where funds are given as loans for various needs of the customers such as car loans, house loans, starting a new business or expanding ongoing business (Kou, Ergu, Lin, & Chen, 2016). It is also important to have effective behavior monitoring models to ensure that bank employees are careful in minimizing the operational risks by providing maximum information to the customers about the financial instruments and the restrictions imposed by the bank for the sake of protecting the interests of the financial institution. Chao, Kou, Peng & Alsaadi, ( 2019 ) conducted a study to understand a new form of money laundering that is trade based which is using the signboard of international trade. It appears along with the capital movement that is mostly concerned with the rise in the collapse of the overall financial market. It is difficult to prevent money laundering since it has a plausible sort of trade characterization. The aim of the paper is to develop monitoring methods that have accurate recognition along with classified form of supervision of the trade based money laundering with the help of multi class knowledge driven classification algorithms that are linked with the micro and macro prudential regulations. Based on an empirical study from China the application is reviewed and the effectiveness is assessed in order to improve the efficiency of the management in the financial markets (Chao, Kou, Peng, & Alsaadi, 2019).
Selecting the most eligible customers for a loan is also essential to managing credit risk: a bank can screen through a list of customers to identify the ones who have a higher probability of repayment within the specified time duration, according to the terms and conditions of the contract. Hentschel and Kothari ( 1995 ) emphasized that using different derivatives is significant for the leverage of the financial institution. A vast majority of companies surveyed were using derivatives to reduce their risk (Kou, Peng, & Wang, 2014 ). Dolde, ( 1993 ) highlighted that several banks are vulnerable to various risks, therefore, banks have undertaken specific precautionary measures like training their employees, developing better credit policies and reviewing the credit rating of the customers applying for the loans (Dolde, 1993 ).
Diversification is adopted by corporations for increasing the returns of the shareholders and minimizing risk. Decision-making criteria is improved by using classifiers that have some algorithms for resolving problems (Kou, Lu, Peng, & Shi, 2012 ). Rumelt ( 1974 ) revealed that only around 14% of firms on the Fortune 500 list were working as single business organizations in 1974, whereas 86% of the businesses operated in diversified product markets. This shows a considerable inclination of the business sector to emphasize diversification instead of single trade. Much research has been conducted focusing on the activities of companies during recent times; most have found a rise in the prevalence of diversified firms (Datta et al., 1991 ).
The first hypothesis considers assessing the role of hedging in reducing a bank’s credit. Based on a model presented by Felix ( 2008 ), which showed risk management strategies of hedging, capital adequacy ratio and diversification may be used to explain credit risk that a bank faces. Thus our first hypothesis is as follows:
The second risk management strategy is diversification, which requires banks to provide a wide range of financial services with flexible terms to customers and to provide credit to a wide range of customers instead of few in order to reduce risk (Fredrick, 2013 ). The concept of diversification can be used by banks as they create a wide customer pool for providing loans, instead of providing large amount of loans to few customers, which inherently increases risk (Hobson, 1998 ). Therefore,
The third hypothesis considers management strategy that requires banks to maintain a particular amount of the capital (Ho & Yusoff, 2009 ). The capital adequacy ratio is critical for banks to be in a better position to manage unexpected risks and thus capital maintained in a bank has a consequence at overall credit risk therefore the it may be hypothesized as following:
The fourth hypothesis considers the role played by corporate governance in minimizing credit risk. Corporate governance assumes that the organization or corporation should adopt all practices that ensure accountability to the stakeholders (Shafiq & Nasr, 2010 ). Therefore,
Methodology.
This study adopts an explanatory research design, which was aimed to collect authentic, credible and unbiased data. The data were collected from the employees of commercial banks located in the province of Balochistan, Pakistan. All ethical considerations were made during the research process. The questionnaire developed for the collection of information was prepared to effectively incorporate all potential factors that include, diversification, hedging, capital adequacy ratio, corporate governance and credit risk. The purpose of this research was clearly explained in the questionnaire as it was being shared with the respondents.
The participants were informed about the research objective and ensured that the information provided would be kept confidential. This step was designed to remove bias and ensure that the participants were able to share their views without having any reservations. This process is important for authentic results and reliable information (Levitt, 2004 ).
The sample size for this study comprised of 250 employees from commercial banks in Balochistan. There are large scale commercial banks that operate in Pakistan with several branches of these banks working in the entire country. Commercial banks approached for this study included Habib Bank Limited, Standard Chartered Bank, United Bank Limited, Summit Bank, Faisal Bank, Askari Bank and Bank Al-Habib.
The questionnaire was adopted from a global survey previously conducted by the World Bank. This study analyzed the work that has been done on managing credit risk in several countries in different parts of the world. Our questionnaire used the framework of this valuable research tool, adopting changes specific to address the localized context of Balochistan.
The information collected from the participants was analyzed to identify trends and practices in the banks operating in Balochistan to understand the practices of these commercial banks for managing credit risk. Following is the theoretical framework of the study.
The relationships between risk management strategies such as diversification, hedging, the capital adequacy ratio and corporate governance with credit risk itself were determined in the paper.
The questionnaire was tested to check the reliability through Cronbach’s alpha (Table 1 ), which shows internal consistency of the instrument; the information revealed that the data are 80% reliable, considering the total of 31 questions asked. The information is essential as this shows that the results and findings of the study are reliable and they can be generalized to the population (Hungerford, 2005 ).
The correlation table shows the relationship between the different variables in the research study. The dependent variable, credit risk, was reviewed against the independent variables: corporate governance, hedging, diversification and capital adequacy ratio. The correlation is essential for further analysis as there should be some relation between the different variables. Each variable is used for the correlation analysis so it highlights the correlation among all the variables with each other. This is useful for assessing the correlation among the independent variables and to ensure that it is not too high leading to a problem of multicollinearity.
Table 2 shows the results of the correlation test between the independent variables and the dependent variable. Before running regression analysis, basic assumptions were also checked. Data normality was checked through skewness and kurtosis and for all variables; these values were in range ± 2. Linearity was checked through correlation analysis and all variables were shown to have a significant relationship with each other. Homogeneity was checked through scatter plot, showing that the variance across all variables was the same. No autocorrelation was found as the value for the Durbin Watson test was 2, showing no correlation among residuals (Antonakis, Bendahan, Jacquart, & Lalive, 2014 ). The value for the variance inflation factor (VIF) was VIF < 5, which shows no relationship among the four independent variables. The regression test was used to determine the influence of each of the variable on credit risk. The results can be seen in Table 3 .
Credit risk can be influenced by different factors but, there is around 36% influence of the four variables that are independent. The variation of 36% can be explained by the independent variables that are hedging, diversification, capital adequacy ratio and corporate governance on credit risk. These factors account for this much change that can be observed in the credit risk faced by the commercial banks. The adjusted r 2 was further analyzed because it is a better measure for a focused analysis on a bank’s performance.
Table 4 shows the results of the assessment of the data for the overall model goodness of fit; the overall model is highly significant at p < 0.05. The analysis of the variance across the small samples of the data reveals that the overall information is consistent.
The standardized coefficients in Table 5 show the rate of change that is caused by each of the variables in the credit risk of the commercial banks. This is critical information as the variable that is having a higher coefficient value will be having more influence on the level of credit risk so it should be emphasized more by the commercial banks for the sake of achieving better performance. The regression analysis highlights that the four independent variables have an impact on credit risk.
The results reveal that corporate governance had the most impact on credit risk (with a 0.288 standardized beta value). In other words, this CRM strategy appears to be the most beneficial for commercial banks to undertake. Next is diversification (0.263 beta), followed by hedging (0.250 beta) and, finally, the capital adequacy ratio (0.040 beta). The results are significant in is showing that these variables have an impact on credit risk. The constant value was calculated at 1.765 and the error term in the equation is 0.237.
The banks in Balochistan would benefit from adopting sound strategies to improve control over credit risk. CRM strategies such as diversification, hedging, corporate governance and the capital adequacy ratio have all been cited in extant research as being crucial for the success in this regard; in fact, many problems arising from credit risk can be resolved by implementing some combination of these strategies. The research findings can likewise help the government of Balochistan to ensure that commercial banks take appropriate risk management measures to help keep them from failures, such as falling into bankruptcy (Greuning & Bratanovic, 2009 ). Society depends on the smooth operation of the banking sector, so individual (and aggregate) bank performance can help contribute to the development and improved welfare of the economy. Therefore, effective inspection should be employed by the banks to check and safeguard bank resources. Effective trainings and refresher courses should be giving to bank employees in the areas of risk asset management, risk control and credit utilization in order to ensure proper usage and performance.
Several banks have failed in the past as they were not able to control their credit risk. Recommendations for banks stemming from this study include the diversification of their products and services, which is critical as it allows the bank to provide customers with many products and services. After diversification, an emphasis on employing corporate governance policies is most important, according to the findings. Hedging and the capital adequacy ratio are also important strategies that can be examined and optimized by banks. Hedging is useful because entering into flexible contracts helps reduce risk. The banks in Balochistan will be able to realize the importance of the capital adequacy ratio as that will allow them to achieve a proper balance between the amounts of capital that should be maintained to manage the needs of the investors. It is recommended that further research on the topic should be conducted so that effective strategies for management of other risks can be identified for banks. The success and further progress of these banks depend on the smooth implementation of risk management strategies and activities, which have been shown to have a very significant positive impact on the ability of the banks of Balochistan to control credit risk.
The data of the research paper will be available upon request.
This is a place in Switzerland where the Basel Committee on Banking Supervision (BCBS) comprising of 45 members from 28 Jurisdictions, consisting of Central Banks and authorities have the responsibility of banking regulation.
Hedging are flexible contracts that allow customers to agree to buy a particular product in future date using spot rates. It allows customers and banks to manage the transaction by locking contracts at desired price.
Super hedging strategy allows the users to hedge their positions with a trading plan based on self-financing. A low price is paid for the portfolio that would ensure that it’s worth to be equal or higher at a future date.
We are grateful to all the reviewers who have shared their valuable comments and suggestions for the research paper. The Editorial Board of Financial Innovation has been extremely kind in their editorial efforts.
There was no funding required for the completion of the research paper.
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Balochistan University of Information Technology Engineering & Management Sciences, Quetta, Pakistan
Zia Ur Rehman, Noor Muhammad, Bilal Sarwar & Muhammad Asif Raz
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NM is the corresponding author and he has also given the idea for the paper. NM has reviewed the theoretical framework and empirical analysis of the research paper. ZR has written the manuscript and collected the data for the paper. BS has reviewed the methodology of the paper and reviewed literature. MAR has given conception advice and edited the paper. All authors have read the paper and approved the final manuscript.
Correspondence to Noor Muhammad .
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Rehman, Z.U., Muhammad, N., Sarwar, B. et al. Impact of risk management strategies on the credit risk faced by commercial banks of Balochistan. Financ Innov 5 , 44 (2019). https://doi.org/10.1186/s40854-019-0159-8
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Received : 06 February 2019
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Published : 05 December 2019
DOI : https://doi.org/10.1186/s40854-019-0159-8
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