**5. Results and discussion**

**Table 3** presents three common reasons for the weakness of discriminatory procedures in practice, for which hypotheses have been formed to improve their effectiveness. The formulation of these hypotheses, their development and increase in the effectiveness of risk management are the goals of its validation for the retail (including corporate) lending business.

The conservative marginal profit formula (9) gives the bank's management, without building complex financial business models, a conservative benchmark or tool that is the basis for making a decision to invest in their own risk management infrastructure to improve the efficiency of credit decision-making.<sup>7</sup> This also includes the decision to invest in the acquisition of third-party services that increase the effectiveness of risk management.

<sup>7</sup> By investment, here we mean the general regular and individual costs attributable to the cost of the business process.


#### **Table 3.**

*Typical reasons for risk management weakness.*

Here are some simplified business cases:


<sup>8</sup> These are credit loans for which there are no clear signs of deterioration in credit quality yet. As a rule, all applicants, after making a decision on a loan, belong to the first stage of impairment.

conservative result \$**6** thousand � **2***=***2**%*=***10**% ¼ \$**6** million . That is, this is the limit, starting from which, it is advisable to attract a rating agency, and this limit can be proportionally reduced, taking into account lending for a period of more than one year, since the expected losses will increase proportionally (more precisely, almost proportionally).

In practice, decision-making problems are obviously more difficult. Since all related aspects must be considered, such as the incomplete "marketability" of proposed transactions, the quality of collateral and its assessment, data privacy factors, the target aspect of financing, control mechanisms, etc. However, this does not detract from the value of the fundamental profit assessment proposed by the developed approach.

Currently, there is a continuous improvement in scoring approaches to assess the applicant for loans. The methods use extended data, including nonfinancial data, which requires additional resources both to maintain significantly increased data volumes and to implement more complex algorithms and procedures. The payback of these resources for the credit business can be assessed by the above tools. According to the portfolio statistics of the decisions made, it is possible to objectively assess the current effectiveness of risk management. In [19], an overview of the areas of alternative credit scoring is presented. This field is emerging and gaining popularity due to the critical role of alternative data in accelerating access to financial services. Historically, a creditworthiness assessment has required the existence of past financial activity, such as repaying a loan. Such strict requirements made people with little or no financial history "credit invisible". Advances in artificial intelligence and machine learning have enabled scoring algorithms to work with nonfinancial data, such as digital footprints from mobile devices and psychometric data, to calculate credit scores. Although most invisible loans are in developing countries, research in this area is predominantly conducted in developed countries, and most alternative credit scoring models are trained on data from developed countries.

The study [20] explores the use of psychometric tests developed by the Entrepreneurial Finance Laboratory (EFL) as a tool to identify high credit risk and potentially increase access to credit for small business owners in Peru. Administrative data is used to compare accrual and repayment behavior patterns among entrepreneurs who have been offered credit based on the traditional credit rating method and the EFL tool. It has been found that a psychometric test can reduce the risk of a loan portfolio if it is used as a secondary screening mechanism for entrepreneurs already working in a bank, i.e. those who have a credit history. For nonbank entrepreneurs who do not have a credit history, using the EFL tool can increase access to credit without increasing portfolio risk. Another pilot project [21] to study the effectiveness of scoring based on psychometric data was launched in 2017 in Spain as part of the business microcredit segment (i.e. social microcredit for self-employed clients who want to start a business and need help from social organizations to develop your business plan). Initial statistical tests say that the discriminatory power (as measured by the Gini index of the ROC curve) can be in the range of 70-80% (compared to the 30–40% Gini index offered by traditional models). This means that the use of psychometric scoring can significantly increase the discriminating power and give a financial profit (9), which for the reference population of loan applicants should exceed the costs of introducing new risk management tools.

Another example of improving scoring power by expanding data coverage is a study [22] showing that adding social media-derived variables to a scorecard increases the Gini index by 7–8%.

*Risk Management Tools to Improve the Efficiency of Lending to Retail Segments DOI: http://dx.doi.org/10.5772/intechopen.108527*

And, of course, improving the scoring schemes themselves, considering the dynamics of scoring variables, and approaches that go beyond regression also show their increased discriminatory effectiveness. Yibei et al. [23] proposes a Bayesian optimal filter to provide risk prediction for lenders, assuming that published credit ratings are estimated simply from structured financial data. A recursive Bayesian scoring is proposed to improve the accuracy of credit scoring by incorporating a dynamic customer interaction topology. Theoretically, it is shown that, in accordance with the proposed concept of evolution, the developed scoring system has higher accuracy than any effective estimate, and the standard errors are strictly less than the lower Cramer-Rao bound in a certain range of scoring points. In [24], the approach of logistic regression is improved by introducing a new class of covariant categorization methods in regression models for binary response variables. Application to real data and Monte Carlo simulation study suggests that one of the methods of this class has better predictive performance and lower computational cost than other methods available in the literature.
