**1. Introduction**

Modern recommendations of banking supervision reflect the requirements for the organization of the use of internal rating models and their quality, allowing the bank to bring the regulatory requirements of reserves and capital closer to economically justified, i.e., risk-sensitive [1]. The requirements of the National Banking Regulator are complex, concerning the preparation of high-quality and validated data for the development of models, the requirements for the development itself, the administrative independence of developers from the interests of business, regular validation of models on current data, systematic refinement of risk assessment models, etc. When deficiencies in discriminatory ability or stability are identified, the requirements define mandatory regular reporting to the Bank's Management and the Regulator on the results of quality analysis of the applied internal rating models. The requirements for the risk and capital management system of the banking group through the

implementation of internal procedures for assessing capital adequacy reflect the requirements [2] to establish the procedure and frequency for assessing the effectiveness of risk assessment methods. It is required to update the documents that establish risk assessment methods and the procedure for validating quantitative models. Obviously, the implementation of these requirements is costly for the bank, including the participation of highly qualified specialists, and the improvement of IT infrastructure and business processes. Therefore, the question arises of the adequacy of costs in relation to the credit institution's own economic effect with an increase in the level of efficiency of risk assessment methods, as well as a management decision-making system based on these methods.

Improving the quality of risk assessment modeling brings financial and nonfinancial benefits in terms of the following points:


The introduction of credit scoring in the banking sector has privileges which have been summarised by Al Amari [3] as follows: more efficient processing time, and subsequent support for the decision-making process; minimization of credit process costs and effort; fewer errors made; provision of estimations to be compared in post audits; inclusion of variables supported through objective analysis to assess the credit risk; modeling based on real data; interrelation between variables are considered; fewer customer-information needs for credit decisions; the cut-off score can be changed according to environmental factors affecting the banking sector.

The direct financial benefit of the first bullet is determined by the discriminatory power of the borrower's analytics based on scoring risk assessment models. This is especially true for mass banking products.

Credit scoring has been considered a major scoring tool for the past several decades and has been widely studied in various fields, including finance and accounting. Various scoring methods are used in the fields of classification and forecasting, where statistical methods are commonly used. The literature explores both complex and traditional techniques, as well as criteria for evaluating effectiveness. In [4], it provides a comprehensive review of 214 articles/books/abstracts that relate to the applications of credit scoring in various fields in general, but primarily in finance and banking. This paper provides a broad overview of various statistical methods and performance evaluation criteria. This review [4] showed that there is no best statistical method to use when building scoring models.

To increase the power of credit scoring, in the presence of the Big Data of individuals, various methods are used, including those based on artificial intelligence

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

methods. As shown in [5], organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. If systems can be designed to accommodate more pragmatic analysis conditions, then this can help improve the conditions of the client profile analysis process. At the same time, process models should be developed for comprehensive analysis and then they can become a sustainable solution for managing the loan system.

The significant dependence of the profit of lending and investing in debt instruments on the quality of risk management has been substantiated by many studies. In [6], the authors developed a model for evaluating the profit that the improvement of rating systems brings. Results of a numerical analysis indicate that improving a rating system with low accuracy to medium accuracy can increase the annual rate of return on a portfolio by 30–40 bp. Therefore, compared to the estimated implementation costs, banks could have a strong incentive to invest in their rating systems. In [7], it is shown that the simple cut-off approach can be extended to a more complete pricing approach, which is more flexible and more profitable. Demonstrated that, in general, more powerful models are more beneficial than weaker ones, provides an example of modeling, and demonstrates the benefit in absolute terms. Later work [8] also examines the economic benefits of using credit scoring models, linking the discriminatory power of the credit scoring model to the optimal credit decision.

The main idea of the presented approach is based on the fundamental analogy of risk management activities with a certain generalized rating/scoring system, which also makes decisions in the retail lending segment. Risk management can be matched to the ROC/CAP-curve, and its power of discrimination can be assessed [9]. Then, determine how optimally such a rating system makes decisions, considering its own power and exogenous risk-return factors.

Approaches to assessing economic efficiency using the ROC-curve methodology were proposed earlier, but they were not directly related to risk management in lending. In [10], the authors'study indicates that banks have incentives to voluntarily participate in a positive credit information exchange mechanism. Because even a small difference in discriminatory power arising from an information gap can lead to a significant drop in profitability since the distribution of change in borrower quality is endogenous due to adverse selection problems. A paper [11] presents a methodology for assessing the economic value of adding additional data to predictive modeling applications. The methodology is based on the representation of the ROC curve and begins with an assessment of the impact of additional data on the performance of the model in terms of overall classification scores. This effect is then translated into economic units, which give the expected economic value that the firm would receive from acquiring a particular information asset. With this valuation, the firm can then set a data acquisition price that targets a specific return on investment. In the work presented in Section 2, we will answer the question of assessing the effectiveness of risk management in making credit decisions and give a methodology for validation. Section 3 proves the formula for marginal profit when the scoring model is improved in units of the Gini index. Section 4 specifies the area in the Gini coordinates and the ratio of profit to risk, where the marginal economic effect, defined by the presented formula, takes place. Section 5 discusses practical cases that are resolved using the marginal formula and typical causes of risk management inefficiency, as well as an overview of current trends in the development of scoring modeling.
