INTERNATIONAL FINANCIAL REPORTING STANDARDS AS A TOOL FOR REGULATORY SUPPORT OF OPERATIONS WITH THE LOAN PORTFOLIO OF UKRAINIAN BANKS

The article analyzes the problem of application of information-analytical and regulatory support of operations with the credit portfolio of the Bank, which is currently relevant and Central to successful lending, and therefore to ensure the return of loans and increase the profitability of credit operations. The necessity of forming an effective and efficient system of information-analytical and legal support of operations with the credit portfolio of commercial banks, the main tool of which is the international financial reporting standards, is revealed. It is determined that the requirements of the modern level of globalization are still ahead of the adoption of integration changes regarding the rules and principles of accounting and management accounting by commercial banks of Ukraine. A universal, effective and efficient system of legal regulation of operations with the credit portfolio of a commercial Bank is built on the basis of the study. It is proved that the regulation of operations with the credit portfolio of a commercial Bank can be schematically represented as a hierarchical scheme with 4 levels, where International accounting standards and International financial reporting standards represent the main first level. It was revealed that according to the international financial reporting standards, loans provided by banks belong to the category of financial assets, which are one of the types of financial instruments. The other three levels of the system of legal regulation of operations with the credit portfolio of a commercial Bank, which are derived from the first level, are analyzed. It is concluded that for a more effective organization of accounting it is necessary to harmonize the Ukrainian legislation in accordance with the requirements of International financial reporting standards, and therefore commercial banks with the help of such a system of analytical indicators will be able to constantly monitor changes in the economic system and respond in a timely manner by making appropriate adjustments in case of unsatisfactory developments

Efficiency and productivity of the Brazilian banks: A new approach based on two-stage network DEA

This article creates a conceptual model, called a network system, to represent the Brazilian banking production system, based on its internal operational processes. The first, called the intermediation process, measures a bank's efficiency in extending loans from its available resources. The second, called the revenue process, measures a bank's efficiency in earning profit, mainly from loans granted. We adopt a two-stage DEA model. In the first stage, a relational network DEA model measures both the network system efficiency scores and internal processes. This technique, associated with the Malmquist Index, assesses performance changes over time. In the second stage, these efficiency scores are considered dependent variables, such that Tobit models can determine how the Brazilian credit market's characteristics can explain the network system and internal processes' efficiency. Results show not only a growing trend toward greater efficiency in the revenue process, but also an increase in productivity accompanied by a decline in the intermediation process technology. Given the high banking spreads in Brazil, these results indicate deterioration in the quality of the credit portfolio and the prospect of future insolvency. We discuss implications of this scenario for domestic banks and collateral policy.

IFRS9 e le sfide di contesto

Under IFRS9, Financial Institutions are required to implement impairment frameworks to determine the expected losses on their credit portfolio taking into account the current (so called “point in time”) and the prospective (so called “forward looking”) economic cycle. The Covid-19 pandemic, which began in early 2020, has posed significant challenges for Financial Institutions in their ability to manage credit risk. Despite numerous guidelines given by regulators, estimating IFRS9 expected loss continues to be a considerable challenge. The challenge partly stems from the relationship between macro-economic scenarios and credit losses, the treatment of moratoriums inside the historical series for development and calibration of IFRS9 risk parameters, and the management of support measures defined at National and European levels (e.g. Next Generation EU) for the forward looking estimations.

Credit Portfolio Convergence in U.S. Banks since the COVID-19 Shock

The COVID-19 pandemic has materially affected U.S. consumer behavior and business operations in many important aspects. This note focuses on the changes in banks’ balance sheets and demonstrates how we could apply a novel measure of portfolio similarity to balance sheet data and assess the drivers of similarity change along the path of the pandemic.

Probabilistic Calibration and Genetic Algorithm-based Bank Credit Strategies for MSMEs and Enlightenment to Tobacco Enterprise Management

Objectives: To further strengthen the role of Micro, Small and Medium Enterprises (MSMEs) in maintaining the vitality of national economy, governments around the world introduced many special policies. They kept guiding the banking industry to increase the support for MSMEs and reduce their financing difficulties in banks. Basing on the analysis of the bank's credit strategy for small and medium-sized enterprises of similar size, this paper gives the management strategy for small and medium-sized enterprises in tobacco industry to obtain bank credit when they cannot expand their turnover. In this paper, we proposed a binary classification model-based probabilistic calibration algorithm to calculate the default probability of enterprises in the formation of risk measurement model, and found the optimal solution of credit strategy using an improved genetic algorithm. Firstly, we discovered the enterprise’s information and invoice data of 123 micro and medium-sized enterprises with existing credit ratings. We extracted several features from multiple perspectives, such as size, relationship in supply chain, profitability, performance ability, and level of development, and removed the correlations among the indicators using principal component analysis (PCA). Secondly, the retained principal components were used as covariates, and we determined the credit ratings of the firms and the probability of default using discrete variables such as the credit ratings of the firms and whether they defaulted. Finally, we substituted the probability of default into the credit risk model to calculate the loss expectation and profit expectation of the credit portfolio, and used the profit expectation of the credit portfolio as the objective function of the 0-1 programming equation to derive the credit strategy with the lowest risk exposure and the highest return basing on the genetic algorithm.

Credit scoring development in the light of the new Brazilian General Data Protection Law

With the advent of the new Brazilian General Data Protection Law (LGPD) which determines the right to the explanation of automated decisions, the use of non-interpretable models for human beings, known as black boxes, for the purposes of credit risk assessment may remain unfeasible. Thus, three different methods commonly applied to credit scoring – logistic regression, decision tree, and support vector machine (SVM) – were adjusted to an anonymized sample of a consumer credit portfolio from a credit union. Their results were compared and the adequacy of the explanation achieved for each classifier was assessed. Particularly for the SVM, which generated a black box model, a local interpretation method – the SHapley Additive exPlanation (SHAP) – was incorporated, enabling this machine learning classifier to fulfill the requirements imposed by the new LGPD, in equivalence to the inherent comprehensibility of the white box models.

Stress tests in Hungarian banking after 2008

The 2008 crisis highlighted the importance of using stress tests in banking practice. The role of these stress tests is to identify and precisely estimate the effect of possible future changes in market conditions on capital adequacy and profitability. This paper seeks to show a possible methodology to calculate the stressed point-in-time probability of default (PD) parameter. The presented approach contains a linear autoregressive distributed lag model to determine the connection between the logit of default rates and the relevant macroeconomic factors, and uses migration matrices to calculate PDs from the forecasted default rates. The authors illustrate the applications of this methodology using the Hungarian real credit portfolio data.

MSME lending and bank efficiency: Evidence from Indonesia

Banks prefer to lend to bigger clients for a variety of reasons, including transaction costs and risk considerations. Due to this phenomenon, the Central Bank of Indonesia issued a regulation that requires banks to channel a minimum proportion of their credit portfolio to micro, small, and medium enterprises (MSMEs). Nevertheless, the impact of channeling funds to MSMEs remains a subject of controversy, in part depending on the dimensions and metrics used. This study examines how MSME lending affects the efficiency of banks in Indonesia, a country where MSMEs constitute more than 99% of business entities. Using a total of 175 panel data observations of banks in Indonesia from 2014–2018, banks’ cost efficiency is first estimated using a stochastic frontier approach (SFA). Panel data regression is used to examine the impact of MSME lending on efficiency. The result of this study shows a significant and positive impact of the proportion of MSME lending on bank efficiency, which indicates that requiring banks to channel funds to MSMEs does not only potentially support economic development, but also is beneficial from the business perspective in the Indonesian context. AcknowledgmentThe research was also made possible with the support of PUTI Grant by Universitas Indonesia No. NKB-2036/UN2.RST/HKP.05.00/2020.

Real Estate Markets and Lending: Does Local Growth Fuel Risk?

Real estate price growth affects credit risk for several reasons: it provides input for economic forecasts as it’s closely tied to economic growth; when used as collateral by banks, rising real estate prices may decrease both expected and actual losses; and banks may become less risk averse in lending practices in the presence of rising property prices. Therefore, we analyze these effects on loan portfolios’ estimated and realized risks on a local level. Using data of 390 German savings banks, however, we find that real estate prices have little or no impact on savings banks’ credit portfolio risk or risk precautions.

Analisa Reward dan Kinerja Karyawan: Service Quality Sebagai Variable Moderating Pada Bank Central Asia

Bank Central Asia (BCA) growth rate in terms of profit reached Rp 20 trillion, grew 15.8 percent (year-on-year/yoy), credit portfolio loans grew 5.3 percent to Rp 595.1 trillion, Third Party Funds (DPK) increased 1.3 percent to Rp 761.6 trillion, Operating Income grew 10.3 percent to Rp 37.8 trillion, capital adequacy ratio (CAR) was at 22.9 percent and return on assets (ROA) was 3.1 percent and return on equity (ROE) was 15.6 percent. The purpose of this study was to analyze the relationship of reward and employee performance with employee loyalty as moderating variables. The method is carried out using descriptive qualitative method with research object of Bank Central Asia. The results showed that one of the elements that influence the success in improving quality services is the existence of good human resource management that can be measured through performance, personality and psychological contracts. Tingkat pertumbuhan Bank Central Asia ( BCA ) dari aspek dilihat dari laba yang mencapai Rp 20 triliun, tumbuh 15,8 persen (year-on-year/yoy), Kredit Portofolio kredit tumbuh 5,3 persen menjadi Rp 595,1 triliun, Dana Pihak Ketiga (DPK) meningkat 1,3 persen menjadi Rp 761,6 triliun, Pendapatan Operasional tumbuh 10,3 persen menjadi Rp 37,8 triliun, Rasio kecukupan modal (CAR) berada di level 22,9 persen serta rasio pengembalian terhadap aset (ROA) 3,1 persen dan pengembalian terhadap ekuitas (ROE) 15,6 persen. Tujuan penelitian ini adalah untuk menganalisis hubungan reward dan kinerja karyawan dengan employee loyalty sebagai variabel moderating. Metode yang dilakukan dengan menggunakan metode deskriptif kualitatif dengan objek penelitian Bank Central Asia. Hasil penelitian menunjukkan salah satu unsur yang berpengaruh terhadap keberhasilan dalam meningkatkan pelayanan yang berkualitas adalah adanya manajemen sumber daya manusia yang baik yang dapat diukur melalui kinerja, kepribadian dan kontrak psikologis.