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.

Optimal Claim Settlement Strategies under Constraint of Cap on Claim Loss

In this paper, we examine the question of how to devise an optimal insurance claim settlement scheme under the constraint of a cap on the amount of the claim payment. We establish objective functions to maximize the net benefit due to exaggerated claims while at the same time maximizing the total expected wealth of the insured. Then, we establish a dual objective function to minimize the total expected loss, including the perspective of the insurer. Finally, we illustrate applications of our work and provide numerical analysis of it along with an example.

Elderhood as an Anti-Ageism Intervention

Ageism is a complex, multi-layered phenomenon impacting feelings, thoughts and behaviors toward self and others. Due to the complexity of ageism, evidence-based anti-ageism interventions have proved challenging and costly. To date, using the concept of elderhood as a mechanism to mitigate the negative impacts of ageism has not been explored. As an anti-ageism strategy, elderhood reframes later life as a stage that encompasses growth and development and expected loss and decline. The current study evaluated a brief video intervention among first-year medical students before participating in a year-long senior mentoring program. First-year medical students (N = 585) from 2018-2021 responded to open-ended questions after viewing the video. Thematic analysis revelated four themes: neutrality, elderhood as development, reframing stigma and elderhood as othering. Findings suggest that elderhood may be a viable and productive anti-ageism strategy.

Pelatihan Penerapan PSAK 71 (Instrumen Keuangan) pada Perusahaan Daerah Air Minum di Wilayah Sumatera Selatan dan Bangka Belitung

Kegiatan Pengabdian Kepada Masyarakat ini bertujuan untuk memberikan pendampingan kepada bagian pengelolaan piutang usaha Perusahaan Daerah Air Minum di Wilayah Sumatera Selatan dan Bangka Belitung untuk mengestimasi besarnya penyisihan piutang tidak tertagih dengan menggunakan Expected Loss Method. Metode kegiatan ini dengan memberikan penyuluhan dan pendampingan kepada PDAM dalam membuat perhitungan estimasi piutang tak tertagih. Hasil pengabdian masyarakat ini ditandai dengan antusias dan kemampuan peserta dalam menerima materi dan mampu melakukan praktik untuk menghitung penyisihan piutang usaha dan penurunan nilai piutang usaha. Hasil kegiatan ini diharapkan bermanfaat bagi PDAM untuk meningkatkan pengelolaan piutang usaha yang nantinya akan meningkatkan pendapatan PDAM dan Pendapatan Asli Daerah.

Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. This option is useful in cases of uncertainty, where the learner does not feel confident enough on the entire label set. Adopting a decision-theoretic perspective, we propose a formal framework of MLC with partial abstention, which builds on two main building blocks: First, the extension of underlying MLC loss functions so as to accommodate abstention in a proper way, and second the problem of optimal prediction, that is, finding the Bayes-optimal prediction minimizing this generalized loss in expectation. It is well known that different (generalized) loss functions may have different risk-minimizing predictions, and finding the Bayes predictor typically comes down to solving a computationally complexity optimization problem. In the most general case, given a prediction of the (conditional) joint distribution of possible labelings, the minimizer of the expected loss needs to be found over a number of candidates which is exponential in the number of class labels. We elaborate on properties of risk minimizers for several commonly used (generalized) MLC loss functions, show them to have a specific structure, and leverage this structure to devise efficient methods for computing Bayes predictors. Experimentally, we show MLC with partial abstention to be effective in the sense of reducing loss when being allowed to abstain.

The Discount Rate Used in the Current Expected Credit Loss Standard Creates Accounting Losses Where There Are No Economic Losses

The Current Expected Credit Loss (CECL) Financial Accounting Standards Board (FASB) standard that goes into effect for major banks in 2020 contains a serious conceptual error. Using the contractual rate rather than the hurdle rate (the competitive rate on a loan for which there is no expected loss) as the rate to discount expected cash collections gives rise to accounting losses where no economic losses exist. This can have a profound effect on required capital and hence lending, especially in economically depressed episodes.

The Development of Expected-Loss Methods of Accounting for Credit Losses: A Review with Analysis of Comment Letters

After the financial crisis of the late 2000s, concern about delayed credit-loss recognition under the incurred-loss method prompted the FASB and the IASB to develop expected-loss methods. We review the development of these methods, including through comment-letter analysis. Initially, the FASB recommended immediate full recognition of expected losses, including at day one, and the IASB recommended spreading the recognition of initially-expected losses across time. After unsuccessful attempts to converge based on proposals that partly reflected initial recommendations of each board, the boards eventually adopted different methods. We report that U.S. respondents largely opposed the FASB's final method, which required day-one recognition of all expected losses, and that non-U.S. respondents largely supported the IASB's final method, which required day-one recognition of 12-month expected losses. Day-one loss was controversial and impeded convergence. Our comment-letter analysis suggests that a day-one-loss-free more forward-looking incurred-loss method might provide a route to a more converged solution.

Impact of Covid-19 on SME portfolios in Morocco: Evaluation of banking risk costs and the effectiveness of state support measures

This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.

Exploring the sources of loan default clustering using survival analysis with frailty

This paper investigates whether three microeconomic loan characteristics are sources of loan default clustering in the Mexican banking sector by employing survival analysis with frailty. Using a large sample of bank loan level data granted to micro, small and medium sized firms from January 2010 to 2018, we test whether classifying loans by the bank's systemic importance, industry or at individual firm level enhances the predictions of loans defaults. Our results show that loans granted by Domestic Systemically Important Banks contribute to the default clustering in micro and small firm loans. This is due to aggregate default rate levels and clusters that are large for these firms loans compared with loans provided to medium-sized firms. These findings have important implications for bank's expected loss management related to the correlated loan default risk