TRADE CREDIT SUBSTITUTION, HOUSEHOLD VULNERABILITY, AND BANK STABILITY IN KAZAKHSTAN

ABSTRACT

The paper synthesizes recent evidence on credit-risk formation in Kazakhstan’s banking sector, integrating macro shocks, commodity and exchange-rate exposure, corporate working-capital channels, household heterogeneity, and bank-internal factors. Asymmetric oil-price movements and FX pass-through shape borrower cash-flows and collateral, while trade credit often substitutes for bank credit in crises with episodic complementarity among large firms. Household debt fragilities are uneven across urban and rural segments. Pandemic-era studies show real-sector shocks elevate systemic risk, conditional on size, leverage, and capitalization. We propose a three-layer assessment architecture: macro-consistent stress scenarios, segment-specific tests for corporates and households, and bank-level diagnostics combining buffers with forward-looking indicators. The framework supports point-in-time ECL and long-run PD/LGD calibration and informs borrower-based tools and macroprudential policy.

АННОТАЦИЯ

Статья синтезирует новейшие данные о формировании кредитного риска в банковском секторе Казахстана, интегрируя макрошоки, товарную и валютную экспозицию, каналы оборотного капитала корпораций, неоднородность домохозяйств и внутрибанковские факторы. Асимметричные колебания цен на нефть и эффект переноса (pass-through) колебаний обменного курса формируют денежные потоки заемщиков и качество залога; в кризисы торговый кредит часто замещает банковское кредитование, при этом у крупных фирм наблюдается эпизодическая взаимодополняемость. Долговые уязвимости домохозяйств неодинаковы в городских и сельских сегментах. Исследования периода пандемии показывают, что шоки реального сектора повышают системный риск, причем эффект обусловлен размером, долговой нагрузкой и капитализацией. Мы предлагаем трехуровневую архитектуру оценки: макро-согласованные стресс-сценарии; сегментные тесты для корпораций и домохозяйств; диагностику на уровне банка, сочетающую буферы с опережающими индикаторами. Предлагаемая рамка поддерживает точечную оценку ожидаемых кредитных потерь (ECL) и долгосрочную калибровку PD/LGD, а также информирует заемщик-ориентированные инструменты и макропруденциальную политику.

Keywords: non-performing loans; credit risk; oil price shocks; exchange-rate pass-through; trade credit; household debt; stress testing; expected credit loss; macroprudential policy; Kazakhstan.

Ключевые слова: неработающие кредиты (NPL); кредитный риск; шоки цен на нефть; эффект переноса обменного курса (pass-through); торговый кредит; долг домохозяйств; стресс-тестирование; ожидаемые кредитные потери (ECL); макропруденциальная политика; Казахстан.

Introduction.

Kazakhstan’s banking system has faced repeated stress from the 2008 to 2009 global crisis, the 2014 to 2015 oil-price collapse with devaluation, and COVID-19, each leaving a mark on asset quality and risk appetite. A surge of problem assets in the late 2000s and early 2010s, followed by clean-up and restructuring, shaped today’s leaner and better-capitalised balance sheets and their residual sensitivity to shocks [1, 23 p.]. The credit cycle is tightly coupled to commodities and the exchange rate, since oil-price volatility and income swings affect repayment capacity, collateral, and internal risk migration. Bank-level evidence for 2009Q1 to 2020Q1 shows asymmetric oil-price effects on non-performing loans via a resource-boom channel [2, 114 p.]. Firm-level data for 2009 to 2016 indicate that in crises enterprises substitute trade credit for bank credit, with complementarities for large firms after 2014 to 2015, which is relevant for forward-looking loss estimates [3]. Household credit is a further conduit, since micro-simulation for Kazakhstan finds uneven resilience to income and currency shocks across urban and rural households, implying clustered retail losses under stress [4]. Pandemic-era studies confirm that exogenous real-sector shocks raise systemic risk, and that the impact is shaped by bank size, leverage, capitalisation, and regulatory backstops, determinants that are salient in Kazakhstan as well [5], [6].

Main part.

Determinants of credit risk in Kazakhstan reflect macroeconomic, bank-specific, and institutional factors. For emerging-market systems, comparative reviews link non-performing loans to GDP growth, unemployment, inflation, public debt, capitalisation, profitability, operating inefficiency, ownership concentration, and size [7]. Kazakhstan-specific work shows that macro factors significantly affect non-performing loans, including through mediated social and external channels, which argues for macroprudential surveillance beyond purely financial indicators [8]. Commodity exposure is a first-order vulnerability. Dynamic threshold evidence indicates that oil-price declines compress income and collateral values and worsen credit quality, while rebounds do not symmetrically repair balance sheets, which complicates provisioning and point-in-time expected-credit-loss estimation [2, 114 p.]. These effects interact with exchange-rate pass-through, since depreciation raises tradables prices and foreign-currency servicing costs and accelerates stage migration where buffers are thin. Corporate working-capital channels add a crisis-specific layer, because when bank credit tightens, trade credit typically substitutes, although large-firm behaviour after 2014 to 2015 shows episodic complementarity; both dynamics propagate payment-chain risk and can turn supplier receivables into bank-like exposures [3]. Retail dynamics supply a third layer. Evidence for Kazakhstan indicates that income-loss shocks dominate other drivers of household default and that rural households are more vulnerable than urban households [4]. Global pandemic-era studies show that exogenous shocks heighten systemic risk, especially in large, leveraged, undercapitalised banks, while strong prudential regimes and governance mitigate propagation [5], [6]. These findings support borrower-based tools such as debt-service-to-income and loan-to-value caps, and capital planning calibrated to consumer and SME concentration [7].

Bank-internal attributes are pivotal. Higher capital and profitability align with lower non-performing loans, whereas operating inefficiency and concentrated ownership weaken screening and monitoring [7]. Kazakhstan-focused analyses concur, since clustering evidence for 2008 to 2014 shows that asset-quality and capital ratios separated sound from risky institutions, which mirrors supervisory early-warning sets [1, 23 p.]. Recent results for Kazakhstan link regulatory compliance to stronger risk-management efficacy and improved financial performance [9, 157 p.]. Regional work during financial instability emphasises that risk-management capability, including methodology, governance, and data, is a driver of resilience rather than a reporting afterthought [10, 3269 p.].

Conclusion.

Kazakhstan’s credit-risk profile emerges from the interaction of macro conditions, commodity and exchange-rate exposure, borrower heterogeneity, and institutional quality. In my view (opinion), an effective assessment architecture has three integrated layers. First, undertake macro-consistent stress testing that models asymmetric oil-price shocks and exchange-rate pass-through, and feed those paths into borrower cash-flow and collateral modules [2, 114 p.]. Second, run borrower-segment stress tests that capture bank–trade-credit substitution in corporates and urban–rural heterogeneity in households [3], [4]. Third, implement bank-level diagnostics that combine capital and profitability buffers with forward-looking indicators such as stage migration, early-arrears roll rates, cure patterns, and cost-to-income, alongside explicit tests of governance and ownership incentives [7], [10, 3269 p.]. Evidence across the cited studies suggests that such a design improves point-in-time loss estimation and long-run calibration, and strengthens coordination between risk, finance, and business when conditions deteriorate.

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