This paper examines a new method for measuring reputational risk developed by the Customer Services Institute in 2018 for UK insurance markets. It sets out the way in which the model was constructed through in-depth qualitative work followed by detailed opinion surveys, and uses two case studies to compare the results from the research with perceptions about how the sector has performed through the lens of different stakeholders, including regulators. It finds that the index correctly identifies two areas where public trust in insurers has been reduced. These areas are renewal charging practices in retail insurance and payment of business interruption insurance claims for SMEs. The paper also concludes that the index gives strong practical guidance about how these reputational issues can be addressed. Two limitations of the model are as follows: first, because it is set up to express issues in consumer terms, it can be difficult to then draw lessons for different organisations within the value chain, and secondly, the survey approach can obscure the experiences of very small minorities, unless the survey is carefully focused on those groups. For very small groups, a more qualitative approach may be more effective.
We investigate the effect of government support on firm zombification during the COVID-19 crisis for Belgium, Germany, Spain, France, United Kingdom, the Netherlands and the United States. For this purpose, we use a simple model that links insolvency developments at the macro level to GDP developments. We first observe from the data that insolvencies have declined during the crisis despite economic contraction. We then use the model to calculate the total firms saved from insolvency by government intervention and the fraction of which are not healthy, the zombies. It appears that government intervention has been effective in saving firms during the height of the crisis in Q2 of 2020. The impact is smaller in Q3 when the recovery set in. But it comes at a cost of efficiency as it causes significantly higher zombification in the economy. The effect can be seen in both quarters, most notably in Belgium. In Germany and the Netherlands, zombification was low in Q2 but soared in Q3. The reverse effect was visible in Spain as the number of insolvencies picked up in Q3. Therefore, government intervention during the crisis has reinforced the existing trend of zombification there. With government support only gradually withdrawing in the recovery phase of the crisis and probably further pushing up the number of zombie firms, risk management faces the challenging task of detecting them. We offer a few suggestions. These are based on the classification of zombies, including ICR and Tobin’s q, as well as research on underlying drivers. Compared to the sector median, zombies appear smaller, less productive, slower growing, with lower investments and higher leverage despite higher equity issues and implied interest rate subsidies. Complementary to this market-based information, more proprietary, higher frequency data such as payment behaviours, revenue growth pace change, credit sourcing, bank covenant compliance and management competencies can be monitored using scorecards. Finally, we recommend a high level of vigilance and an additional provisioning to anticipate any delayed effects of the crisis.
In 2016 Allan D. Grody and Peter J. Hughes proposed a method and system termed ‘Risk Accounting’, an integrated financial and risk accounting framework. Risk Accounting incorporates a novel operational risk exposure quantification technique based on the Risk Unit (RU), a new common additive metric designed to express all forms of operational risk in banks. In this paper, we report on initial tests of the inherent predictiveness of the RU. The test focused on the period leading up to the global financial crisis of 2007-8 and involved the restatement into RUs of publicly available accounting data in the United States relative to a subset of large US banks. We contend that the RU’s inherent predictiveness could be concluded if it is demonstrated that an accelerated increase in trended operational risk RUs and subsequent material unexpected losses are positively correlated. We further describe how a monetary value can be stochastically derived and assigned to the RU over time. The inclusion of valued RUs in accounting systems will potentially enable the systematic adjustment of financial performance and condition relative to accepted nonfinancial risks to complement the accounting treatment already applied to financial (credit and market) risks. The resulting harmonisation of the accounting treatment applied to both financial and nonfinancial risks based on stochastic modelling will enable risk-adjusted economic profit to be adopted as the primary business performance metric and economic capital as the primary method of determining both operating and regulatory capital requirements. The real-time or near-real-time production of portfolio views of operational risk exposures based on the RU adds analytical rigour to their management and causes risk mitigation to become both a risk reduction and a profit optimisation initiative. The more effective management, oversight and governance of exposures to operational risks is the anticipated outcome.
The purpose of this paper is to show the interaction between the Basel IV output floor and business model management. Specifically, the paper analyses how banks can optimise the output floor by moderately adjusting the composition of their portfolio. The individual topics are explained based on simplified exemplary cases. The presented capital floor analysis may help a bank’s top management to allocate the available capital better, formulate a coherent internal risk appetite, including the cost of capital in their pricing models, and set explicit targets for key performance drivers directly linked to the desired shareholder returns. With a target business model in mind, our approach can therefore be used to determine target levels for the individual risk positions that contribute to the output floor. The paper presents a procedure for overall bank management, particularly for business model planning in the presence of the Basel IV floor. Managers, analysts and regulators can apply our approach to analyse the business model of an individual bank, as well as the output floor of the banking sector as a whole. To our knowledge, our paper is the first academic contribution on the impact of the new prudential floor approach on the banks’ business model.
This paper analyses the relationship between macroeconomic and credit cycles. It is not a straightforward relationship, particularly in sovereign credit assessment. Modelling such a relationship requires blending scenario analysis and stress testing, together with dynamic modelling of macroeconomic and credit variables. The novelty of the presented approach is its ability to cross-pollinate machine learning and Monte Carlo (MC) simulation as part of a process that overcomes the challenges faced by risk managers. The result is a probabilistic forward-looking view of credit risk scenarios that can guide action. Sovereign credit ratings are expert opinions based on relevant macroeconomic, financial and policy information. We introduce a predictive machine learning model of sovereign credit ratings that lends itself naturally to MC simulations and stress testing. The Least Absolute Shrinkage and Selection Operator (LASSO) allows considering many variables simultaneously in a nonlinear fashion as candidates for predicting sovereign ratings. The portfolio stress testing capability comes in by augmenting the set of variables used in the MC simulations to include external shock variables common to the sovereigns in the portfolio, for example, relevant global commodity prices. The resulting rating distribution can be used to calculate different relevant risk metrics, including credit-sensitive measures of risk-weighted assets.
With the aim of reducing the excessive variability of risk-weighted assets (RWA) and improving the comparability and transparency of banks’ risk-based capital ratios, the European Banking Authority restructured the regulatory market risk framework. The long-awaited final version of the FRTB Market Risk Framework was published by the Basel Committee on Banking Supervision (BCBS) in January 2019. This paper aims to analyse the main reasons that led the regulators to formulate the new Market Risk Framework named Fundamental Review of Trading Book (FRTB) and the consequent risk management impacts. The first FRTB impact assessments conducted by the same European authorities suggest important increases in capital charges for banks that use either the standardised or internal models approach. Beyond the impact on capital charges, the FRTB framework will have a deep impact on market risk management activity, analytics, data collection, market risk limits, control systems, market risk procedures and policies. From this perspective, the FRTB represents a great change in the market risk management paradigms. It requires not only new measurement, management and control tools but also new financial skills and knowledge for the European banking sector. This is for all banks and those that, in the current regulatory context, use the standardised approach for the calculation of the capital requirement.
Part I of this paper examined the risk function’s evolution in response to (i) financial disclosures becoming increasingly risk-based, (ii) an increasing need to optimise capital management and business mix to enhance Return on Equity (ROE). The optimisation frameworks to determine the optimal ‘risk strategies’ need to be established by the risk function, which is now at the core of financial disclosure, technology and strategy. Part II examines the necessary competencies for the risk executives, in particular Chief Risk Officers (CROs), to be effective and lead the evolution. These are analytical, digital and strategic competencies. For the leadership roles in well-established finance, accounting, actuarial functions, and in engineering, it is recognised that professional qualifications, advanced content knowledge and experience are required for the leaders to be effective. We observe that this is often not the case for bank risk executives. It is not uncommon to see a leader without specific risk expertise and experience holding senior risk executive, even CRO, roles. CRO roles also have limited upwards mobility and can be the last stop, bridging the executive to retirement. We examine the potential causes, including the historical reasons, insiders’ bias, cognitive biases, pigeon-holed career paths and misuse of power. We make suggestions for improvements and opening the path for the next generation of risk professionals to fill the executive and board roles and lead the necessary evolution.
우리나라가 2023년부터 시행을 목표로 추진하고 있는 신지급여력제도(K-ICS)에서는 현행 지급여력제도(RBC)에서 측정하지 않았던 보험계약의 해지리스크를 보험리스크의 세부항목으로 측정하고 있다. 이에 따라 장래 해지율 변동에 따른 해지리스크의 측정이 지급여력 비율 측정에 중요한 영향을 미칠 것으로 예상된다. 그런데, 경과기간별 해지율간의 상관성을 제거하지 않을 경우 해지리스크가 정확하게 측정되지 않는 결과를 초래할 수 있다. 통계적으로도 우리나라 생보사의 상이한 경과기간별 해지율 변동률의 상관관계는 경과기간 1년과 2년의 상관계수가 0.67이고, 상관성이 가장 낮은 것으로 나타난 경과기간 1년과 15년 해지율 변동률의 상관계수도 0.16을 기록하는 등 전체 경과기간에 걸쳐 높은 수준의 양(+)의 상관성을 보이고 있다. 이에 본 연구에서는 국내 보험회사의 해지율 통계를 적용한 주성분분석(PCA)모형을 통해 해지리스크를 측정하여 경과기간별 해지율간 상관성을 제거할 경우 상관성을 제거하기 전 해지리스크 규모 대비 금리연동형 저축보험은 26.9%, 금리연동형 종신보험은 25.9% 감소한다는 실증분석 결과를 제시하였다. 이러한 실증분석 결과를 볼 때, 정교한 해지리스크 측정을 위해서는 경과기간별 해지율 변동의 상관성이 제거될 필요가 있으며 이에 대한 효과적인 방법론으로 주성분분석모형을 지급여력제도의 해지리스크 측정에 적용할 수 있을 것으로 사료된다. 모든 보험회사에 공통적으로 적용되는 지급여력제도 표준모형에서는 해지리스크의 정교한 측정과 함께 해지리스크 측정결과의 회사별 비교가능성 제고방안 등이 종합적으로 고려되어야 하기 때문에, 향후 새로운 지급여력제도의 도입시 주성분분석모형에 의한 해지리스크 측정모형의 비교가능성 제고방안 등 정책적 방법론에 대한 연구도 필요할 것으로 판단된다.
While most financial institutions have significantly enhanced their traditional risk management capabilities over the past decade, these organisations — and their boards of directors — are often less prepared when facing extreme and multi-faceted uncertainty. Preparing for uncertainty, particularly over long time horizons, is much more complex than preparing for particular risk events, as it means taking account of unknown unknowns. To help their organisations navigate extreme uncertainty, boards report that they are focusing on one topic above all: resilience. Leveraging insights from interviews with nearly 1,000 board members, this paper outlines the forms of resilience that should be bolstered and recommends three actions boards should take to build resilience. Specifically, boards should consider and promote operational resilience, organisational resilience, reputational resilience and business-model resilience, in addition to the more familiar financial resilience. To build resilience and prepare for uncertainty, this paper proposes that boards of financial institutions should take three key steps: first, they must understand the main drivers of uncertainty that will impact their operating environment. Secondly, based on these drivers of uncertainty, they should look at the specific financial and operational implications for the company and consider scenario and contingency planning. Thirdly, boards should set clear expectations for management — including setting an appropriate risk appetite, detecting risks and control weaknesses, developing responses, and setting clear metrics — and hold management accountable for strong performance and stewardship of risk.
The purpose of the paper is to shed light on the looming risk of developing country debt defaults for financial institutions in the wake of the pandemic crisis. There are mounting calls to delink debt relief and conditions on developing countries so that debt cancellations should be delivered immediately without performance criteria or record of accomplishment. To date, however, debt cancellations have not sufficiently distinguished developing country beneficiaries according to their performance in sustainable development policies, neither have cancellations taken into account commitments towards improved governance trajectories, despite the requirements of poverty reduction programmes involving civil society. International financial institutions thus face the risk of large write-offs at a time of portfolio fragility due to an environment of low interest rates, meager profitability and weak economic growth. This paper argues that much of the resistance of private creditors comes from deeply rooted skepticism as to whether debt relief and write-offs lead to sustained improvement in creditworthiness. Accordingly, prudent risk management requires resisting calls for blanket debt relief when there is little scope for improved governance. Financial institutions should insist on strict criteria regarding inclusive development policies. As new legislation to facilitate debt-restructuring agreements, likely at the expense of private financial institutions, is currently being discussed, the insistence on ‘fair burden sharing’ between official and private creditors should be a wake-up call for banks. A range of financial risk management instruments could link debt relief with enhanced governance commitments, including debt swaps, recapture clauses and the monitored recycling of debt-servicing relief into high-priority projects. The pandemic crisis provides financial institutions with an opportunity to transform debt relief into a leverage for improving sustainable development prospects, hence better creditworthiness.
The drastic change in the Earth’s climate is a key concern for central bank risk managers. The consequences of climate change for the economy are harmful and potentially far-reaching. Central banks are exposed to climate change through their asset purchase programmes and credit operations and via the impact of climate change on the economy in general. Risk management in this case is challenging and complex because climate change is surrounded by fundamental uncertainty. Given this fundamental uncertainty, risk managers can however rely on the precautionary principle for practical purposes. This principle aims to anticipate and minimise the potential impact of serious or irreversible events under conditions of uncertainty. Stronger risk mitigating measures taken at an early stage serve as a hedge against the cost of enduring temporary catastrophes or draconian interventions at a later stage. This paper discusses the fundamental uncertainty of climate change and offers some recommendations for the identification, assessment, mitigation and disclosure of climate change uncertainty in central bank risk management. These recommendations are however equally relevant for commercial banks and institutional investors.
Two upper bounds for ruin probability under the discrete time risk model for insurance controlled by two factors: proportional reinsurance and surplus investment are presented. The latter is of interest because of the assumption that insurers invest some or their entire financial surplus on both the stock and bond markets, for which bond interest rates follow a time - homogeneous Markov chain. In addition, the control of reinsurance and stock investment in each time period are assumed to be constant values. The first upper bound for finite time ruin probability and ultimate ruin probability was derived under the condition that the Lundberg coefficient exists. The second upper bound is for finite time ruin probability and was developed from a new worse than used function. Numerical examples are used to illustrate these results, and the upper bound of ruin probability using real-life motor insurance claims data from a broker is also presented.
The model use of artificial intelligence (AI) and machine learning (ML) has caused unprecedented sensation around the wide applicability of these techniques. The rapid adoption of those alternative tools and methodologies by the heavily regulated financial sector, in areas that are outside the conventional credit lending and market participation, has posed significant challenges for model risk management professionals, including correctly defining AI and ML, properly establishing a governance framework, and, most importantly, effectively challenging AI/ML models. In this paper, the author attempts to describe the history of AI/ML, the evolution of key mathematical theories and modelling, commonalities and distinctions between statistical models and ML algorithms, and challenges of evaluation of some ML models. She discusses plausible solutions to practically address those challenges.
Since the onset of the COVID-19 recession, loss forecasting and stress testing models have dramatically overpredicted losses. As all models are pattern recognisers trained on past events, such an unprecedented event inevitably leads to model errors. Rather than, however, view the models as broken, they are useful in providing an upper bound of what could have happened if government assistance and loan forbearance had not been provided. The present work develops an approach for quantifying the short- and long-term impacts of these government and lender policies in order to create quantitative model overlays. These overlays express the problem via a set of key parameters that can be set via management judgment or simulation studies. Examples of this approach and parameter sensitivity analysis are provided using time series models of National Credit Union Administration and Federal Deposit Insurance Corporation call report data. This paper provides a framework for incorporating simulations, simple to complex, into an existing stress testing framework to better project future losses.
Conduct risk refers to behaviours of firms, including financial institutions, which may result in poor outcomes for the consumer. Conduct risk arises in financial institutions due to the nature of various client relationships, many of which include fiduciary duties, as well as due to the impact that financial institutions make on the world’s financial markets. Financial institutions have always managed conduct risk. In the years since the financial crisis, conduct risk has been the subject of increasing scrutiny, as regulators across jurisdictions expanded requirements to address various types of misconduct. The coronavirus disease 2019 (COVID-19) associated health and economic crisis has created new pressures, incentives and opportunities that can lead to heightened conduct risk exposure as institutions adapt to an ever-increasing volatile market and changes to their operations and control environment (eg professionals now must work from home). As individuals attempt to exploit the pandemic, both the institutions and customers are at greater risk. Regulators, aware of the changes brought about by COVID-19, continue to expect firms to take responsibility, and identify and manage their risks and regulatory obligations. COVID-19-heightened conduct risk exposes financial institutions to large fines and penalties, regulator imposed business restrictions and brand dilution. Senior management face potential regulatory disciplinary action and loss of professional reputation. On 1st June, 2020, the Criminal Division of the US Department of Justice (DOJ) published updates to its guidance on the Evaluation of Corporate Compliance Programmes. This guidance helps institutions to assess the effectiveness of their compliance programme through the consideration of various factors, including, but not limited to, the company’s size, industry, geographic footprint, regulatory landscape, and other factors, both internal and external to the company’s operations, that might impact its compliance programme. This paper suggests practical steps to identify and mitigate increased conduct risk arising from COVID-19. Financial institutions subject to US jurisdiction can apply these same steps to meet the June 2020 updated US DOJ criteria of corporate compliance programmes and enable firms to assess the effectiveness of its compliance programme in identifying and managing risks arising from COVID-19. Companies that meet the DOJ criteria earn substantially reduced penalties and stand a good chance of avoiding criminal charges and a government-imposed monitor. The first step is to transfigure (mis)perceptions that Conduct Risk Management is bad for business and convert detractors into supporters by demonstrating a positive ‘return on investment’. It is essential to include stakeholders across first line of defense business units and second line of defense control functions. Firms should fully document efforts to address conduct risk and ensure a culture of compliance and integrity so that the organisation gets full credit for its work to prevent and detect misconduct, should a regulatory inquiry arise. With this firm foundation, financial institutions should update the conduct risk assessment as an ineffective risk assessment is the common root cause for corporate scandals. Once they identify new and emerging inherent risks, financial firms should test the efficacy of responsive policies, processes and controls to determine residual risks that create a reasonable likelihood of significant legal, reputational or financial impacts arising from misconduct strengthening or expanding forensic data science and analytics can be particularly helpful in limiting opportunities for would-be wrongdoers. With this effective framework in place, financial institutions can mitigate often overlooked or underestimated conduct risks either amid a crisis or under business-as-usual conditions.
AbstractThe accelerated failure‐time model assumes a survival function of the formS(t) = S0(θt), whereS0is an underlying survival function and θ may depend on a number of covariates. This is equivalent to a location‐shift model for the log failure time, and in particular to a loglinear regression model when θ is loglinear. Parametric and semiparametric approaches to analyses based on these models with possibly censored failure‐time responses are reviewed. The accelerated failure‐time assumption is compared and contrasted with that of proportional hazards.
