Very little explicit feedback has been heard from the Basel Committee concerning BCBS 239 since the deadline was not reached. There is no mention of BCBS 239 in either the ‘Tenth progress report on the adoption of the Basel regulatory framework’, or any of the ‘Regulatory Consistency Assessment Program (RCAP), Assessment of Basel 3 G-SIB framework and review of D-SIB frameworks’ reports (save for a brief mention in the report for Japan). This may be due to the slow progress of banks to implement BCBS 239, or that national supervisors are unsure about how to measure compliance for the principles without benchmarks or examples of successful implementation.

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**Organizational support for implementing and running IFRS 9 will require change,** through greater involvement of different departments that hitherto have not been as directly active in finance activities. These particularly include risk and regulatory reporting.

While data federation is certainly not a new concept, organizations currently facing a new era of increased reporting requirements and lower yields, coupled with growing markets and investors on the hunt for the next new opportunity, are searching for ways to use data more efficiently and effectively.

“The trend towards federation is mainly happening because data volumes are growing at such a pace, and also different types of data are growing,” says Edward Boag, chief data officer at Pioneer Investments. “It’s becoming more difficult to collect everything in one place and link it all in the old-fashioned way. It’s much easier to federate data, leaving it where it is and putting links in place instead.”

]]>Instances of fraud and theft targeted at FIs and their customers are on the rise, and for today’s FIs, preventing them is a huge challenge. The world in which they operate is becoming ever more complex, as innovations in mobile and online technology bring benefits – but also new loopholes for criminals to exploit.

]]>This whitepaper will explore the main properties of topological analysis and explain how shapes are measured and represented. It also shows how TDA provides a framework for machine learning and why it provides a way to understand the overall organization of the data directly.

]]>This presentation contains forward-looking statements. These statements may be identified by words such as "expect", "should", "could", "shall", and similar expressions. Wolters Kluwer cautions that such forward-looking statements are qualified by certain risks and uncertainties that could cause actual results and events to differ materially from what is contemplated by the forward-looking statements. Factors which could cause actual results to differ from these forward-looking statements may include, without limitation, general economic conditions, conditions in the markets in which Wolters Kluwer is engaged, behavior of customers, suppliers and competitors, technological developments, the implementation and execution of new ICT systems or outsourcing, legal, tax, and regulatory rules affecting Wolters Kluwer's businesses, as well as risks related to mergers, acquisitions and divestments. In addition, financial risks, such as currency movements, interest rate fluctuations, liquidity and credit risks could influence future results. The foregoing list of factors should not be construed as exhaustive. Wolters Kluwer disclaims any intention or obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise.

Growth rates are cited in constant currencies unless otherwise noted.

]]>With the forthcoming FRTB standards due to be implemented at the end of 2019, banks have been busy assessing capital impacts. When combined with other regulations such as increased Basel III capital requirements, Dodd Frank Volcker Rule, bilateral margining requirements, IFRS 9, IRRBB, SA-CCR and SA-CVA, capital consumption and performance have become a greater focus more than ever. As each desk needs separate approval for IMA as opposed to SA, the choice of desk structures of banks will have significant influence on the overall market risk capital requirements. The BCBS-352 (FRTB) regulations define trading desks as “a group of traders or trading accounts that implements a well-defined business strategy operating within a clear risk management strategy”2. In order for banks to determine capital impacts before implementation, a series of hypothetical scenarios should be set up relevant to the firm’s portfolios. The baseline test would be the current desk structure post Volcker rule (for the U.S. and Canada) and production portfolios, whereby both the standardized and internal model capital results are calculated and the minimum of the sum of both approaches are calculated to arrive at a probable desk structure.

]]>In recent times, however − partly in response to the credit crisis in 2008 − the discipline of model risk management (MRM) has become more formalized and rigorous, driving the need for enterprise-level model information management systems. The regulatory scrutiny being applied to them is intensifying and spreading globally, with US and European regulators leading the charge. For example, whereas regulators were previously more interested in the numbers they were provided, now more regulators want to have a core understanding of the models banks used to generate these number

]]>We determined that in many cases, sellers are configuring best execution pricing algorithms to penalize correspondents to some extent for delivery factors such as turn time and overlays, and this practice varies based on product. Some correspondent lenders are losing market share to the cash window with the top three reasons being cited as less inspection content, rep and warrant relief, and turn times. Co-Issuance is providing specific turn time advantages of nless than seven days and the correspondents use different inspection criteria if they are not servicing the loan, focusing mainly on investor scalability and securitization requirements while expediting the process with compliance tools. Several correspondents were found to be reducing the upfront standard pre-purchase review.

]]>The Millennial Generation is now firmly embedded in the workplace, including in internal audit and compliance departments. A recent Deloitte Millennial Survey says that “Millennials, who are already emerging as leaders in technology and other industries, will comprise 75 percent of the global workforce by 2025.” From a management perspective, we need to blend the realities of the work we do with the sociable, optimistic, collaborative, tech savvy, and achievement oriented Millennial staff.

]]>Risk measures, such as Expected Shortfall and Value at Risk, are designed to calculate the risk level of a portfolio. But some risk models may work better than others for different asset classes and for different periods of time. We ranked four types of models using the MSCI Model Scorecard, an innovative tool that measures how well a model has predicted risk, either with Expected Shortfall (ES) or Value at Risk (VaR).

]]>In an industry that is renowned for acronyms, these concepts have achieved outstanding success in garnering more than we have seen for any other domain: IDM, IDG, IAM, IMG and IAG to name a few. This paper will concentrate on the term IAG as defined above but will also touch on Identity Management (IDM) and Identity Access Management (IAM) to add context.

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** N**

∑**(1−R)q _{i}ν_{i}**

i=1

where **q _{i}** is the (risk-neutral) probability of a counterparty default in the

When calculations are carried out in the usual way the probability of default, **q _{i}** is assumed to be independent of the expected exposure,

Both wrong-way and right-way risk are observed in practice. Consider the case of a hedge fund that has a large unhedged portfolio of derivatives with a dealer. If the market variables that drive the value of the derivatives move in such a way that the value of the portfolio to the hedge fund is negative, the hedge fund is losing money. If the losses are sufficiently large the hedge fund will default. At the same time the value of the portfolio to the dealer is positive so the dealer’s exposure is large. This results in wrong-way risk for the dealer. The probability of default is large when the dealer’s exposure is large.

Alternatively, consider the case of a gold producer that has used derivatives to hedge 50% of its production by entering into forward contracts in which the dealer agrees to buy gold in the future at a specified price. In this case there is likely to be right-way risk. The gold producer is likely to default when the price of gold is low and it is losing money on the unhedged part of its production. But when the price of gold is low the value of the forward contracts to the dealer is also low. In this case, the derivatives have a positive value to the gold producer and a negative value to the derivatives dealer. The dealer’s exposure is therefore likely to be low when the gold producer defaults.

A simple way of dealing with wrong-way risk is to multiply the expected exposure **ν _{i}** by a factor “alpha” in the version of the model in which

We propose a different approach, which we first suggested in Hull and White (2012). Instead of changing the calculation of the expected exposures, the **v’s**, we change the calculation of the probability of default, the **q’s**. The probability of default between time **t** and time **t+δt**, the hazard rate **h(t)**, depends on the evolution of the market variables in the Monte Carlo simulation used to calculate CVA up until time **t**. We model a relationship between the hazard rate of a counterparty and a variable (or variables) that can be calculated in the Monte Carlo simulation and may affect the dealer’s exposure to the counterparty. This relationship can be either deterministic or stochastic. There are three ways of proceeding:

1. Assume a relationship between the counterparty’s hazard rate and a variable, **x**, that is closely related to the dealer’s exposure to the counterparty. A reasonable approach here is to set **x** equal to the value of the dealer’s portfolio with the counterparty. If there is no relationship between the hazard rate and the portfolio value then there is no wrong way or right way risk. A positive relationship is indicative of wrong way risk and a negative relationship is indicative of right-way risk.

2. Assume a relationship between the counterparty’s hazard rate and a variable, **x**, that a) affects the value of the counterparty’s portfolio and b) has a big effect on the counterparty’s health. This variable, because it affects the value of the counterparty’s portfolio, is already part of the Monte Carlo simulation. The variable chosen for a gold producer might be the price of gold. For another company, it could be an exchange rate or interest rate.

3. Assume a relationship between the counterparty’s hazard rate and a variable **x** that does not affect the value of the counterparty’s portfolio, but potentially has a big effect on its health. Possible choices for **x** are the counterparty’s five-year credit spread, its stock price, or the Moody’s KMV distance to default for the counterparty. Daily historical data must be used to estimate correlations between the variable chosen and the other variables in the Monte Carlo simulation so that the process assumed for the variable is appropriately modeled.

The relationship between the hazard rate **h** and the variable **x** has the form

** h(t)=f(x(t))**

for some function** f**. The function can involve a noise term (although in practice we find this makes very little difference). The function chosen may depend on the nature of the variable **x **and the nature of the company. The function **f **must have the property that **h(t)≥0** for all possible values of **x(t)** and the noise term, if any. It must also have the property that for all times** t** the expected probability that there is no default before time** t **based on the random hazard rates equals the probability of no default before time **t** that is inferred from credit spreads at time zero. This means that the expected value of **exp(−h-t)** must equal **exp(−st/(1−R))** where **h¯** is the average hazard rate between time zero and time** t**, **s** is the counterparty’s credit spread for maturity** t**, and expectations are taken over all possible paths that **x** may follow. To allow the condition to be satisfied, the function **f** must involve a parameter which is a function of time. A simple model which we have used is

** h(t)=exp[a(t)+bx(t)]**

Here **b** is a parameter determining the impact the changes in **x** on proportional changes in the hazard rate and **a(t) **is a function of time chosen to match credit spreads.

In light of the low entry barriers to cybersecurity attacks in banks, it is incumbent upon them to invest in systems and technologies that go beyond merely pre-empting an attack.

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