What CECL Means To Investors
Recent updates to U.S. GAAP will dramatically change the way financial institutions incorporate credit risk into their financial statements. The new method is called the Current Expected Credit Loss (CECL) model and will take effect over the next few years. For many institutions, CECL will mean a one-time reduction in book equity and lower stated earnings during periods of portfolio growth. These reductions occur because CECL implicitly double-counts credit risk from the time of loan origination, as we will meticulously demonstrate. But for investors, will the accounting change alter the value of your shares?
Three Distinct Measures of Value
To answer this question well, we need to parse three distinct measures of value:
1. Book Value: This is total shareholders’ equity as reported in financial reports like 10-Ks and annual reports prepared in accordance with U.S. GAAP.
2. Current Market Value (also known as Market Cap): Current share price multiplied by the number of outstanding shares. This is the market’s collective opinion of the value of your institution. It could be very similar to, or quite different from, book value, and may change from minute to minute.
3. Intrinsic Value (also known as Fundamental Value or True Value): The price that a rational investor with perfect knowledge of an institution’s characteristics would be willing to pay for its shares. It is by comparing an estimate of intrinsic value versus current market value that we deem a stock over- or under-priced. Investors with a long-term interest in a company should be concerned with its intrinsic or true value.
How Does an Accounting Change Affect Each Measure of Value?
Accounting standards govern financial statements, which investors then interpret. An informed, rational investor will “look through” any accounting quirk that distorts the true economics of an enterprise. Book value, therefore, is the only measure of value that an accounting change directly affects.
An accounting change may indirectly affect the true value of a company if costly regulations kick in as a result of a lower book value or if the operational cost of complying with the new standard is cumbersome. These are some of the risks to fundamental value from CECL, which we discuss later, along with potential mitigants.
Key Feature of CECL: Double-Counting Credit Risk
The single-most important thing for investors to understand about CECL is that it double-counts the credit risk of loans in a way that artificially reduces stated earnings and the book values of assets and equity at the time a loan is originated. It is not the intent of CECL to double-count credit risk, but it has that effect, as noted by no less authorities than the two members of the Financial Accounting Standards Board (FASB) who dissented from the rule. (CECL was adopted by a 5-2 vote.)
Consider this simple example of CECL accounting: A bank makes a loan with an original principal balance of $100. CECL requires the bank to recognize an expense equal to the present value of expected credit losses[i] and to record a credit allowance that reduces net assets by this same amount. Suppose we immediately reserve our $100 loan down to a net book value of $99 and book a $1 expense. Why did we even make the loan? Why did we spend $100 on something our accountant says is worth $99? Is lending for suckers?
Intuitively, consider that to make a loan of $100 is to buy a financial asset for a price of $100. If other banks would have made the same loan at the same interest rate (which is to say, they would have paid the same price for the same asset), then our loan’s original principal balance was equal to its fair market value at the time of origination. It is critical to understand that an asset’s fair market value is the price which market participants would pay after considering all of the asset’s risks, including credit risk. Thus, any further allowance for credit risk below the original principal balance is a double-counting of credit risk.
Here’s the underlying math: Suppose the $100 loan is a one-year loan, with a single principal and interest payment due at maturity. If the note rate is 5%, the contractual cash flow is $105 next year. This $105 is the most we can receive; we receive it if no default occurs. What is the present value of the $105 we hope to receive? One way to determine it is to discount the full $105 amount by a discount rate that reflects the risk of nonpayment. We established that 5% is the rate of return that banks are requiring of borrowers presenting similar credit risk, so an easy present value calculation is to discount next year’s contractual $105 cash flow by the 5% contractual interest rate, i.e., $105 / (1 + 5%) = $100. Alternatively, we could reduce the contractual cash flow of $105 by some estimate of credit risk. Say we estimate that if we made many loans like this one, we would collect an average of $104 per loan. Our expected future cash flow, then, is $104. If we take the market value of $100 for this loan as an anchor point, then the market’s required rate of return for expected cash flows must be 4%. ($104 / (1 + 4%) = $100.) It is only sensible that the market requires a lower rate of return on cash flows with greater certainty of collection.
What the CECL standard does is require banks to discount the lower expected cash flows at the higher contractual rate (or to use non-discounting techniques that have the same effect). This would be like discounting $104 at 5% and calculating a fair market value for the asset of $104 / (1 + 5%) ≈ $99. This (CECL’s) method double-counts credit risk by $1. The graph below shows the proper relationship between cash flow forecasts and discount rates when performing present value calculations, and shows how CECL plots off the line.
Proper Valuation Combinations (—)
FASB Vice Chairman James Kroeker and Board member Lawrence Smith described the double-counting issue in their dissent to the standards update: “When performing a present value calculation of future cash flows, it is inappropriate to reflect credit risk in both the expected future cash flows and the discount rate because doing so effectively double counts the reflection of credit risk in that present value calculation. If estimates of future cash flows reflect the risk of nonpayment, then the discount rate should be closer to risk-free. If estimates of future cash flows are based on contractual amounts (and thus do not reflect a nonpayment risk), the discount rate should be higher to reflect assumptions about future defaults.” Ultimately, the revised standard “results in financial reporting that does not faithfully reflect the economics of lending activities.”[ii]
The Accounting Standards Update notes two tangential counterpoints to Kroeker and Smith’s dissent. The first point is that banks would find alternative methods challenging, which may be true but is irrelevant to the question of whether CECL faithfully reflects true economics. The second point is that the valuation principles Kroeker and Smith lay out are for fair value estimates, whereas the accounting standard is not intended to produce fair value estimates. This concedes the only point we are trying to make, which is that the accounting treatment deviates (downwardly, in this case) from the fundamental and market value that an investor should care about.
How CECL Affects Each Measure of Value
As noted previously, the direct consequences of CECL will hit book value. Rating agency Fitch estimates that the initial implementation of CECL would shave 25 to 50 bps off the aggregate tangible common equity ratio of US banks if applied in today’s economy. The ongoing impact of CECL will be less dramatic because the annual impact to stated earnings is just the year-over-year change in CECL. Still, a growing portfolio would likely add to its CECL reserve every year.[iii]
There are many indirect consequences of CECL that may affect market and true value:
1. Leverage: The combination of lower book values from CECL with regulations that limit leverage on the basis of book value could force some banks to issue equity or retain earnings to de-leverage their balance sheet. Consider these points:
a. There is a strong argument to be made to regulators that the capital requirements that pre-dated CECL, if not adjusted for the more conservative asset calculations of CECL, will have become more conservative de facto than they were meant to be. There is no indication that regulators are considering such an adjustment, however. A joint statement on CECL from the major regulators tells financial institutions to “[plan] for the potential impact of the new accounting standard on capital.”[iv]
b. Withholding a dividend payment does not automatically reduce a firm’s true value. If the enterprise can put retained earnings to profitable use, the dollar that wasn’t paid out to investors this year can appreciate into a larger payment later.
c. The deeper threat to value (across all three measures) comes if regulations force a permanent de-leveraging of the balance sheet. This action would shift the capital mix away from tax-advantaged debt and toward equity, increase the after-tax cost of capital and decrease earnings and cash flow per share, all else equal.
Because banks face the shift to CECL together, however, they may be able to pass greater capital costs on to their borrowers in the form of higher fees or higher interest rates.
d. Banks can help themselves in a variety of ways. The more accurate a bank’s loss forecasts prove to be, the more stable its loss reserve will be, and the less likely regulators are to require additional capital buffers. Management can also disclose whether their existing capital buffers are sufficient to absorb the projected impact of CECL without altering capital plans. Conceivably, management could elect to account for its loans under the fair value option to avoid CECL’s double-counting bias, but this would introduce market volatility to stated earnings which could prompt its own capital buffers.
2. Investor Perception of Credit Risk: Investors’ perception of the credit risk a bank faces affects its market value. If an increase in credit allowance due to CECL causes investors to worry that a bank faces greater credit risk than they previously understood, the bank’s market value will fall on this reassessment. On the other hand, if investors have independently assessed the credit risk borne by an institution, a mere change in accounting treatment will not affect their view. An institution’s true value comes from the cash flows that a perfectly informed investor would expect. Unless CECL changes the kinds of loans an institution makes or the securities it purchases, its true credit risk has not changed, and nothing the accounting statements say can change that.
3. Actual Changes in Credit Risk: Some banks may react to CECL by shifting their portfolio mix toward shorter duration or less credit risky investments, in an effort to mitigate CECL’s impact on their book value. If underwriting unique and risky credits was a core competency of these banks, and they shift toward safer assets with which they have no special advantage, this change could hurt their market and fundamental value.
4. Volatility: ABA argues that the inherent inaccuracies of forecasts over long time horizons will increase the volatility of the loss reserve under CECL.[vi] Keefe, Bruyette & Woods (KBW) goes the other way, writing that CECL should reduce the cyclicality of stated earnings.[vii] KBW’s point can loosely be understood by considering that long-term averages are more stable than short-term averages, and short-term averages drive existing loss reserves. Certainly, if up-front CECL estimates are accurate, even major swings in charge-offs can be absorbed without a change in the reserve as long as the pattern of charge-offs evolves as expected. While cash flow volatility would hurt fundamental value, the concern from volatility of stated earnings is that it could exacerbate capital buffers required by regulators.
5. Transparency: All else equal, investors prefer a company whose risks are more fully and clearly disclosed. KBW reasons that the increased transparency required by CECL will have a favorable impact on financial stock prices.[viii]
6. Comparability Hindered: CECL allows management to choose from a range of modeling techniques and even to choose the macroeconomic assumptions that influence its loss reserve, so long as the forecast is defensible and used firm-wide. Given this flexibility, two identical portfolios could show different loss reserves based on the conservatism or aggressiveness of management. This situation will make peer comparisons impossible unless disclosures are adequate and investors put in the work to interpret them. Management can help investors understand, for example, if its loss reserve is larger because its economic forecast is more conservative, as opposed to because its portfolio is riskier.
7. Operational Costs: Complying with CECL requires data capacity and modeling resources that could increase operational costs. The American Bankers Association notes that such costs could be “huge.”[ix] Management can advise stakeholders whether it expects CECL to raise its operational costs materially. If compliance costs are material, they will affect all measures of value to the extent that they cannot be passed on to borrowers. As noted earlier, the fact that all US financial institutions face the shift to CECL together increases the likelihood of their being able to pass costs on to borrowers.
8. Better Intelligence: Conceivably, the enhancements to data collection and credit modeling required by CECL could improve banks’ ability to price loans and screen credit risks. These effects would increase all three measures of value.
CECL is likely to reduce the book value of most financial institutions. If regulators limit leverage because of lower book equity or the operational costs of CECL are material, and these costs cannot be transferred on to borrowers, then market values and fundamental values will also sag. If banks react to the standard by pulling back from the kinds of loans that have been their core competency, this, too, will hurt fundamental value. On the positive side, the required investment in credit risk modeling offers the opportunity for banks to better screen and price their loans.
Bank management can provide disclosures to analysts and investors to help them understand any changes to the bank’s loan profile, fee and interest income, capital structure and operational costs. Additionally, by optimizing the accuracy of its loss forecasts, management can contain the volatility of its CECL estimate and minimize the likelihood of facing further limitations on leverage.
[i] The term “expected loss” can be confusing; it does not necessarily mean that default is likely. If you have a 1% chance of losing $100, then your “expected loss” is 1% × $100 = $1. As long as a loan is riskier than a Treasury, your expected loss is greater than zero.
[ii] FASB Accounting Standards Update 2016-13, p. 237 and p. 235 http://www.fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1176168232528&acceptedDisclaimer=true
[iii] By the end of a loan’s life, all interest actually collected and credit losses realized have been reflected in book income, and associated loss reserves are released, so lifetime interest income and credit losses are the same under any standard.
[iv] Joint Statement on the New Accounting Standard on Financial Instruments – Credit Losses. https://www.federalreserve.gov/newsevents/press/bcreg/bcreg20160617b1.pdf
Modigliani, Franco and Miller, Merton H. (1963) Corporate Income Taxes and the Cost of Capital: A Correction. The American Economic Review, Vol. 53, No. 3, pp. 433-443. https://www.jstor.org/stable/1809167?seq=1#page_scan_tab_contents
[vi] Gullette, Mike. (2016) FASB’s Current Expected Credit Loss Model for Credit Loss Accounting (CECL). American Bankers Association.
[vii] Kleinhanzl, Brian, et al. FASB is About to Accelerate Loan Loss Recognition for the Financial Industry. Keefe, Bruyette & Woods.
[viii] Kleinhanzl, Brian, et al, p. 1.
[ix] Gullette, Mike. (2016), p. 4.
Preparing for Model Validation: Ideas for Model Owners
Though not its intent, model validation can be disruptive to model owners and others seeking to carry out their day-to-day work. We have performed enough model validations over the past decade to have learned how cumbersome the process can be to business unit model owners and others we inconvenience with what at times must feel like an endless barrage of touch-point meetings, documentation requests and other questions relating to modeling inputs, outputs, and procedures.
We recognize that the only thing these business units did to deserve this inconvenience was to devise or procure a methodology for systematically improving how something gets estimated. In some cases, the business owner of an application tagged for validation may view it simply as a calculator or other tool, and not as a “model.” And in some cases we agree with the business owner. But in every case, the system under review has been designated as a model requiring validation either by an independent risk management department within the institution or (worse) by a regulator, and so, the validation project must be completed.
As with so many things in life, when it comes to model validation preparation, an ounce of prevention goes a long way. Here are some ideas model owners might consider for making their next model validation a little less stressful.
Overall Model Documentation
Among the first questions we ask at the beginning of a model validation is whether the model has been validated before. In reality, however, we can make a fairly reliable guess about the model’s validation history simply by reading the model owner’s documentation. A comprehensive set of documentation that clearly articulates the model’s purpose, its inputs’ sources, how it works, what happens to the outputs and how the outputs are monitored is an almost sure sign that the model in question has been validated multiple times.
In contrast, it’s generally apparent that the model is being validated for the first time when our initial request for documentation yields one or more of the following:
- An 800-page user guide from the model’s vendor, but no internally developed documentation or procedures
- Incomplete (or absent) lists of model inputs with little or no discussion of how inputs and assumptions are obtained, verified, or used in the model
- No discussion of the model’s limitations
- Perfunctory monitoring procedures, such as, “The outputs are reviewed by an analyst for reasonableness”
- Vague (or absent) descriptions of the model’s outputs and how they are used
- Change logs with just one or two entries
No one likes to write model documentation. There never seems to be enough time to write model documentation. Compounding this challenge is the fact that model validations frequently seem to occur at the most inopportune moments for model owners. A bank’s DFAST models, for example, often undergo validation while the business owners who use them are busy preparing the bank’s DFAST submission. This is not the best time to be tweaking documentation and assembling data for validators.
Documentation would ideally be prepared during periods of lower operational stress. Model owners can accomplish this by predicting and staying in front of requests from model risk management by independently generating documentation for all their models that satisfies the following basic criteria:
- Identifies the model’s purpose, including its business and functional requirements, and who is responsible for using and maintaining the model
- Comprehensively lists and justifies of the model’s inputs and assumptions
- Describes the model’s overall theory and approach, i.e., how the model goes about transforming the inputs and assumptions into reliable outputs (including VBA or other computer code if the model was developed in house)
- Lays out the developmental evidence supporting the model
- Identifies the limitations of the model
- Explains how the model is controlled—who can access it, who can change it, what sorts of approvals are required for different types of changes
- Comprehensively identifies and describes the model’s outputs, how they are used, and how they are tested
Any investment of time beforehand to incorporate the items above into the model’s documentation will pay dividends when the model validation begins. Being able to simply hand this information over to the validators will likely save model owners hours of attending follow-up meetings and fielding requests. Additional suggestions for getting the model’s inputs and outputs in order follow below.
All of the model’s inputs and assumptions need to be explicitly spelled out, as well as their relevance to the model, their source(s), and any processes used to determine their reliability. Simply emailing an Excel file containing the model and referring the validator to the ‘Inputs’ tab is probably going to result in more meetings, more questions, and more time siphoned out of the model owner’s workday by the validation team.
A useful approach for consolidating inputs and assumptions that might be scattered around different areas of the model involves the creation of a simple table that captures everything a validator is likely to ask about each of the model’s inputs and assumptions.
Systematically capturing all of the model’s inputs and assumptions in this way enable the validators to quickly take inventory of what needs to be tested without having to subject the model owner to a time-consuming battery of questions designed to make sure they haven’t missed anything.
Being prepared to explain to the validator all the model’s outputs individually and how each is used in reporting and downstream applications greatly facilitates the validation process. Accounting for all the uses of every output becomes more complicated when they are used outside the business unit, including as inputs to another model. At the discretion of the institution’s model risk management group, it may be sufficient to limit this exercise only to uses within the model owner’s purview and to reports provided to management. As with inputs, this can be facilitated by a table.
Outputs that impact directly on financial statements are especially important. Model validators are likely to give these outputs particular scrutiny and model owners would do well to be prepared to explain not only how such outputs are computed and verified, but how the audit trails surrounding them are maintained, as well.
To the extent that outputs are subjected to regular benchmarking, back-testing, or sensitivity analyses, these should be gathered as well.
A Series of Small Investments
A model owner might look at these suggestions and conclude that they seem like a lot of work just to get ready for a model validation. We agree. Bear in mind, however, that the model validator is almost certain to ask for these things at some point during the validation, when, chances are, a model owner is likely to wish she had the flexibility to do her real job. Making a series of small-time investments to assemble these items well in advance of the validator’s arrival not only will make the validation more tolerable for model owners but will likely improve the overall modeling process as well.
New Capital Planning Expectations for Large Financial Institutions and What It Means For You
The Federal Reserve Board (FRB) recently released regulatory guidance outlining its capital planning expectations for large financial companies. The guidance addresses many areas of the capital planning process where regulators are looking for continued improvement within large bank holding companies and attempts to clarify differences in the Fed’s expectations based on firm size and complexity. The guidance is effective for the 2016 CCAR cycle.
The Federal Reserve has provided separate guidance for two different categories of large financial institutions:
- LISCC Firms1 and ‘Large and Complex’ firms were provided capital planning guidance under SR 15-18, and
- ‘Large and Noncomplex’ firms were provided capital planning guidance under SR 15-19.
SR 15-18 Summary
Specifically, SR 15-18 applies to firms that:
- Are subject to the LISCC framework,
- Have total consolidated assets of $250 billion or more, or
- Have consolidated total on-balance sheet foreign exposure of $10 billion or more.
For the largest and most complex firms, the guidance clarifies expectations that have been previously communicated to firms, including through past Comprehensive Capital Analysis and Review (CCAR) exercises and related supervisory reviews.
SR 15-19 Summary
SR 15-19 applies to firms and ‘Large and Noncomplex’ institutions that:
- Are not otherwise subject to the LISCC framework,
- Have total consolidated assets between $50 billion and $250 billion, and
- Have total consolidated on-balance-sheet foreign exposure of less than $10 billion.
Implications of these capital planning guidelines
Both sets of guidelines (SR 15-18 and SR 15-19) lay out the governance, risk management, internal controls, capital policy, scenario design, and projection methodology expectations relating to the capital planning process. They also lay out some important distinctions between the two institution types relating to how models and model risk management are expected to be used.
We summarize some of the key differences between what is required of these two institution types in the table below.
Current 2017 LISCC Portfolio Firms
According to the Federal Reserve, here are the current LISCC firms:
- American International Group, Inc.
- Bank of America Corporation
- The Bank of New York Mellon Corporation
- Barclays PLC
- Citigroup Inc.
- Credit Suisse Group AG
- Deutsche Bank AG
- The Goldman Sachs Group, Inc.
- JP Morgan Chase & Co.
- Morgan Stanley
- Prudential Financial, Inc.
- State Street Corporation
- UBS AG
- Wells Fargo & Company
 Large Institution Supervision Coordinating Committee (LISCC) – the Board of Governors of the Federal Reserve has the responsibility for the supervision of systemically important financial institutions, including large bank holding companies, the U.S. operations of certain foreign banking organizations, and nonbank financial companies that are designated by the Financial Stability Oversight Council (FSOC) for supervision by the Board of Governors. A list of LISCC firms can be found at http://www.federalreserve.gov/bankinforeg/large-institution-supervision.htm.
RDARR: Principles for Effective Risk Data Aggregation and Risk Reporting
Background and Impetus for RDARR
The global financial crisis revealed that many banks had inadequate practices for timely, complete, and accurate aggregation of risk exposures. These limitations impaired their ability to generate reliable information to manage risks, especially during times of economic stress. These limitations resulted in severe consequences to individual banks and the entire financial system.
Whether or not your bank is designated as an SIB, we expect your regulator to apply the Principles. You may wish to proactively enhance your RDARR. RiskSpan’s RDARR Advisory Services team has decades of finance, accounting, data, and technology expertise to help banks meet these increasing supervisory expectations.
Responding to this pervasive systemic issue, the Basel Committee on Banking Supervision (BCBS) issued the “Principles for Effective Risk Data Aggregation and Risk Reporting” (RDARR).
Objectives of RDARR
The BCBS RDARR prescribes principles (the Principles) with the objective of strengthening risk data aggregation capabilities and internal risk reporting practices. Implementation of the Principles is expected to enhance risk management and decision-making processes in order to:
- Enhance infrastructure for reporting key information, particularly that used by the board and senior management to identify, monitor and manage risks;
- Improve decision-making processes;
- Enhance the management of information across legal entities, while facilitating a comprehensive assessment of risk exposures at a consolidated level;
- Reduce the probability and severity of losses resulting from risk management weaknesses;
- Improve the speed at which information is available and hence decisions can be made; and
- Improve the organization’s quality of strategic planning and the ability to manage the risk of new products and services.
The Principles of RDARR
Fourteen Principles are structured in four sections:
Overarching governance and infrastructure
2. Architecture/ Infrastructure
Risk data aggregation capabilities
3. Data Accuracy and Integrity
Risk reporting practices
7. Reports Accuracy
9. Clarity and Usefulness
Supervisory review, tools and cooperation
The BCBS prescribes requirements and practices for each Principle that define compliance.
Scope of RDARR
The Principles are initially prescribed to systemically important banks (SIBs) as designated by the international Financial Stability Board (FSB). Initially, they were expected to be fully implemented by January 1, 2016.
The BCBS “strongly” suggests that supervisory bodies apply the Principles to a wider range of banks, proportionate to the size, nature, and complexity of these banks’ operations.
Consistent with other recent supervisory pronouncements, we expect these principles to eventually be applied by other regulators.
Progress in Adopting RDARR
The BCBS has conducted multiple self-assessment surveys of SIBs to measure preparedness for compliance with the Principles and identify common challenges, along with potential strategies for compliance.
The survey results indicate many banks continue to encounter difficulties in establishing strong data aggregation governance, architecture and processes, often relying on manual workarounds. Many banks failed to recognize that governance/infrastructure practices are important prerequisites for facilitating compliance with the Principles.
Many banks indicated that they will be unable to comply with at least one Principle by the January 2016 deadline.
Impact of the Principles
This guidance has increased the required capabilities of RDARR for measuring and reporting risks.
The new paradigm for risk data aggregation and risk reporting imposes many new standards, most notably:
- A bank’s senior management should be fully aware of and understand the limitations that prevent full risk data aggregation.
- Controls surrounding risk data need to be as robust as those applicable to accounting data.
- Risk data should be reconciled with source systems, including accounting data where appropriate, to ensure that the risk data is accurate.
- A bank should strive towards a single authoritative source for risk data per each type of risk.
- Supervisors expect banks to document and explain all of their risk data aggregation processes whether automated or manual.
- Supervisors expect banks to consider accuracy requirements analogous to accounting materiality.
- Due to the wide and comprehensive scope of RDARR Principles, many SIBs have struggled to identify and implement the enhancements to facilitate full compliance.
Examples of RiskSpan RDARR Assistance Include:
- Interpret Principles and Requirements – Interpret the Principles and their application to your existing risk, data, risk reporting, IT infrastructure, data architecture, and quality.
- Assess Current Capabilities – Assess your existing risk data, risk reporting, IT infrastructure, data architecture, and data quality to identify gaps in the capabilities prescribed by the Principles.
- Develop and Implement Remediation – Develop and implement remediation plans to eliminate gaps and facilitate compliance.
- Develop and Implement Standard Risk Taxonomies – Develop standard risk taxonomies to meet the needs for risk reporting, regulatory compliance.
- Develop or Enhance Risk Reporting – Develop automated risk reporting dashboards for market, credit, and operational risk that are supported by reliable source data.
- Document and Assess End State RDARR – Develop good documentation of the end state to demonstrate compliance to regulators.
RiskSpan RDARR Advisory Services
Whether or not your bank is designated as a SIB, recent trends indicate that your regulator may soon expect you to apply the Principles. You will need to pro-actively enhance your RDARR.
The Basel Committee on Banking Supervision Principles for Effective Risk Data Aggregation and Risk Reporting guidance has increased the burden on you for measuring and reporting risks. This new paradigm for risk data aggregation and risk reporting imposes many new standards.
RiskSpan’s RDARR Advisory Services team has decades of finance, accounting, data, and technology expertise to help banks meet these increasing supervisory expectations.
About The Author
Steve Sloan, Director, CPA, CIA, CISA, CIDA, has extensive experience in the professional practices of risk management and internal audit, collaborating with management and audit committees to design and implement the infrastructures to obtain the required assurances over risk and controls.
He prescribes a disciplined approach, aligning stakeholders’ expectations with leading practices, to maximize the return on investment in risk functions. Steve holds a Bachelor of Science from Pennsylvania State University and has multiple certifications.