Change in CECL Approved by the FDIC

The Federal Deposit Insurance Corporation (FDIC) approved a measure that will allow a three-year phase in of the impact of CECL on regulatory capital yesterday (12/18/18). This change will also delay the impact on bank stress tests until 2020.  The change does not affect the rule itself but now allows banks the option to phase in impacts of CECL on regulatory capital over a three-year period. The details of this change can be found in the FDIC memorandum released yesterday.  The memorandum also adjusts how reserves for “bad loans” will be accounted for in regulatory capital.

The Financial Risk Group is recommending that banks utilize this time to better understand the impact, and the opportunities, that result from the mandated changes. “Time to implementation has been a limiting factor for some institutions to explore the identification of additional stakeholder value, but this should no longer be the case,” stated John Bell, FRG’s managing partner. FRG has (and is currently) partnered with clients of all types on a number of assessments and implementations of CECL.  The lessons to date regarding CECL are available in a number of our publications, including: CECL-Considerations, Developments, and Opportunities and Current Expected Credit Loss-Why The Expectations Are Different.

IFRS 17: Killing Two Birds

Time is ticking for the 450 insurers around the world to comply with the International Financial Reporting Standard 17 (IFRS 17) by January 1, 2021 for companies with their financial year starting on January 1.

Insurers are at different stages of preparation, ranging from performing gap analyses, to issuing requirements to software and consulting vendors, to starting the pilot phase with a new IFRS 17 system, with a few already embarking on implementing a full IFRS 17 system.

Unlike the banks, the insurance industry has historically spent less on large IT system revamps. This is in part due to the additional volume, frequency and variety of banking transactions compared to insurance transactions.

IFRS 17 is one of the biggest ‘people, process and technology’ revamp exercises for the insurance industry in a long while. The Big 4 firms have published a multitude of papers and videos on the Internet highlighting the impact of the new reporting standard on insurance contracts that was issued by the IASB in May 2017. In short, it is causing a buzz in the industry.

As efforts are focused on ensuring regulatory compliance to the new standard, insurers must also ask: “What other strategic value can be derived from our heavy investment in time, manpower and money in this whole exercise?”

The answer—analytics to gain deeper business insights.

One key objective of IFRS 17 is to provide information at a level of granularity that helps stakeholders assess the effect of insurance contracts on financial position, financial performance and cash flows, increasing transparency and comparability.

Most IFRS 17 systems in the market today achieves this by bringing the required data into the system, compute, report and integrate to the insurer’s GL system. From a technology perspective, such systems will comprise a data management tool, a data model, a computation engine and a reporting tool. However, most of these systems are not built to provide strategic value beyond pure IFRS 17 compliance.

Apart from the IFRS 17 data, an insurer can use this exercise to put in place an enterprise analytics platform that caters beyond IFRS 17 reporting, to broader and deeper financial analytics, to customer analytics, operational and risk analytics. To leverage on new predictive analytics technologies like machine learning and artificial intelligence, a robust enterprise data platform to house and make available large volumes of data (big data) is crucial.

Artificial Intelligence can empower important processes like claims analyses, asset management, risk calculation, and prevention. For instance, better forecasting of claims experience based on a larger variety and volume of real-time data. The same machine can be used to make informed decisions about investments based on intelligent algorithms, among other use cases.

As the collection of data becomes easier and more cost effective, Artificial Intelligence can drive whole new growths for the insurance industry.

The key is centralizing most of your data onto a robust enterprise platform to allow cross line of business insights and prediction.

As an insurer, if your firm has not embarked on such a platform, selecting a robust system that can cater to IFRS 17 requirements AND beyond will be a case of killing 2 birds with one stone.

FRG can help you and your teams get ready for IFRS 17.  Contact us today for more information.

Tan Cheng See is Director of Business Development and Operations for FRG.

Is Your Business Getting The Full Bang for Its CECL Buck?

Accounting and regulatory changes often require resources and efforts above and beyond “business as usual”, especially those like CECL that are significant departures from previous methods. The efforts needed can be as complex as those for a completely new technology implementation and can take precedence over projects that are designed to improve your core business … and stakeholder value.

But with foresight and proper planning, you can prepare for a change like CECL by leveraging resources in a way that will maximize your efforts to meet these new requirements while also enhancing business value. At Financial Risk Group, we take this approach with each of our clients. The key is to start by asking “how can I use this new requirement to generate revenue and maximize business performance?”

The Biggest Bang Theory

In the case of CECL, there are two significant areas that will create the biggest institution-wide impact: analytics and data governance. While the importance of these is hardly new to financial institutions, we are finding that many neglect to leverage their CECL data and analytics efforts to create that additional value. Some basic first steps you can take include the following.

  • Ensure that the data utilized is accurate and that its access and maintenance align to the needs and policies of your business. In the case of CECL these will be employed to create scenarios, model, and forecast … elements that the business can leverage to address sales, finance, and operational challenges.
  • For CECL, analytics and data are leveraged in a much more comprehensive fashion than previous methods of credit assessment provided.  Objectively assess the current state of these areas to understand how the efforts being put toward CECL implementation can be leveraged to enhance your current business environment.
  • Identify existing available resources. While some firms will need to spend significant effort creating new processes and resources to address CECL, others will use this as an opportunity to retire and re-invent current workflows and platforms.

Recognizing the business value of analytics and data may be intuitive, but what is often less intuitive is knowing which resources earmarked for CECL can be leveraged to realize that broader business value. The techniques and approaches we have put forward provide good perspective on the assessment and augmentation of processes and controls, but how can these changes be quantified? Institutions without in-house experienced resources are well advised to consider an external partner. The ability to leverage expertise of staff experienced in the newest approaches and methodologies will allow your internal team to focus on its core responsibilities.

Our experience with this type of work has provided some very specific results that illustrate the short-term and longer-term value realized. The example below shows the magnitude of change and benefits experienced by one of our clients: a mid-sized North American bank. A thorough assessment of its unique environment led to a redesign of processes and risk controls. The significant changes implemented resulted in less complexity, more consistency, and increased automation. Additionally, value was created for business units beyond the risk department. While different environments will yield different results, those illustrated through the methodologies set forth here provide a good example to better judge the outcome of a process and controls assessment.

 

 Legacy EnvironmentAutomated Environment
Reporting OutputNo daily available manual controls for risk reportingDaily in-cycle reporting controls are automated with minimum manual interaction
Process SpeedCredit run 40+ hours
Manually-input variables prone to mistakes
Credit run 4 hours
Cycle time reduced from 3 days to 1 for variable creation
Controls & AuditMultiple audit issues and Regulatory MRAsAudit issues resolved and MRA closed
Model ExecutionSpreadsheet driven90 models automated resulting in 1,000 manual spreadsheets eliminated

 

While one approach will not fit all firms, providing clients with an experienced perspective on more fully utilizing their specific investment in CECL allows them to make decisions for the business that might otherwise never be considered, thereby optimizing the investment in CECL and truly ensuring you receive the full value from your CECL buck.

More information on how you can prepare for—and drive additional value through—your CECL preparation is available on our website and includes:

White Paper – CECL: Why the expectations are different

White Paper – CECL Scenarios: Considerations, Development and Opportunities

Blog – Data Management: The Challenges

Current Expected Credit Loss (CECL) a New Paradigm for Captives, Too

The ramifications of CECL on Financial Institutions has in large part focused on Banks, but as we addressed in a recent paper, “Current Expected Credit Loss: Why the Expectations Are Different,” this new accounting treatment extends to a much larger universe.  An example of this are the captives that finance American’s love affair with cars; their portfolios of leases and loans have become much larger and the implications of CECL more significant.

As with other institutions, data, platforms, and modeling make up the challenges that captives will have to address.  But unlike other types of institutions captives have more concentrated portfolios, which may aid in “pooling” exercises, but may be inadvertently affected by scenario modeling.  A basic tenet for all institutions is the life-of-loan estimate and the use of reasonable and supportable forecasts.  While some institutions may have had “challenger” models in the past that moved in this direction, captives have not tended to utilize this type of approach in the past.

The growth of captives portfolios and the correlation to a number of macro-economic factors (e.g. interest rates, commodity prices, tariffs, etc.) call for data and scenarios that require a different level of modeling and forecasting.  Because FASB does not provide template methodologies or calculations it will be necessary to develop these scenarios with the mindset of the “reasonable and supportable” requirement.  While different approaches will likely be adopted, those that utilize transaction level data have the ability to provide a higher level of accuracy over time, resulting in the goals laid out in the new guidelines.  As might be imagined the ability to leverage experience in the development and deployment of these types of models can’t be overemphasized.

We have found that having the ability to manage the following functional components of the platform are critical to building a flexible platform that can manage the changing needs of the users:

  • Scenario Management
  • Input Data Mapping and Registry
  • Configuration Management
  • Model Management

Experience has taught that there are significant considerations in implementing CECL, but there are also some improvements that can be realized for institutions that develop a well-structured plan. Captives are advised to use this as an opportunity to realize efficiencies, primarily in technology and existing models. Considerations around data, platforms, and the models themselves should leverage available resources to ensure that investments made to address this change provide as much benefit as possible, both now and into the future.

IFRS 9: Evaluating Changes in Credit Risk

Determining whether an unimpaired asset’s credit risk has meaningfully increased since the asset was initially recognized is one of the most consequential issues banks encounter in complying with IFRS 9. Recall the stakes:

  • The expected credit loss for Stage 1 assets is calculated using the 12-month PD
  • The ECL for Stage 2 assets (defined as assets whose credit risk has significantly increased since they were first recognized on the bank’s books) is calculated using the lifetime PD, just as it is for Stage 3 assets (which are in default).

To make the difference more concrete, consider the following:

  • A bank extends an interest-bearing five-year loan of $1 million to Richmond Tool, a hypothetical Virginia-based tool, die, and mold maker serving the defense industry.
  • At origination, the lender estimates the PD for the next 12 months at 1.5%, the PD for the rest of the loan term at 4%, and the loss that would result from default at $750,000.
  • In a subsequent reporting period, the bank updates those figures to 2.5%, 7.3%, and $675,000, respectively.

If the loan were still considered a Stage 1 asset at the later reporting date, the ECL would be $16,875. But if it is deemed a Stage 2 or Stage 3 asset, then the ECL is $66,150, nearly four times as great.

Judging whether the credit risk underlying those PDs has materially increased is obviously important. But it is also difficult. There is a “rebuttable presumption” that an asset’s credit risk has increased materially when contractual payments are more than 30 days past due. In general, however, the bank cannot rely solely upon past-due information if forward-looking information is to be had, either on a loan-specific or a more general basis, without unwarranted trouble or expense.

The bank need not undertake an exhaustive search for information, but it should certainly take into account pertinent intelligence that is routinely gathered in the ordinary course of business.

For instance, Richmond Tool’s financial statements are readily available. Balance sheets are prepared as of a point in time; income and cash flow statements reflect periods that have already ended. Nonetheless, traditional ratio analysis serves to evaluate the company’s prospects as well as its current capital structure and historical operating results. With sufficient data, models can be built to forecast these ratios over the remaining life of the loan. Richmond Tool’s projected financial position and earning power can then be used to predict stage transitions.

Pertinent external information can also be gathered without undue cost or effort. For example, actual and expected changes in the general level of interest rates, mid-Atlantic unemployment, and defense spending are likely to affect Richmond Tool’s business prospects, and, therefore, the credit risk of the outstanding loan. The same holds true for regulatory and technological developments that affect the company’s operating environment or competitive position.

Finally, the combination of qualitative information and non-statistical quantitative information such as actual financial ratios may be enough to reach a conclusion. Often, however, it is appropriate to apply statistical models and internal credit rating processes, or to base the evaluation on both kinds of information. In addition to designing, populating, and testing mathematical models, FRG can help you integrate the statistical and non-statistical approaches into your IFRS 9 platform.

For more information about FRG’s modeling expertise, please click here.

IFRS 9: Modeling Challenges

Calculating expected credit losses under IFRS 9 is easy. It requires little more than high school algebra to determine the aggregate present value of future cash flows. But it is not easy to ascertain the key components that are used by the basic equation—regardless whether the approach taken is “advanced”  (i.e., where PD, LGD, and EAD are modeled) or ”simplified” (also called “intermediate”). The forward-looking stance mandated by IFRS 9 makes the inherently difficult process of specifying these variables all the more complex.

For the sake of brevity, let’s consider only the advanced approach for this discussion. There are two immediate impacts on PD model estimation: the point-in-time requirements and the length of the forecast horizon.

PD estimates need to reflect point-in-time (PIT) rather than through-the-cycle (TTC) values. What this means is that PDs are expected to represent the current period’s economic conditions instead of some average through an economic cycle. Bank risk managers will have to decide whether they can adapt a CCAR (or other regulatory) model to this purpose, determine a way to convert a TTC PD to a PIT PD, or build an entirely new model.

The length of the forecast horizon has two repercussions. First, one must consider how many models to build for estimating PDs throughout the forecast. For example, it may be determined that a portfolio warrants one model for year 1, a second model for years 2 to 3, and a third model for years 3+. Second, one should consider how far into the forecast horizon to use models. Given the impacts of model risk, along with onus of maintaining multiple models, perhaps PDs for a horizon greater than seven years would be better estimated by drawing a value from some percentile of an empirical distribution.

Comparatively speaking, bank risk managers may find it somewhat less difficult to estimate LGDs, especially if collateral values are routinely updated and historical recovery rates for comparable assets are readily available in the internal accounting systems. That said, IFRS 9 requires an accounting LGD, so models will need to be developed to accommodate this, or a process will have to be defined to convert an economic LGD into an accounting one.

Projecting EADs is similarly challenging. Loan amortization schedules generally provide a valid starting point, but unfortunately they are only useful for installment loans. How does one treat a revolving exposure? Can one leverage, and tweak, the same rules used for CCAR? In addition, embedded options have to be taken into account. There’s no avoiding it: estimating EADs calls for advanced financial modeling.

As mentioned above, there are differences between the requirements of IFRS 9 and those of other regulatory requirements (e.g., CCAR). As a result, the models that banks use for stress testing or other regulatory functions cannot be used as-is for IFRS 9 reporting. Bank risk managers will have to decide, then, whether their CCAR models can be adapted with relatively minor modifications. In many cases they may conclude that it makes more sense to develop new models. Then all the protocols and practices of sound model design and implementation come into play.

Of course, it is also important to explain the conceptual basis and present the supporting evidence for PD, LGD, and EAD estimates to senior management—and to have the documentation on hand in case independent auditors or regulatory authorities ask to see it.

In short, given PD, LGD, and EAD, it’s a trivial matter to calculate expected credit losses. But preparing to comply with the IFRS 9 standard is serious business. It’s time to marshal your resources.

IFRS 9: Classifying and Staging Financial Assets

Under IFRS 9, Financial Instruments, banks will have to estimate the present value of expected credit losses in a way that reflects not only past events but also current and prospective economic conditions. Clearly, complying with the 160-page standard will require advanced financial modeling skills. We’ll have much more to say about the modeling challenges in upcoming posts. For now, let’s consider the issues involved in classifying financial assets and liabilities.

The standard introduces a principles-based classification scheme that will require banks to look at financial instruments in a new way. Derivative assets are classified as “fair value through profit and loss” (FVTPL), but other financial assets have to be sorted according to their individual contractual cash flow characteristics and the business model under which they are held. Figure 1 summarizes the classification process for debt instruments. There are similar decisions to be made for equities.

The initial classification of financial liabilities is, if anything, more important because they cannot be reclassified. Figure 2 summarizes the simplest case.

That’s only the first step. Once all the bank’s financial assets have been classified they have to be sorted into stages reflecting their exposure to credit loss:

  • Stage 1 assets are performing
  • Stage 2 assets are underperforming (that is, there has been a significant increase in their credit risk since the time they were originally recognized)
  • Stage 3 assets are non-performing and therefore impaired

These crucial determinations have direct consequences for the period over which expected credit losses are estimated and the way in which effective interest is calculated. Mistakes in staging can have a very substantial impact on the bank’s credit loss provisions.

In addition to the professional judgment that any principles-based regulation or accounting standard demands, preparing data for the measurement of expected credit losses requires creating and maintaining both business rules and data transformation rules that may be unique for each portfolio or product. A moderately complex organization might have to manage hundreds of rules and data pertaining to thousands of financial instruments. Banks will need systems that make it easy to update the rules (and debug the updates); track data lineage; and extract both the rules and the data for regulators and auditors.

IFRS 9 is effective for annual periods beginning on or after January 2018. That’s only about 18 months from now. It’s time to get ready.

IFRS 9 Figure 1

IFRS 9 Figure 2

 

 

 

Fed Exempts Large Noncomplex Banks from CCAR Qualitative Assessment

The U.S. central bank finalized its rule exempting large and noncomplex banks from the qualitative component of Comprehensive Capital Analysis and Review (CCAR) program. Bank holding companies and U.S. intermediate holding companies of foreign banking organizations that have total consolidated assets between $50 billion and $250 billion and total nonbank assets of less than $75 billion, and that are not identified as global systemically important banks, must conduct stress tests but are not required to undergo the qualitative assessment to which the largest banks are additionally subject. The Fed’s press release also states that the scenarios and instructions for the 2017 CCAR cycle will be released by the end of this week.

https://www.federalreserve.gov/newsevents/press/bcreg/20170130a.htm

 

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