How Embracing SR 11-7 Guidelines Can Support the CECL Process

The Board of Governors of the Federal Reserve System’s SR 11-7 supervisory guidance (2011) provides an effective model risk management framework for financial institutions (FI’s). SR 11-7 covers everything from the definition of a model to the robust policies/procedures that should exist within a model risk management framework. To reduce model risk, any FI should consider following the guidance throughout internal and regulatory processes as its guidelines are comprehensive and reflect a banking industry standard.

The following items and quotations represent an overview of the SR 11-7 guidelines (Board of Governors of the Federal Reserve System, 2011):

  1. The definition of a model – “the term model refers to a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.”
  2. A focus on the purpose/use of a model – “even a fundamentally sound model producing accurate outputs consistent with the design objective of the model may exhibit high model risk if it is misapplied or misused.”
  3. The three elements of model risk management:
    • Robust model development, implementation, and use – “the design, theory, logic underlying the model should be well documented and generally supported by published research and sound industry practice.”
    • Sound model validation process – “an effective validation framework should include three core elements: evaluation of conceptual soundness, …, ongoing monitoring, …, and benchmarking, outcomes analysis, …”
    • Governance – “a strong governance framework provides explicit support and structure to risk management functions through policies defining relevant risk management activities, procedures that implement those policies, allocation of resources, and mechanisms for evaluating whether policies and procedures are being carried out as specified.”

The majority of what the SR 11-7 guidelines discuss applies to some of the new aspects from the accounting standard CECL (FASB, 2016). Any FI under CECL regulation must provide explanations, justifications, and rationales for the entirety of the CECL process including (but not limited to) model development, validation, and governance. The SR 11-7 guidelines will help FI’s develop effective CECL processes in order to limit model risk.

Some considerations from the SR 11-7 guidelines in regards to the components of CECL include (but are not limited to):

  • Determining appropriateness of data and models for CECL purposes. Existing processes may need to be modified due to some differing CECL requirements (e.g., life of loan loss estimation).
  • Completing comprehensive documentation and testing of model development processes. Existing documentation may need to be updated to comply with CECL (e.g., new models or implementation processes).
  • Accounting for model uncertainty and inaccuracy through the understanding of potential limitations/assumptions. Existing model documentation may need to be re-evaluated to determine if new limitations/assumptions exist under CECL.
  • Ensuring validation independence from model development. Existing validation groups may need to be further separated from model development (e.g., external validators).
  • Developing a strong governance framework specifically for CECL purposes. Existing policies/procedures may need to be modified to ensure CECL processes are being covered.

The SR 11-7 guidelines can provide FI’s with the information they need to start their CECL process. Although not mandated, following these guidelines overall is important in reducing model risk and in establishing standards that all teams within and across FI’s can follow and can regard as a true industry standard.

Resources:

  1. Board of Governors of the Federal Reserve System. “SR 11-7 Guidance on Model Risk Management”. April 4, 2011.
  2. Daniel Brown and Dr. Craig Peters. “New Impairment Model: Governance Considerations”. Moody’s Analytics Risk Perspectives. The Convergence of Risk, Finance, and Accounting: CECL. Volume VIII. November 2016.
  3. Financial Accounting Standards Board (FASB). Financial Instruments – Credit Losses (Topic 326). No. 2016-13. June 2016.

Samantha Zerger, business analytics consultant with FRG, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

 

Improve Your Problem-Solving Skills

This is the fifth post in an occasional series about the importance of technical communication in the workplace.

 “Work organisations are not only using and applying knowledge produced in the university but they are also producing, transforming, and managing knowledge by themselves to create innovations (Tynjälä, Slotte, Nieminen, Lonka, & Olkinuora, 2006)”.

Problem-solving skills are rooted in the fact that you must learn how to think, not what to think. Most classes in high schools and colleges teach you what to think (e.g., history dates, mathematical equations, grammar rules), but you must learn further problem-solving skills in order to help you learn how to think.

In the technical workplace, you are expected to be able to be given a problem and come up with a solution; a solution that possibly has never been thought of before. Employers are looking for people that have the right skills in order to do that very thing. Because of this, most interview processes will inevitably include at least one problem-solving question.

  • “How have you handled a problem in your past? What was the result?”
  • “How would you settle the concerns of a client?”
  • “How would you handle a tight deadline on a project?”

The way you answer the problem-solving question usually gives the interviewer a good sense of your problem-solving skills. Unfortunately, for the interviewee, problem solving is grouped into a BROAD skill set made up of:

  • Active listening: in order to identify that there is a problem
  • Research: in order to identify the cause of the problem
  • Analysis: in order to fully understand the problem
  • Creativity: in order to come up with a solution, either based on your current knowledge (intuitively) or using creative thinking skills (systematically)
  • Decision making: in order to make a decision on how to solve the problem
  • Communication: in order to communicate the issue or your solution to others
  • Teamwork: in order to work with others to solve the problem
  • Dependability: in order to solve the problem in a timely manner

So how do you, as the interviewee, convey that you have good problem-solving skills? First, acknowledge the skill set needed to solve the problem relating to each step in the problem-solving process:

Step in Problem SolvingSkill Set Needed
1. Identifying the problemActive listening, research
2. Understanding and structuring the problemAnalysis
3. Searching for possible solutions or coming up with your own solutionCreativity, communication
4. Making a decisionDecision making
5. Implementing a solutionTeamwork, dependability, communication
6. Monitoring the problem and seeking feedbackActive listening, dependability, communication

Then, note how you are either planning to or are improving your problem-solving skills. This may include gaining more technical knowledge in your field, putting yourself in new situations where you may need to problem solve, observing others who are known for their good problem-solving skills, or simply practicing problems on your own. Problem solving involves a diverse skill set and is key to surviving in a technical workplace.

Resources:

  1. Problem-Solving Skills: Definitions and Examples. Indeed Career Guide.
  2. Tynjälä, Päivi & Slotte, Virpi & Nieminen, Juha & Lonka, Kirsti & Olkinuora, Erkki. (2006). From university to working life: Graduates’ workplace skills in practice.

 

Samantha Zerger, business analytics consultant with the Financial Risk Group, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

 

 

Documenting CECL

The CECL Standard requires more than just another update in the calculation of a financial institution’s (FI’s) allowance for credit losses; the new standard also pushes institutions to be more involved in the entire allowance process, especially on the management/executive level. From explanations, justifications and rationales to policies and procedures, the standard requires them all. The FI needs to discuss them, understand them, and document them.

The first point is to discuss all decisions that must be made regarding the CECL process. This includes everything from the definition of default to the justification of which methodology to use for which segment of the data. Although these discussions may be onerous, the CECL standard requires full understanding and completeness of all decisions. Once there is understanding, all decisions must be documented for regulation purposes:

CECL Topic 326-20-50-10: An entity shall provide information that enables a financial statement user to do the following:

  1. Understand management’s method for developing its allowance for credit losses.
  2. Understand the information that management used in developing its current estimate of expected credit losses.
  3. Understand the circumstances that caused changes to the allowance for credit losses, thereby affecting the related credit loss expense (or reversal) reported for the period.

CECL Topic 326-20-50-11: To meet the objectives in paragraph 326-20-50-10, an entity shall disclose all of the following by portfolio segment and major security type:

  1. A description of how expected loss estimates are developed
  2. A description of the entity’s accounting policies and methodology to estimate the allowance for credit losses, as well as discussion of the factors that influenced management’s current estimate of expected credit losses, including:
    • Past events
    • Current conditions
    • Reasonable and supportable forecasts about the future
  3. A discussion of risk characteristics relevant to each portfolio segment
  4. Etc.

Although these may seem like surprising jumps in requirements for CECL, these are simply more defined requirements than under existing ALLL guidance. Note that some of the general requirements under the existing guidance will remain relevant under CECL, such as:

  • “the need for institutions to appropriately support and document their allowance estimates”
  • the “…responsibility for developing, maintaining, and documenting a comprehensive, systematic, and consistently applied process for determining the amounts of the ACL and the provision for credit losses.”
  • the requirement “…that allowances be well documented, with clear explanations of the supporting analyses and rationale.”

As you can see, documentation is an important component of the CECL standard. While the documentation will, at least initially, require more effort to produce, it will also give the FI opportunity to fully understand the inner workings of their CECL process.

Lastly, advice to avoid some headache—take the time to document throughout the entire process of CECL. As my math professor always said, “the due date is not the do date.”

Resources:

  1. FASB Accounting Standards Update, No. 2016-13, Financial Instruments – Credit Losses (Topic 326).
  2. Frequently Asked Questions on the New Accounting Standard on Financial Instruments – Credit Losses. FIL-20-2019. April 3, 2019.

Samantha Zerger, business analytics consultant with FRG, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

The Importance of Good Teamwork in the Technical Workplace

This is the fourth post in an occasional series about the importance of technical communication in the workplace.

Daily teamwork is an essential part of technical workplace success. Strong technical communication and collaboration skills are necessary to be an active and successful member of a team working to achieve a common goal.

When thinking about the term teamwork, the collaborative effort of a team, many associate Tuckman’s stages (1965) of team development: forming, storming, norming, and performing. In 1977, Tuckman added a fifth stage: adjourning.

  1. Forming – This stage involves the meeting of all the team members, discussions of each member’s skills, and dividing up of responsibilities.
  2. Storming – This stage involves the sharing of ideas among the members. Team leaders must resolve any challenges/competition between members and ensure that the project is moving forward.
  3. Norming – This stage involves the complete understanding of a common goal and responsibilities among members. Major conflicts are resolved.
  4. Performing – This stage involves the team members working efficiently together with minimal issues. The project work should continue on schedule.
  5. Adjourning – This stage involves the team members reaching their project end and going their separate ways onto new projects.

Although Tuckman’s stages represent a standard team development flow, there is much more to think about as a member of that team. How should I converse with others during a conflict in the Storming stage? How should I discuss my skills with other members in the Forming stage? How do I ensure that I do not fall behind the project schedule in the Performing stage?

Here are some tips that may help you in the different stages of team development:

  • Be flexible in your work. In the Forming stage, you may be asked to complete a task that you may not particularly enjoy. Thus, in order to be a good team member, you must be flexible enough to say that you will complete the task to reach the team’s common goal.
  • Complete your tasks in a timely manner. In the Performing stage, keep track of your own responsibilities and when the tasks are due. Communicate freely with the leader of the team throughout the stage to keep up-to-date on the team’s activities. If possible, finish your tasks early and offer help to other team members.
  • Avoid conflicts. In the Storming stage, some conflict is certain but there are ways to avoid larger conflicts with other team members.
    • Be aware of other member’s attitudes towards certain topics. Speak about those topics in smaller settings.
    • Offer compromises when tensions start to rise. The compromises might seem more appealing than the associated conflicts.
    • Attempt to resolve conflicts as soon as possible. The quicker they are resolved, the quicker the project can move forward.
    • Communicate face-to-face. Sometimes words get lost in translation.
  • Communicate often. Throughout the stages, make it a point to communicate with the team leader and other members of the team on a continual basis. This may include sending your status updates to the team leader, asking questions about your tasks, or simply checking in with other members.

All in all, it is important to be a team player. Every team member should be on the same side: the one that completes the project efficiently, successfully, and with minimal headaches.

Resources

  • Tuckman, B. W. (1965). Development sequence in small groups. Psychological Bulletin, 63, 384-399.

Samantha Zerger, business analytics consultant with the Financial Risk Group, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

Improving Verbal and Nonverbal Communication

This is the third post in an occasional series about the importance of technical communication in the workplace.

The ability to speak and portray yourself professionally, efficiently, and effectively are the most important business skills any person can have other than skills required by a job title. Those who have impressive verbal/non-verbal communication skills on day one of a new job or during an interview stand out as strong, confident employees or candidates. Those who give a strong presentation during a conference or to coworkers stand out as experts in their field. Those who speak up and intelligently portray their thoughts on a matter stand out as leaders.

Communicating with others during an interview, presentation, or meeting can be daunting for some, especially if they lack experience in a work environment. Those with more experience in the workplace tend to understand how to portray the necessary information in an effective way. Usually these people are managers, executives or, in general, leaders because they are able to communicate in order to get the results they need, whether it be completed tasks or motivated employees.

It is important to note that the term verbal communication relates to the simple use of sounds and words, whereas the term non-verbal communication consists of other forms of communication. The way a person talks is not the only concern; the way a person physically portrays themselves is also important.

Technical Communication Today categorizes a person’s act of presenting a topic to a group by body language, appearance, voice, rhythm and tone. The categories can also be translated to include any type of interaction in the workplace, whether it be one-on-one (e.g., interview), one-to-many (e.g., presentation), or many-to-many (e.g., social gathering). Those five categories help frame the tips we can present to those who lack experience or who want to improve their verbal and non-verbal communication skills.

Focus on your body language
  • Control your facial expressions and think about how they are being conveyed to others. Is your facial expression conveying that you are happy or displeased?
  • Stand/sit up straight and drop your shoulders. Usually those who are nervous tend to slouch and raise their shoulders which limits airflow.
  • Make eye contact with others when they are speaking to you. This indicates that you are paying attention and that you are interested in what they are saying.
Dress for success
  • Dress appropriately for the occasion. Ensure that what you are wearing matches the importance of the situation. When in doubt, dress a level better than you expect others to dress.
Control your voice
  • Enunciate your words and phrases. This ensures that others are hearing you correctly.
  • Project your voice and speak louder than normal when presenting to a large group of people.
Focus on your speaking rhythm
  • Do not be afraid of silence. Embrace the silent moments and make them work in your favor. Use them to emphasize your main points or use them to avoid the um’s and ah’s of nervousness (i.e., think before you speak).
  • Slow your speech. Usually those who are nervous tend to speak too quickly.
Choose a tone for your voice
  • Select a tone based off of the image you want to project; e.g., professional, passionate, pleased.

Also, practice makes perfect. Do not assume that simply reading the above tips is sufficient in improving your skills. Practice and actively concentrate on them on a daily basis to further improve your verbal/non-verbal communication skills. The more you practice, the more comfortable you will be in professional settings and in front of an audience.

 

Resources:

  • Richard Johnson-Sheehan. “Technical Communication Today: Special Edition for Society for Technical Communication Foundation Certification”. Fifth Edition.

 

Samantha Zerger, business analytics consultant with the Financial Risk Group, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

 

 

CECL – The Power of Vintage Analysis

I would argue that a critical step in getting ready for CECL is to review the vintage curves of the segments that have been identified. Not only do the resulting graphs provide useful information but the process itself also requires thought on how to prepare the data.

Consider the following graph of auto loan losses for different vintages of Not-A-Real-Bank bank[1]:

 

While this is a highly-stylized depiction of vintage curves, its intent is to illustrate what information can be gleaned from such a graph. Consider the following:

  1. A clear end to the seasoning period can be determined (period 8)
  2. Outlier vintages can be identified (2015Q4)
  3. Visual confirmation that segmentation captures risk profiles (there aren’t a substantial number of vintages acting odd)

But that’s not all! To get to this graph, some important questions need to be asked about the data. For example:

  1. Should prepayment behavior be captured when deriving the loss rates? If so, what’s the definition of prepayment?
  2. At what time period should the accumulation of losses be stopped (e.g., contractual term)?
  3. Is there enough loss[2] behavior to model on the loan level?
  4. How should accounts that renew be treated (e.g., put in new vintage)?

In conclusion, performing vintage analysis is more than just creating a picture with many different colors. It provides insight into the segments, makes one consider the data, and, if the data is appropriately constructed, positions one for subsequent analysis and/or modeling.

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1] Originally I called this bank ACME Bank but when I searched to see if one existed I got this, this, and this…so I changed the name. I then did a search of the new name and promptly fell into a search engine rabbit hole that, after a while, I climbed out with the realization that for any 1 or 2 word combination I come up with, someone else has already done the same and then added bank to the end.

[2] You can also build vintage curves on defaults or prepayment.

 

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CECL—Questions to Consider When Selecting Loss Methodologies

CECL—The Caterpillar to Butterfly Evolution of Data for Model Development

CECLData (As Usual) Drives Everything

CECL—Questions to Consider When Selecting Loss Methodologies

Paragraph 326-20-30-3 of the Financial Accounting Standards Board (FASB) standards update[1] states: “The allowance for credit losses may be determined using various methods”. I’m not sure if any statement, other than “We need to talk”, can be as fear inducing. Why is it scary? Because in the world of details and accuracy, this statement is remarkably vague and not prescriptive.

Below are some questions to consider when determining the appropriate loss methodology approaches for a given segment.

How much history do you have?

If a financial institution (FI) has limited history[2] then the options available to them are, well, limited. To build a model one needs sufficient data to capture the behavior (e.g., performance or payment) of accounts. Without enough data the probability of successfully building a model is low. Worse yet, even if one builds a model, the likelihood of it being useful and robust is minimal. As a result, loss methodology approaches that do not need a lot of data should be considered (e.g., discount cashflow or a qualitative factor approach based on industry information).

Have relevant business definitions been created?

The loss component approach (decomposing loss into PD, LGD, and EAD) is considered a leading practice at banks[3]. However, in order to use this approach definitions of default and, arguably, paid-in-full, need to be created for each segment being modeled. (Note: these definitions can be the same or different across segments.) Without these definitions, one does not know when an account has defaulted or paid-off.

Is there a sufficient number of losses or defaults in the data?

Many of the loss methodologies available for consideration (e.g., loss component or vintage loss rates) require enough losses to discern a pattern. As a result, banks that are blessed with infrequent losses can feel cursed when they try to implement one of those approaches. While low losses do not necessarily rule out these approaches, it does make for a more challenging process.

Are loan level attributes available, accurate, and updated appropriately?

This question tackles the granularity of an approach instead of an approach itself. As mentioned in the post CECL – Data (As Usual) Drives Everything, there are three different data granularity levels a model can be built on. Typically, the decision is between loan-level versus segment level. Loan-level models are great for capturing sensitivities to loan characteristics and macroeconomic events provided the loan characteristics are accurate and updated (if needed) on a regular interval.

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1]FASB accounting standards update can be found here

[2] There is no consistent rule, at least that I’m aware of, that defines “limited history”. That said, we typically look for clean data reaching back through an economic cycle.

[3] See: Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice August 2013

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CECL—The Caterpillar to Butterfly Evolution of Data for Model Development

CECLData (As Usual) Drives Everything

CECL—The Caterpillar to Butterfly Evolution of Data for Model Development

I don’t know about you, but I find caterpillars to be a bit creepy[1]. On the other hand, I find butterflies to be beautiful[2]. Oddly enough, this aligns to my views on the different stages of data in relation to model development.

As a financial institution (FI) prepares for CECL, it is strongly suggested (by me at least) to know which stage the data falls into. Knowing its stage provides one with guidance on how to proceed.

The Ugly

At FRG we use the term dirty data to describe data that is ugly. Dirty data typically has these following characteristics (the list is not comprehensive):

  • Unexplainable missing values: The key word is unexplainable. Missing values can mean something (e.g., a value has not been captured yet) but often they indicate a problem. See this article for more information.
  • Inconsistent values: For example, a character variable that holds values for state might have Missouri, MO, or MO. as values. A numeric variable for interest rate might have a value as a percent (7.5) and a decimal (0.075)
  • Poor definitional consistency: This occurs when a rule that is used to classify some attribute of an account changes during history. For example, at one point in history a line of credit might be indicated by a nonzero original commitment amount, but at a different point it might be indicated by whether a revolving flag is non-missing.
The Transition

You should not model or perform analysis using dirty data. Therefore, the next step in the process is to transition dirty data into clean data.

Transitioning to clean data, as the name implies, requires scrubbing the information. The main purpose of this step is to address the issues identified in the dirty data. That is, one would want to fix missing values (e.g., imputation), standardized variable values (e.g., all states are identified by a two-character code), and correct inconsistent definitions (e.g., a line indicator is always based on nonzero original commitment amount).

The Beautiful

A final step must be taken before data can be used for modeling. This step takes clean data and converts it to model-ready data.

At FRG we use the term model-ready to describe clean data with the application of relevant business definitions. An example of a relevant business definition would be how an FI defines default[3]. Once the definition has been created the corresponding logic needs to be applied to the clean data in order to create, say, a default indicator variable.

Just like a caterpillar metamorphosing to a butterfly, dirty data needs to morph to model-ready for an FI to enjoy its true beauty. And, only then, can an FI move forward on model development.

 

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1] Yikes!

[2] Pretty!

[3] E.g., is it 90+ days past due (DPD) or 90+ DPD or in bankruptcy or in non-accrual or …?

 

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CECL—Questions to Consider When Selecting Loss Methodologies

CECLData (As Usual) Drives Everything

The Importance of Technical Communication

This is the introduction to a new blog series, The Importance of Technical Communication, which will focus on topics such as verbal and written communication, workplace etiquette, and teamwork in the workplace.

Soft skills, as a general term, include interpersonal skills, leadership, dependability, willingness to learn, and effective communication skills that can be used in any career. These are known by sociologists and anthropologists as skills that are generally required to become a functioning member of society. But, it seems that there are many articles pointing out a lack of these soft skills among college graduates and stating it as a main reason why many cannot get hired. Some headlines include:

Results from a survey by the Workforce Solutions Group at St. Louis Community College regard these deficiencies specifically as applicant shortcomings. In the St. Louis regional survey, it states that poor work habits, lack of critical thinking and problem solving skills, lack of teamwork or collaboration, and lack of communication or interpersonal skills rank the highest in applicant shortcomings within both technology and finance domains.

 TechnologyFinance
Poor work habits56%50%
Lack of critical thinking and problem solving skills44%50%
Lack of teamwork or collaboration49%43%
Lack of communication or interpersonal skills58%38%
Table 1: Applicant Shortcomings – 2018 State of St. Louis Workforce Report to the Region

In today’s society, with tools at our fingertips, communication is key. In the workplace, interpersonal skills are needed at a rapid, daily pace. Often other workplace issues, such as lack of collaboration skills, arise from communication issues. Given these alarming statistics, how do we, in the technology and finance domain, encourage the improvement of these skills within our companies and deal with applicants who lack them? This blog series will discuss these questions and provide tips on how to correctly technically communicate in the workplace.

Samantha Zerger, business analytics consultant with the Financial Risk Group, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

 

CECL – Data (As Usual) Drives Everything

To appropriately prepare for CECL a financial institution (FI) must have a hard heart-to-heart with itself about its data. Almost always, simply collecting data in a worksheet, reviewing it for gaps, and then giving it the thumbs up is insufficient.

Data drives all parts of the CECL process. The sections below, by no means exhaustive, provide key areas where your data, simply being by your data, constrains your options.

Segmentation

Paragraph 326-20-30-2 of the Financial Accounting Standards Board (FASB) standards update[1] states: “An entity shall measure expected credit losses of financial assets on a collective (pool) basis when similar risk characteristic(s) exist.” It then points to paragraph 326-20-55-5 which provides examples of risk characteristics, some of which are: risk rating, financial asset type, and geographical location.

Suggestion: prior to reviewing your data consider what risk profiles are in your portfolio. After that, review your data to see if it can adequately capture those risk profiles. As part of that process consider reviewing:

  • Frequency of missing values in important variables
  • Consistency in values of variables
  • Definitional consistency[2]
Methodology Selection

The FASB standard update does not provide guidance as to which methodologies to use[3]. That decision is entirely up to the FI[4]. However, the methodologies that are available to the FI are limited by the data it has. For example, if an FI has limited history then any of the methodologies that are rooted in historical behavior (e.g., vintage analysis or loss component) are likely out of the question.

Suggestion: review the historical data and ask yourself these questions: 1) do I have sufficient data to capture the behavior for a given risk profile?; 2) is my historical data of good quality?; 3) are there gaps in my history?

Granularity of Model

Expected credit loss can be determined on three different levels of granularity: loan, segment (i.e., risk profile), and portfolio. Each granularity level has a set of pros and cons but which level an FI can use depends on the data.

Suggestion: review variables that are account specific (e.g., loan-to-value, credit score, number of accounts with institution) and ask yourself: are the sources of these variables reliable? Do they get refreshed often enough to capture changes in customer or macroeconomic environment behavior?

Hopefully, this post has started you critically thinking about your data. While data review might seem daunting, I cannot stress enough—it’s needed, it’s critical, it’s worth the effort.

 

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1] You can find the update here

[2] More on what these mean in a future blog post

[3] Paragraph 326-20-30-3

[4] A future blog post will cover some questions to ask to guide in this decision.

 

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