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 [link], 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 …?

 

RELATED:

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.

 

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