Balancing the COVID-19 Economic Model amid the multitude of issues saturating our voyage thru uncharted waters

Are we acting like or ‘experiencing’ an economic environment more similarly performing like Unemployment jumped to 7, 8 or even 10%? Is Unemployment handicapped? Should it be?

Considering the federal stimulus funds already dispersed, in addition to the substantial dry powder, are we actually surprised at the way some markets are performing. A disproportionate amount of people are feeling disconnected in what we see, and what we do every day.

It was an opportunity for P360 to model a recession ‘Live’ as we waded thru February and March. Data was not yet available but with Mosaic’s live modeling, the new curves were immediately pushed out and we were able to get a jump on performance risk.

The modeling allows for instant updates which has provided a more distinct picture when adjusting for such inputs as GDP, Unemployment and Interest Rates.

Certain economic indices may recover in a more ‘V’ shape fashion while the economy on the whole will prove a different shape that is not even defined in the Latin alphabet. Expect Unemployment to decrease in dramatic form with some quick erratic swipes, however, prepare for a more lingering affect over the next many quarters. Maintaining a rate that is 2 or 3 times our recent norm is a highly likely scenario.

Now that you have it, what are you going to do with it?
Be Smart. Spend Smart. You don’t have to spend it all!

An obvious goal would be to owe the least amount as possible. But how do you do that? Is it even possible given the current rules and timing? Do all your employees want to come back to work? For sure we expect changes however we need to plan and track for a ‘Smart Spend’.

Don’t be afraid of having money leftover that you may owe as a loan. Spending PPP funds that you thought would be forgiven, but don’t qualify, could put you in a much worse position.

Try to keep your spending simple. You can utilize a separate checking account and spend just on the basics to help track forgiveness. Anticipate a similar but perhaps more stringent underwrite as you exit the PPP. Remember the lenders will be on the hook for any amount that doesn’t qualify so put yourself in their shoes if you come upon a questionable expense.

We’re experiencing unprecedented numbers in Unemployment yet some companies and the financial markets have experienced a “Quick V.”

P360 helps makes sense of the information available to help portfolio lenders better anticipate performance.

Bridging the gap includes concepts such as will unemployment numbers really “act’ like the unemployment numbers? With government and Fed interactions aimed at stabilizing the economy and helping companies and individuals, what is the net effect? How should this information be interpreted and utilized by your institution?

Live Modeling has enabled P360 clients to ‘get ahead of the curves’ with Mosaic’s COVID-19 Economic Scenario.

We all anticipated that March numbers were not going to portray the most likely, or actual performance risk in the world in which we live. Data was largely not available. Charts and graphs and data sources were scarce.

P360 developed an economic scenario in order to help lenders better anticipate loan loss, thus solving for CECL and more importantly, creating a short to medium term plan by implementing the COVID-19 model.

A funny but controversial show, South Park, drills down and satirizes every day topics. Concepts are simplified and sometimes distorted as seen thru the eyes of the young characters. One such episode features the Underpants Gnomes who are to blame for mysteriously missing underpants – sound familiar parents?

The secret lair of the gnomes is viewed as many gnomes work feverishly adding underpants to their already heaping pile. As self-proclaimed business experts, the gnomes business plan is proudly displayed showing that they collect underpants in phase 1, and make profit in phase 3, with confusion and no explanation as to ‘how’ in phase 2.

Phase 2 is the often that elusive business process that converts what you’re doing in Phase 1, to a successful outcome noted in Phase 3. It is the ‘secret sauce’ of success.

Implementing CECL can feel just that way.

We are told what to do and why to do it, but the process can feel mysterious.

Mosaic solves for the mysterious Phase 2 in CECL reporting.

Basically, Mosaic collects underpants. Underpants of all styles, colors and sizes are collected and warehoused. Then Mosaic’s loan level expected loss modeling is implemented as Phase 2 in order to deliver Phase 3 - CECL Success.

The biggest difference between Mosaic and South Park, aside from not collecting underpants, is that Mosaic’s process is not elusive at all.

Mosaic’s Phase 2 has been vetted by national accounting firms and is easily implemented for financial institutions of all shapes and sizes to help convert disparate data to useable information.

Keeping up with CECL can be fun and challenging, or it can be like pulling off a scab slowly not knowing what you’ll find underneath.

We prefer fun and challenging.

P360 was driven to simplify expected loss almost 8 years ago with a loan level, forward-looking model. This type of live modeling has proven to help credit unions and other lenders migrate from their historic ALLL methodologies to a CECL solution. Regression analysis with discounted cash flows at the loan level address loans in all phases of their life cycle. Individual charge off, recovery, impairment, delinquency and credit score data helps fine-tune expected loss by institution, and supports all CECL methods.

Here’s how we look at modeling for expected loss: Mosaic is a live, loan level model that provides a number of risk outputs for each loan, including Probability of Default (PD), Loss Severity (LS), Life of Loan, Unexpected Loss and Loan Grading. The model incorporates a number of inputs including external data sources which address Q&E factors. The power and simplicity with this type of modeling provides flexibility to Portfolio Managers to explore unlimited methodologies.

We have had many discussions with CFO’s and other lending executives who plan on utilizing multiple methods, or at least explore multiple methods in order to fine tune their ALLL. Common methodologies as discussed in the industry include: Discounted Cash Flow, Average Charge Off, Vintage and other Static Pool Analysis, Roll rate, Probability of Default and Regression Analysis.

7 Models - FASB/AICPA

One Model. 7 Methods. Mosaic seems to simplify the process by providing a model that delivers Expected Loss, plus additional loan level risk data giving portfolio managers the option to explore reporting methods. Remember to aggregate and warehouse your loan information. All methods rely on secure and accurate data.

Join us at our CECL Educational session held June 22nd at Financial Partners Credit Union in Downey, CA.

Key topics include:

• CECL Modeling
• Modeling Example
• Inputs, Outputs, and look-back testing
• Monitoring the Economy
• SCCUA State of Lending
• Working with CECL Results

Email us to reserve your spot. info@p360inc.com

Last week's User Group in Southern California exceeded our expectations. The credit union participants had great feedback and allowed us to dive deep into the Mosaic system and CECL planning. We had such great feedback during and after the meeting that we have scheduled a new CECL educational series to begin in June 2017. More information to follow. Always feel free to email us with any questions (info@p360inc.com). Stay tuned.

CECL topics will include:
CECL Modeling
Model Example
Inputs, Outputs, Lookback Testing
Monitoring the Economy
SCCUA State of Lending
Working with CECL Results

I once heard sound advice ‘Start at the beginning’

In order to prepare for CECL, the first thing we have to do is to understand the concept. Compliance with CECL may not start this year, but getting started with CECL does. Aggregating the data today and building the process will help you design a better way to gain insight into your loan portfolio which is all CECL is trying to accomplish.

Let’s boil down what is expected for CECL into 3 steps:
Data Collection - External Inputs - Forward Model
1. The simple process of collecting and warehousing data creates an environment for you to identify inconsistencies such as missing information, original loan data, refreshed credit scores and property values. Not only do you need this information for reporting and analytics, you gain the benefit of clean data.
2. Incorporating external inputs such as economic data in your modeling provides for greater visibility and accuracy determining probability of default and loss severity. Information from this piece of the CECL puzzle further supports pricing and guideline adjustments.
3. CECL language does not provide the exact model specs, so much as it suggests using a forward-looking methodology with cash flows and the ability to track performance and losses. Risk Models exist and have been used for decades but the time is now to prepare your institution for a flexible solution and smooth transition.

Accolade Advisory and Corporate One are strong partners and great resources for Credit Unions. For more information on the CECL model, check out their webinar CECL Series

Institutions are expected to create and maintain an Expected Loss model with forward-looking cash flow analysis. This is not an easy task and most Credit Unions could use assistance with this part but initiating the process can be as easy as aggregating and auditing your data. Reporting will only be as good as the data warehouse so confirm you have the ability to track changes and trends. A small amount of time dedicated to your loan data now will save you mountains of time.

You will be so much more prepared and have more options exploring Expected Loss models with your data element under control.

Today’s CNBC commentary included a visit from Joe Hinrichs, EVP at Ford Motor Company noted a glut in the used car market – but only specifically relating to certain cars that were in vogue a few years ago. Namely, smaller, economical cars which made up a large part of sales growth has shifted to the larger SUV type models.

Price of used cars dropping significantly partially due to people leasing more cars 3 to 4 years ago, and the types of cars coming back to the market place versus what consumers want now.

Demand now is for SUVs and larger vehicles so there is a mismatch on what was sold 3 to 4 years ago and coming back to the market place, to what customers want now. This trend is expected to continue thru 2017 increasing the supply glut of the wrong kind of car before stabilizing.

How can we use this information in real time? Does the same loan-to-value ratio between two different cars carry the same risk? These are great questions to ask before running new auto loan campaigns and promotions. Being able to consume information from external sources can provide valuable insight when taking on additional risk. This proves to be another benefit when aggregating loan level data and using a Live Modeling when implementing CECL.

Keeping in touch with your members and concentrating on direct lending is becoming even more important as car makers test the ‘shared ownership’ business model.

Today’s Wall Street Journal includes an article comparing Cadillac’s subscription plan with that of NetFlix. The shared ownership concept is not new as other start-ups named in the article have been around for years. wsj-Cadillac's a Netflix for Cars

And to further pressure the future of the car sales model, continued advancement in driverless cars and the increasing use of ride share services such as Lyft and Uber may convert some car buyers and slow the demand – thus decreasing the potential to originate new loans. One estimate suggests a possible 40% decrease in new auto sales.

Now is the time to create a process to identify and price your most probable borrowers. Part of this process is already complete for many P360 clients as they aggregate data to prepare for CECL. Other clients are a step ahead as they utilize Mosaic’s expected loss model to help compare their pricing with future performance and profitability.

This is yet another benefit of preparing for CECL. CECL Modeling Centralizing loan and borrower information becomes a powerful tool when combined within a relational database with economic data and a forward-looking model.

Gain a stronger foothold to your future. Consider and quantify as many external factors affecting new loan origination as possible and cut to the chase with direct lending because driverless cars, ride services and shared ownership may cut into your supply of good borrowers.

Reserve methodologies of Credit Unions and other lenders vary based on sophistication, geo-economic environment, loan products and management of related risk characteristics. No matter the factors varying these institutions, key reserve procedures should include - the ability to evaluate loan risk regularly, a process for individual loans to be called out, access to current data and valuations, and the ability to identify areas of concentration by multiple attributes.

Risk managers agree that merely identifying risk is not enough. Flagging risk as an isolated exercise when credit scores or payment strings are updated is reactionary and can be too little, too late. It does not give the institution an opportunity to prepare and make adjustments, nor does it empower the institution to help the borrower. Isolated risk attributes do not indicate the existence of probable risk as compared to an expected loss approach which accounts for sensitivity to change and potential movement of the risk attributes.

The goal for most lenders is to employ a more proactive approach to calculate loan risk. Getting to the next level with a forward-looking methodology enables portfolio managers to quantify risk with probability of default (PD) and loss given default (LGD) at the loan level. This opportunity to stay ahead becomes more attractive as the expected loss information is used as a by-product to help with pricing, new campaigns and member loyalty.

The understanding and testing for changes, movement and sensitivity in risk characteristics proves insightful to estimating future loan performance. Quantifying risk at the loan level is actionable, more meaningful and can be applied to Loan Loss Reserves in anticipation of CECL as well as other areas of lending.

We have experienced interesting market conditions recently including the first Rate Hike in quite a while. Last month’s move by the Federal Reserve was only the second time in a decade that rates have increased, and more are sure to follow More Rate Hikes for 2017.

As economic metrics such as Interest Rates change, it is important to monitor and use the most recent information when running Expected Loss and other risk measures on your loan portfolio. Just like having access to loan level information such as Current Balance, Payment Date, refreshed Credit Scores and updated Property Values, we need to think of Economic Scenarios and other External Inputs as basic data points to include in loan risk modeling.

Keeping up with all the information available can be a daunting task but there are tools and resources available with real-time access. The Federal Reserve makes available historic rates and forecasts for many instruments on their website, including the Effective Federal Funds Rate (EFFR) Federal Reserve. Credit Unions and other portfolio lenders can leverage these information sources, and may not need to recreate the wheel and invest in large projects or additional staff in order to embrace the mountain of information available to help identify loan risk.