Perspectives: Finance

February 12, 2018
Auto Lending at a Crossroads
By Alec Hollis, Director – ALM Strategy Group, ALM First Financial Advisors, LLC

“Transitions to delinquency show persistent increases for auto and credit card debt; auto loan delinquency rates especially problematic for subprime auto finance loans.”

So reads the press release from The Federal Reserve Bank of New York for a recent Quarterly Report on Household Debt and Credit. Headlines like these are becoming more common in relation to auto lending as regulators cite concerns over several years of strong growth alongside eased underwriting standards and unabated flows into delinquency.

The unprecedented growth in auto debt can be derived in part by the underlying demand for the collateral. Annual auto sales have seen several consecutive years of growth, setting an all-time high of 17.5 million units in 2016. This growth has trickled down to auto lenders, as total auto loan debt notched a new all-time record at $1.21 trillion in outstanding balances at the end of the third quarter of 2017. This represents a 48% increase from ten years ago – second only to a 157% increase in student loan debt – while total household debt increased 7% over the same time period.

This growth has the OCC’s attention – the agency took notice as early as the spring of 2012, citing banks launching new products, services and processes to catalyze asset growth, and specifically mentioning the growth in indirect auto lending. While growth in and of itself is not necessarily bad, the OCC has consistently discussed auto lending, which is why it is important for financial institutions to understand the reasons behind the elevated risk status.

Originations
Total originated auto loans surpassed $430 billion through the first three quarters of 2017, with roughly $88 billion of those loans considered to be subprime (credit scores below 620). Subprime auto loan originations have not been growing as fast as in preceding years, as some major market participations have capped subprime production, but overall originations continue unabated, with an ongoing streak of year-over-year increases.

At the close of 2017, roughly 20% of auto loan originations were subprime, compared to 21% in 2016, 23% in 2015 and 29% pre-crisis in 2007. Despite this decrease, Figure 1 shows that subprime origination volume has nevertheless has accelerated to roughly pre-crisis levels today, while originations with excellent credit have far surpassed pre-crisis levels.

Figure 1



Delinquencies
Delinquencies in the auto lending space have likewise ticked up. Auto loans 90+ days delinquent measured 3.97% of the outstanding balance in the third quarter of 2017, continuing a streak of quarterly increases. Delinquency flow (newly delinquent loans) has also been increasing steadily for several years. Figure 2 shows the outstanding seriously delinquent balance, which has increased steadily since 2014.

Figure 2


Although widespread delinquencies have yet to materialize, there are certainly problematic sectors. Auto finance companies represent $602 billion – or roughly half of the $1.21 trillion outstanding – in auto loan debt. When it comes to subprime lending, auto finance companies dominate, representing 74% of outstanding balances with credit scores at origination of less than 620.

Auto financing companies might not look quite so dominant, though, when digging into delinquency flows. Figure 3 shows the flow into serious delinquency for auto loans originated with a credit score of less than 620. These flows have diverged from banks and credit unions in a major way, and are currently at levels not seen since the financial crisis for this major subset of subprime auto lenders.

Figure 3


The OCC has been consistently discussing and monitoring the trends in delinquencies since they first mentioned the drift higher in 2013. Asset quality indicators such as delinquency ratios and net charge-offs are trailing indicators, meaning that they take time to materialize as the credit lifecycle matures for a particular vintage of loans. Many are expecting delinquencies to continue to rise, as aggressively underwritten vintages continue to mature. To prepare for this, it is important for financial institutions to ensure collections operations can meet the potential delinquencies and that reserves are appropriate given this expectation.

Indirect Auto Lending
As it relates to auto lending, the OCC has widely discussed fair-lending risk, a result of yielding underwriting decisions to auto dealers or other third parties. Not only does this practice create a risk to credit standards, but it also carries significant compliance risk.

A notable case in 2013 involved Ally Financial, a large lender in the indirect auto space. The Consumer Financial Protection Bureau (CFPB) and the Department of Justice (DOJ) took action against discriminatory lending practices present in Ally’s program. Incentivized by dealer markups, minority borrowers were being charged higher interest rates at the discretion of the auto dealer. As a result, Ally was required to pay a total of $98 million in damages and penalties.

This example demonstrates how crucial it is for financial institutions to have adequate controls and appropriate compensation for dealer relationships. The last thing an institution needs is a dealer making underwriting decisions – not to mention the potential multimillion dollar penalties that may accompany them.

Action Plan
Recent news is riddled with coverage on auto loan delinquencies, subprime auto lending and large institutions scaling back from auto lending. Most recently, TCF Financial Corporation, a Minnesota-based bank holding company with $23 billion in total assets, announced discontinuation of all indirect auto lending. Other big banks have announced the limitation of auto loan originations in general, citing rising stress and protection from credit risk. As far back as 2015, Wells Fargo announced a cap on subprime production, after years of being aggressive lenders in the space. Moves like these could indicate some concern.

Particularly in regards to indirect lending, institutions need to understand the importance of assessing the additional risks posed by dealer relationships, as well as the additional fees. Return-on-capital models can objectively assess the profitability of product lines – if risks are mounting, institutions can take a cue from TCF and perhaps take a step back from the market.

Overall, auto lending can be a very important part of the balance sheet for many consumer-focused financial institutions, and indirect lending and dealer relationships can be an excellent tool to expand the institution’s reach. However, if ensuring safe and steady growth is the goal, history has shown that loosening credit standards to increase loan volume is not often successful in the long run.

Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

Alec Hollis is a Director in the ALM Strategy Group at ALM First Financial Advisors, LLC.


OCTOBER 16, 2017
Understanding discounted cash flow modeling as an option for CECL  
By Brandon Quinones, Risk Management Consultant, Sageworks

One of the main impetuses for changing the prevailing model for estimation of the allowance for loan and lease losses (ALLL) was the FASB’s view that reliance on historic information to determine “incurred-but-not-realized” losses in reserve calculations did not allow institutions to adjust reserve levels given a reasonable and supportable expectation of future events. Thus, a new standard requiring institutions to “… estimate expected credit losses over the contractual term of the financial asset(s)…” and “…consider available information relevant to assessing the collectability of cash flows. This information may include internal information, external information, or a combination of both relating to past events, current conditions, and reasonable and supportable forecasts.”1

Contractual v. Expected Cash Flows
When estimating losses using a discounted cash flow (DCF) approach, expected cash flow models are appropriate for reserve calculation under the new standard. A few material differences between the two calculations are modeling factors such as prepayment, default estimates, loss estimates and recovery activities that otherwise would not be used in a contractual cash flow calculation.

Track movement of loans by segment
to identify trends in portfolio growth
Approach
To calculate and apply these tendencies, the following inputs are critical to the calculation of discounted cash flow:



Of all the portfolio assumptions noted in the table, perhaps the most important to calculate are the Probability of Default (PD) and Loss Given Default (LGD).

PD and LGD are parameters that can be leveraged by institutions in a standalone measurement. Institutions currently using a PD and LGD approach for current GAAP may make an effort to calculate a lifetime PD and a symmetrical LGD to determine a rate for loss in an attempt to accomplish life-of-loan requirements as part of the new standard.

BENEFITS
Long-Term Assets
Calculating and understanding the average life and/or prepayment rate of a loan/loan type (e.g., CRE, Mortgages, C&I) is mandatory when calculating the expected credit losses.

An institution calculating its life-of-loan loss experience utilizing methodologies such as Vintage Analysis, Migration, PD and LGD, and/or Static Pool analysis will require look-back periods sufficient to cover the expected life of the pool. For example, if a loan pool has an average life of four years, an institution would need four years of data to conduct a single four-year observation of losses, and such a data set would only be inclusive of loans that were on the balance sheet four years prior.

A DCF approach can employ recent, shorter-term observations for deployment in a forward-looking amortization schedule. DCF is, and will be, a preferred methodology for calculating the reserve of longer-lived assets.

Readily Available Industry/Peer Data
In instances where loan pools lack loan-counts to be statistically relevant, haven’t experienced a material amount of defaults/losses during periods where data is available and/or have new portfolios that are more analogous to industry/peer experience, a DCF best accommodates alternative measurements while maintaining institution-specific risk.

In using DCF, financial institutions may deploy industry-level PD, LGD and CPR (Conditional Prepayment Rate) toward their own loan structures for a reasonable and possibly more relevant expectation of life-of-loan loss.

Forecasting
The CECL standard frequently references concepts related to making adjustments based on reasonable and supportable forecasts2, concepts that are most logically addressed by using a DCF methodology. In projecting expected cash flows, each period within a forward-looking amortization schedule can/will vary slightly based on future expectations of external/economic data.

CHALLENGES
By its very nature, executing an expected cash flow schedule for each loan every month/quarter may not be practical in a spreadsheet environment. On the other hand, institutions utilizing a third-party provider may run into challenges recording the loan data required to build an accurate amortization schedule.

The process starts and ends with developing policies and procedures around the ongoing maintenance of loan-level data. Every institution should begin to define rules for storage and/or maintenance of data. By taking steps now, financial institutions will find themselves in a position to calculate a reasonable and supportable reserve.

1 ASU 326-20-30-6
2 ASU 326-20-30-7


Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

Brandon Quinones, Risk Management Consultant, Sageworks
Brandon Quinones is a Risk Management Consultant in the Bank Division at Sageworks, where he primarily focuses on helping community banks and credit unions manage their allowance for loan and lease loss (ALLL) provisions.




September 18, 2017
Managing Mortgage Pipeline Risk
By Robert Perry, Principal – ALM and Investment Strategy, ALM First Financial Advisors, LLC

Residential mortgage banking is a sizable and important market segment, and many institutions operate originate-and-sell models, in which mortgage production is sold to investors (e.g., Fannie Mae or Freddie Mac). Loans locked with borrowers but yet to be originated-and-sold represent the entity’s “mortgage pipeline.”

Managing this pipeline is critical in today’s market and calls for skilled management to keep risk under control while ensuring profitability. The hedging process can often seem confusing – even daunting – to some because it involves complex computations and the use of sophisticated models to manage risk and determine pricing. When done correctly, however, hedging strategies protect lenders from the unpredictability of interest rate movements and other financial risks, thus improving risk-adjusted returns and long-term business viability.

Managing the pipeline for secondary sale
When a mortgage lender locks with a borrower and the loan enters the mortgage pipeline, an open interest rate exposure is created. If interest rates change significantly, the price of the loan will change significantly as well. Additionally, the borrower is free to choose another lender without penalty. When a particular lock fails to originate, it is known as a “fallout” or “hard fallout”. This is where good pipeline management becomes essential; understanding your fallout is critical to understanding your market exposure.

Common strategies for managing pipeline market risk include using forward-sale commitments and hedging using capital market instruments.

Forward-sale commitment
Forward-sale commitments are direct commitments to sell to the investor at some point in the future; commonly, this includes GSE investors, such as Fannie Mae. Forward-sale commitments can be made on a “mandatory” or “best-efforts” basis for future delivery of the loan. A “mandatory” commitment requires the originator to deliver a set dollar amount of mortgage loans at a certain price by a specific date; if the originator does not deliver, the agent charges a “pair-off” fee.

A “best efforts” commitment hedges fallout risk by not charging a pair-off fee assessment if the loan fails to close; however, this comes at a cost, as the price will be less favorable.



Hedging with capital market instruments
Hedging the pipeline can also be accomplished through the use of capital markets instruments, most frequently using the TBA, or “To Be Announced”, mortgage-backed securities market. Larger, more sophisticated lenders tend to use this vehicle due to efficiency, flexibility gains and the ability to employ warehousing strategies to boost interest income – all leading to higher returns.

A successful hedging program includes three key steps:

1. Maintain models and accurate data
Because hedging decisions are made based on data, data quality is paramount to the hedging process. Ensuring accurate and timely data is of utmost importance, and often involves disciplined and rigorous databasing and IT architecture. Automation and integration of the LOS, servicing platform and financial modeling software are important to foster efficiency and to reduce the possibility of human error.

2. Estimate fallout
Understanding fallout, as discussed, is imperative to the hedging process, and can contribute significantly to hedge tracking error. Factors impacting fallout include interest rate movements, product type, pipeline stage, borrower characteristics and origination channels.

3. Compute the hedge dollar amount
To determine the dollar amount that needs to be hedged, the risk manager must measure the market risk exposure associated with the mortgage assets, after adjusting for the expected fallout impact. Also depending on the institution’s circumstances, the mortgage servicing rights (MSR) asset volatility could also be important to model. Because the firm has a long position in mortgages, the firm should initiate a hedge by selling short the appropriate amount of TBA MBS.

A well-planned mortgage pipeline management program reduces the risk of the pipeline’s price volatility. Eliminating all risk would mean a perfect score, even if the hedge position resulted in a loss. Adjustments to the hedging process should reflect post-process evaluations of the accuracy of predictions, such as the back-testing of hedge ratios.

While internal hedging can bring cost savings, ultimately a hedge strategy is only as good as its execution. Thus, partnering with firms that are experienced in analysis and capital markets is often a prudent approach.



Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

Robert Perry, Principal – ALM and Investment Strategy, ALM First Financial Advisors, LLC
Robert Perry is a Principal at ALM First Financial Advisors. He is responsible for the ALM and Investment Strategy Groups, which includes development of asset liability and investment portfolio themes, as well as strategic focus for financial institution client portfolios primarily invested in the high credit quality sectors. He also is instrumental in balance sheet hedging strategy development.



AUGUST 16, 2017
Building an Optimal Investment Portfolio
By Robert B. Segal, CFA, Atlantic Capital Strategies, Inc.

Bank investment portfolios are an increasingly important part of balance sheet management. As portfolios have grown by 5.9% over the past year, according to the FDIC, they also produce a larger share of earnings. However, regulatory challenges and the low interest rate environment have pushed some into lopsided positions, such as high concentrations of agency notes and collateralized mortgage obligations (CMOs), with those institutions dealing with what are now sub-optimal portfolio allocations.

These investment portfolios show heightened risk exposures, whether through maturity extension, early call features or declining levels of income – less palatable sources of risk in an industry currently focused on improving earnings. In order to boost long-term performance while mitigating risk, investment officers should keep an eye on the following key areas.

Target Duration
Investment policy statements describe the framework by which the institution manages its portfolio. One goal is to enhance profitability within the overall asset/liability management objectives, while a second aim is to establish a process for implementing specific measures to manage sensitivity to interest rate changes.

Accordingly, management should establish a target level of duration that reflects the institution’s asset/liability position, income requirements and risk tolerance. Academic studies consistently show that longer-duration portfolios provide higher levels of income. At the same time, highly-leveraged institutions need liquidity to fund loans, and this may reduce the desired level of price sensitivity, causing the investment officer to “shorten-up.”

Maintaining duration, moreover, is an essential factor in preserving margin and maximizing net interest income. As portfolios age, duration can decline unless cash flows are reinvested back out on the curve; this “opportunity cost” limits earnings potential. Similarly, portfolios comprised exclusively of mortgage securities can extend if prepayments lag initial projections, creating unexpected interest rate risk. Investment officers should closely monitor their portfolios and take steps to ensure target durations are preserved to protect net interest income.

Diversification
Many portfolios become heavily weighted toward certain “comfortable” sectors. The returns fixed-income investors receive are determined by various factors, such as volatility of rates, credit and yield curve slope. An emphasis on callable agencies, for example, implies a reliance on returns from taking extension risk or prepayment risk.

With an expected drop in market rates, this institution will receive unwanted funds which must be reinvested at lower yields. Conversely, calls slow down in a rising-rate environment, providing less cash to put to work at better yields or to fund loans. A diversified portfolio (more call-protected assets, in this case) would keep cash flow fluctuations to a minimum, leading to improved portfolio performance.

Cash Flow
It is recommended that the treasury group prepare cash flow projections in a base case, as well as several alternate scenarios. An institution exposed to mortgage security prepayments, for example, can act in advance to protect against a decline in income in a falling-rate environment by either pre-investing or realigning the portfolio. The cash flow projections provide the information necessary to understand the position and evaluate suitable strategies, with the ultimate goal of establishing an optimal cash flow profile.

Bond Ladder
A laddered portfolio consists of securities that mature in successive years, starting in the short term and extending out to five years or longer. Assembling a stable basket of cash flows avoids locking in all one’s funds at “low” yields, while enabling the investor to pick up some additional income.

The benefit of a ladder is that as rates move higher, bonds coming due in the near term can provide funds for reinvestment when the alternatives may be more attractive. Depending on the institution’s preference and individual situation, the principal can be put to work at the desired maturity. If current yields are higher than the bonds rolling off, the institution is able to increase overall returns, boosting portfolio performance.

Fixed vs. Floating
Many investment officers wonder about the optimal strategy for deploying assets – whether to put on longer-term fixed-rate investments that pay a higher coupon or add floating-rate instruments that would benefit if rates rise. The investment officer might be considering two options: a five-year fixed-rate note yielding 2.4% or a similar term floating-rate bond priced at 90-Day LIBOR (1.3%) plus 50 basis points, for a current yield of 1.8%.

If the market forecast is correct, then the yield for the floating-rate bond will increase 25 basis points in September 2018 and a similar amount the following year – bringing the yield to 2.3% at September 2019. By contrast, the institution will have received a constant 2.4% for the fixed-rate option. Assuming a $1 million investment, the fixed-rate bond provides interest income of $72,000 over the three-year time horizon, compared with $65,250 for the “floater.” Even as the yield curve has flattened, fixed-rate assets may still provide higher levels of current income than floating-rate alternatives in the intermediate term.

Best Execution
In light of recent advances in technology, regulatory agencies such as FINRA have reiterated their commitment to ensuring best execution as a key investor protection requirement. FINRA stated in a November 2015 regulatory notice, for example, that the market for fixed-income securities has evolved significantly and transaction prices for most securities are widely available to market participants.

Broker/dealer transaction costs can vary greatly based on the scope of the transaction and access to the most liquid dealers. For example, the Bid-Ask Spread Index from MarketAxess shows that block trades on actively traded corporate bonds currently have a 3-basis-point bid-ask spread, and “odd lots” trade at 7 basis points. Individual transactions often trade at higher spreads, indicating that investors may be “leaving money on the table.” A more diligent approach toward trading efficiencies could help support the bottom line.

Municipal Bonds
Banks have boosted their holdings of municipal bonds steadily over the past decade, according to Federal Reserve statistics. Industry reports generally show that institutions holding larger percentages of municipal bonds tend to be the high performers, and banks holding at least 30% of their investment portfolios in munis are typically found in the first quartile for investment yield.

A primary benefit of municipal bonds is the long period of call protection. Bank treasurers may be relatively certain they’ll hold on to the initial yield for seven to ten years, regardless of interest rate movements. With considerable optionality on most bank balance sheets, municipals provide much-needed predictable cash flow. In addition, the municipal curve remains steep, providing some price protection for a rising-rate environment.

The Bottom Line
Taking some of these steps may enable management to build more efficient investment portfolios that generate higher levels of income over time. Building predictable cash flow characteristics provides the flexibility to manage the portfolio effectively within the context of the balance sheet, while also leading to stable returns.

Of course, the institution should consider its asset-liability position when making these decisions. Investment officers should also continue to maintain robust risk management practices, keeping interest rate risk exposure at reasonable levels.



Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

Robert B. Segal, CFA, Atlantic Capital Strategies, Inc.
Robert B. Segal is the founder and CEO of Atlantic Capital Strategies, Inc., and has over 35 years of experience in the banking industry, having worked in several community banks with roles in mortgage banking, sales and trading and asset-liability management. Bob is also currently a Director-at-Large on the FMS Board of Directors.




AUGUST 8, 2017
Understanding Requirements for Model Validations
By James Jarrett, Director, Baker Tilly Virchow Krause, LLP

With the use of models becoming more frequent among financial institutions, federal examiners are pressuring institutions to perform validations on all of the models being utilized. The common models being used include Bank Secrecy Act/Anti-Money Laundering, Interest Rate Risk/Asset Liability Management and Allowance for Loan and Lease Loss (ALLL).

Management and individuals involved in modelling at financial institutions need to understand the applicable regulatory requirements per current bulletins, key elements to review for each type of model validation – including frequency of completion – and best practices for reporting the results.

REGULATORY REQUIREMENTS

The Office of the Comptroller of the Currency (OCC), the Federal Deposit Insurance Corporation (FDIC) and the Federal Reserve Bank (FRB) have all issued specific guidance on the use of business models. The items below provide some history for regulatory guidance on model validations:
Track movement of loans by segment
to identify trends in portfolio growth
Institutions can review growth patterns of the loan portfolio by looking at their segments and by reviewing their balances. If a specific segment has grown significantly, the institution can begin to identify and document the reasons for changes in loan demand and supply.

Model Risk Management
The use of a model does not reduce risk to zero. Model risk is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. Model risk can lead to financial loss, poor business and strategic decision-making or damage to an institution’s reputation. Model risk should be managed like all other risks and be part of the annual risk assessment process.

Regulatory guidance outlines a principle for managing model risk called “effective challenge,” which is defined as critical analysis by objective, informed parties who can identify model limitations and assumptions and produce appropriate changes.

In its basic form, a model consists of three components:

Each of these three components should be included as part of the model validation process to help ensure there are no areas of weakness that would increase the overall model risk.

Model Risk Management Framework
An effective model risk management framework should include:

■ Disciplined and knowledgeable development that is well documented and conceptually sound
■ Controls to ensure proper implementation
■ Processes to ensure correct and appropriate use
■ Effective validation processes
■ Strong governance, policies and controls
■ Use of vendor and other third-party models should be incorporated into the model risk management framework

These points outline that the use of a model begins prior to implementation, a documentation roadmap is essential and the process does not end with implementation. Additionally, the last bullet point states that even third-party models are subject to the other bullet points.

There are three key elements to model risk management:


MODEL VALIDATION
Model validation is specifically detailed in the regulatory guidance – it is the set of processes and activities intended to verify models are performing as expected. Additionally, model validations identify potential limitations and assumptions and require the need to determine the impact. Model validations should be completed by staff with the appropriate knowledge and experience of the model subject matter.

The three components (input, process and reporting) should be subject to validation. This applies to in-house developed models, as well as those purchased from a vendor. The amount of testing required will depend on how the model is used and the amount of control the institution has within the model. In some models (e.g., Interest Rate Risk), the institution may supply the third-party vendor the input information and assumptions to be input into the model. In these cases, the institution does not have direct access to the model calculations and scenarios. In comparison, a BSA/AML model and an ALLL model are usually purchased software that is implemented on the institution’s information technology domain. The institution will have direct access to the software, including the ability to edit assumptions and alerts and to tailor the model to its products, services and operations.

Model validations should be completed by staff independent of those responsible for implementation, development and use. While staff completing the validation should have the knowledge, skills and expertise needed for that subject area, the concept of knowledge does not mean from an information technology standpoint. The person completing the validation should have knowledge of the purpose of the model. For example, when validating a BSA/AML model, the person completing the validation should have sufficient knowledge of the requirements of BSA/AML to be able to review transaction detail, alerts and suspicious activity.

It is always best to complete the model validation in a test environment. This would eliminate the potential negative impact on “actual” customer information in the event there are issues. Additionally, model validation is not a “one and done” process. The regulatory guidance states “Banks should conduct a periodic review – at least annually, but more frequently if warranted.” As these model validations are normally done by a third party, this is an additional annual cost institutions must consider.

Components of a Model Validation
Conceptual design
Evaluate the logic and design of the model.

The model was designed in a way to achieve a certain objective; now the question is: Is the model designed in a way to do exactly that? Is there anything missing? Are all risks that the institution is exposed to taken into consideration? Does it include all products and services? Documentation is key in this component and ensuring the proper group is involved.

System validation
Validate the system to ensure that it is properly designed to perform.

After ensuring that the conceptual design is adequate in mitigating risks, the system itself should be tested to ensure that it reflects the same. For example, testing the output and effectiveness of the generated alerts to drive further tuning of the thresholds and scenarios. In many cases, institutions should run the model parallel to the existing process for several months to validate the results. During the validation process, this parallel testing should be reviewed.

During system validation, it is essential to ensure systems, products, services and transactions are considered and flow to the model. For example, the implementation of the BSA/AML model would need to ensure all products, services, systems are considered. Not all products (e.g., Trust) are contained on the core processing system. The model validation should verify that all systems are properly mapped to the model software.

Data validation
Validate that accurate and complete information is captured by a system to execute the model.

A system can be designed and implemented to achieve its objective, but end up failing badly due to data integrity issues. If the input data is not reliable, the output would not be in a position to give any value. This part will require identifying source systems and transaction codes, ensuring accurate data feeds. This piece of the validation is critical as the results of the data drive the results of the model and the reporting. During this phase of the validation, information is traced from the originating system to the model to verify all of the key data is captured. This would include any assumptions within the model. The basic concept here is “garbage in, garbage out.”

Process validation
This phase includes an evaluation of controls, the reconciliation of source data systems with model inputs, and the usefulness and accuracy of model outputs and reporting.

During this phase, it is verified that everything from the core system (source data) was captured by the model. For an IRR model, this involves a review of the data sent to a third party and the output reports compiled by the third party to ensure the information is part of the model.

Model Risk and Deficiencies
Several factors can influence the outcome of the validation and whether it performs as it should. The most common issues affecting the effectiveness and accuracy of the models include: ■ Exclusion of customers, products and services

■ System data is inaccurate, incomplete or irrelevant to the model purpose or design
■ Data mapping errors/irregularities, file load errors
■ Design of rules and/or configurations inconsistent with regulatory expectations and the institution’s risk exposures
■ Logic errors which produce inaccurate output
■ Lack of change management and/or adaptation to changes in organization activities that affect model performance
■ Lack of resources and expertise to effectively manage model risk management activities
■ Unclear lines of authority or accountability

REPORTING THE RESULTS
While a model validation is not technically an audit, a formal report should be written or issued if a third party has been contracted to complete a model validation. The reports should be issued to management responsible for the model. Additionally, consider presenting the report to the audit committee or board of directors of the institution. The information can help inform them on various components of the institution’s operations and strategies.

The following items should be included in the report:

■ The scope of the model validation
■ The date the validation was completed
■ Which regulatory compliance requirements the validation was conducted under (e.g., OCC, FRB or FDIC)
■ Detailed procedures completed during the validations
■ Detailed recommendations for improvements and corrective action to be taken by management
■ An overall rating (e.g., satisfactory, needs improvement, unsatisfactory) as to the effectiveness of the model

During the next regulatory review, the report and workpaper documentation should be provided to the examiners.

For those institutions that are subject to validation requirements, following the rules and timing requirements are a must, but all financial institutions using models within their organization would be wise to validate. As organizations understand the validation process more thoroughly, there are organizational and strategic opportunities to be gained.



Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

James Jarrett, Director, Baker Tilly Virchow Krause, LLP
James Jarrett is a director in the corporate governance and risk management group of Baker Tilly Virchow Krause, LLP and has more than 25 years of audit, accounting, and regulatory compliance experience in the financial services industry




July 18, 2017
Identifying Defendable Modeling Assumptions 
By Jerry Clark, Senior Vice President – Sales and Marketing, ZM Financial Systems

The words “Asset Liability Management” make our field sound very precise, but often the most important part of managing your risk and profitability is overlooked: assumptions.

Assumptions are critical when modeling potential future outcomes, as the data your model reports is only as good as the data put into the model. Where do you get your assumptions and how do you know they are “right?” First, a few key points to consider when developing modeling assumptions:

1.  Assumptions should be grounded in specific historical experience whenever possible.
2.  Adjustments should be made to reflect changes from the past (such as new management).
3.  Common sense and intuition are allowable.
4.  Industry averages and/or third-party supplements may be appropriate when lacking quantifiable experience.
5.  Stress testing your assumptions is very important.

AL models differ in look and feel, but most offer the ability to perform valuation and income simulation. Assumptions are grouped into those that impact cash flows, pricing and economic factors, but before delving into an assumption discussion, it is important to reiterate the age-old saying “garbage in garbage out.” The data you use as a foundation for your modeling must be as accurate and complete as possible. Layering assumptions on top of bad data, regardless of the correctness of the assumptions, can magnify inaccuracies and lead to wrong conclusions. Loan and deposit details should be loaded by instrument to correctly capture attributes such as caps, floors, pricing and payment structures.


Cash Flow Adjustments
1. Prepayments occur when borrowers make payments ahead of schedule on their loans. These should be layered onto contractual cash flows for lending-based products such as mortgage loans, commercial loans and mortgage-backed securities.

Most AL models allow the use of prepayment speed projections (e.g., CPR) and allow you to vary the speeds by forecast scenario. Commercial and other loans can be more challenging given their unique structure. Regression analyses and formulas are more appropriate, although it may be simplest to calculate historical averages and apply them to your projections for these loans. Some AL models also allow integration with third parties to incorporate multiple factors beyond rates, providing more dynamic prepayment modeling.

On a related note, prepayment penalties exist in many loan contracts and should be modeled when they exist.

2. Structured Cash Flows are unique to instruments such as CMOs. The correct way to model these is to use an engine backed by a deal library containing the payment rules for a particular scenario. Another approach is to import scenario-specific cash flows for your portfolio from the broker who provides the instruments – this is acceptable as long as the scenarios you receive match up with the scenarios you are modeling. Many institutions model CMOs like regular mortgages, ignoring the payment rules and only applying simplified prepayment matrices. This is rarely an acceptable approach and can lead to hidden risk. You should strongly consider the significance of these balances before taking such an approach.

3. Defaults and Recoveries happen when loans cannot be repaid under the contractual terms. Modeling for these has become common in AL models, given that DFAST, CCAR and CECL have hit the mainstream. Probability of default (PD) and loss given default (LGD) are the most common projection metrics, although migration matrices are also popular.

4. Early Withdrawals are very similar to prepayments, and occur when depositors withdraw their money prior to maturity for their term deposits. Decays are declines in deposit balances that do not have specific maturities. Deposit studies may be required to understand your unique behavior. As with loans, early redemption penalties often exist on term deposits and should be modeled when they exist.


Pricing
1. Pricing/Spread is an assumption driven more by policy and committee than historical experience. A recent historical analysis is a great place to start. You might look at loans or deposits originated last month against a driver rate or yield curve to get a baseline; however, understanding your pricing process could lead you to model future business differently than past business. One hint here: remember that business is negotiable – published spreads often differ from reality, so spend a little time researching and comparing before settling on spread assumptions.

2. Rate Responses and Lag Effects are used to mimic the timing delay between market rate moves and rates on products such as deposits. Single- and multi-betas are often the assumptions derived and then put into AL models for forecasting these rate movements.

3. New Business Term Structure is tied to pricing/spread in most models: instruments are priced by referencing term points on a yield curve. Development of this assumption also requires research of your recent history to understand patterns and behaviors. A data warehouse can be an excellent tool for understanding both the pricing and term structure behavior in your new business.


Economic Forecasts
1. Determining the Rates to use when modeling depends on your purpose:
-For valuations, an implied curve derived from market rates is preferable. Bloomberg and Reuters are commonly used sources of market rates.
-Stress testing can take many forms. Rate shocks, ramps and twists are usually derived from market rates, again from a source such as Bloomberg.
-When projecting earnings, it is generally appropriate to use an internally-developed rate forecast. The premise is that you plan based on expectations, so your budgets and goals should be set based on some sort of most likely forecast. Some institutions are uncomfortable making projections, in which case they rely on S&P (Global Insights) or another consensus-type forecast.

2. Balance and Fee Projections usually come from either line of business feedback or top-down goals. Advanced institutions may use econometric models to estimate behavior, but direct feedback and estimates are usually preferable. Mortgage servicing contains very unique attributes that may require external assistance to model.

3. Economic Factors such as CPI, GDP and unemployment are important ingredients when moving beyond basic income forecasting to projecting losses, capital, liquidity and other aspects of your business.

AL models contain other broad assumptions such as discount curves and volatilities, as well as instrument-specific assumptions, including discounting methodologies and spreads. Assumptions in your modeling process should be understood and defendable by someone in your organization. The last thing you want is for an examiner or your manager to ask a question you cannot answer.

Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Author

Jerry Clark is Senior Vice President of Sales and Marketing for ZM Financial Systems and has more than 30 years of experience in market risk, treasury, accounting and finance.


June 27, 2017
Creating Funding Stability During Uncertain Times 
By D. James Lutter and Todd A. Terrazas, PMA Financial Network

The days of cheap funding appear to be winding down. Since 2008, financial institutions have been able to access and maintain stable deposit balances. Due to risk aversion among the general public, deposits across all institution sizes have witnessed significant growth with relative ease.

Accessible funding has been a great benefit to financial institutions. However, with most economists and Wall Street professionals believing the economy to be in the latter stages of a bull cycle, it is important for institutions to document and understand how each source will react in various stress situations. Understanding funding sources and how they will react to different markets can help lead to a proper liquidity balance.

Identify and Define Funding Sources
There are a variety of funding options available to financial institutions, and it is important to incorporate those that fit within your strategic plan. In doing so, you can identify what gaps exist within your current funding sources by answering a few basis questions:

- Are deposits in-footprint or out-of-footprint?
- Are deposits operating or non-operating?
- Are deposits commercial, retail or institutional?
- What are your noncore funding sources – wholesale, reciprocal or listing service?
- What are concentrations amongst the various sources and what are their investment objectives (rate, diversification, etc.)?
- What degree of interest rate sensitivity exists and how are you hedging it?

By answering these questions, a financial institution can obtain a quick snapshot of its deposit mix and begin to effectively define an operating and contingency funding plan.


Which Funding Sources Are Right for Your Institution?
After recognizing current funding sources and any pitfalls that may exist, a financial institution should look to bridge the gaps. Once appropriate funding options have been determined, the next step is to identify the role each option will play within an operating and contingency funding plan. It is critical that diversification, credit sensitivity and concentration limits be included.

A good test of these attributes can be identified through analysis of the strengths, weaknesses, opportunities and threats (SWOT). For example, a SWOT analysis of a municipal depositor may resemble the following:

Strengths – A municipal depositor is typically local, has a predictable deposit cycle and can be a stable funding source

Weaknesses – Deposit capabilities can fluctuate and are cyclical, usually requiring some form of collateralization (per state statute or investment policy); credit restrictions may also be present

Opportunities – A municipal client can become a significant, multifaceted relationship through transaction activity, long-term banking service contracts, borrowing, safekeeping, etc.; additionally, diversification among multiple municipalities may mitigate cyclicality risk

Threats – General economic conditions may deteriorate, creating revenue shortfalls from a declining tax base and/or a delay in state or federal aid

Regulators expect a financial institution to have established funding policies, ensuring that proper controls are in place to adequately address the environment in which it operates. Testing sources on a regular basis allows the institution to readily access funds as needed, while eliminating the element of surprise.


Monitor and Maintain Your Funding Sources
To avoid undue stress, it’s important for financial institutions to monitor the inherent risk characteristics of its funding sources, as well as the evolving needs of those sources. Gaining a comprehensive understanding of your funding sources and the relationships to their investors and depositors provides much needed information to help understand how those deposits will respond under stress.

Adverse effects to a financial institution’s credit profile will increase the cost of funds and may limit its ability to access funding. Different depositors have diverse investment criteria and yield expectations. A comprehensive understanding of these metrics will enhance the financial institution’s ability to price and access funding sources. Furthermore, it allows the institution to execute a risk-averse operating and contingency funding plan. To build a solid, ongoing understanding of its funding sources, a financial institution should continually ask these important questions:

-How does the market view my institution? Do I know the credit criteria my funding sources monitor (qualitative and quantitative)? What are the implications if the criteria are breached?
-Do I understand my funding sources’ (depositors’) investment objectives (safety, liquidity, yield, etc.)?
-Have I identified, and do I monitor, the factors that could affect my ability to access various funding sources?
-Does my funding source have concentration limits?
- Have I documented each funding source’s role and communicated it where applicable?


Conclusion
Developing reliable, diversified funding sources is critical to the success of a financial institution. By defining, identifying and maintaining funding sources, an institution can gain further insight and discover tools that help mitigate risks when issues arise. A well-defined plan will help maintain stability, provide sound liquidity and interest rate management, and add value through increased earning.

Disclaimer: The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of the Financial Managers Society.

About the Authors

D. James (Jim) Lutter, Senior Vice President of Trade Operatons, PMA Financial Network
D. James Lutter is is the Senior Vice President of Trading and Operations at PMA Financial Network, Inc. and PMA Securities, Inc., where he oversees PMA Funding, a service of both companies that provides over 1,000 financial institutions with a broad array of cost-effective funding alternatives.

Todd A. Terrazas , Business Development & Product Manager, PMA Financial Network
Todd Terrazas is the Business Development & Product Manager for PMA Funding, where he is responsible for developing financial institution partner relationships and managing funding product solutions and association affiliations.