The importance of sound internal data gathering practices cannot be understated. However, in light of the new CECL standard, many lending institutions have found themselves unable to meet the data requirements. This may have served as a wake-up call for organizations at all levels to look at their internal data warehousing systems and identify and remedy the gaps in their strategies. For some institutions, it may be difficult to consolidate data siloed within various stand-alone systems. Other institutions, even after consolidating all available data, may lack sufficient loan count, timespan, or data elements to meet the CECL standard with internal data alone. This post will discuss some of the strategies to make up for shortfalls while data gathering systems and procedures are built and implemented for the future.  

Identify Your Data

The first step is to identify the data that is available. As many tasks go, this is easier said than done. Often, organizations without formal data gathering practices and without a centralized data warehouse find themselves looking at multiple data storage systems across various departments and a multitude of ad-hoc processes implemented in time of need and not upgraded to a standardized solution. However, it is important to begin this process now, if it is not already underway. As part of the data identification phase, it is important to keep track of not only the available variables, but also the length of time for which the data exists, and whether any time periods have missing or unreliable information. In most circumstances, to meet the CECL standard, institutions should have loan performance data that will cover a full economic cycle by the time of CECL adoption. Such data enables an institution to form grounded expectations of how assets will perform over their full contractual lives, across a variety of potential economic climates. Some data points are required regardless of the CECL methodology, while others are necessary only for certain approaches. At this part of the data preparation process, it is more important to understand the big picture than it is to confirm only some of the required fields—it is wise to see what information is available, even if it may not appear relevant at this time. This will prove very useful for drafting the data warehousing procedures, and will allow for a more transparent understanding of requirements should the bank decide to use a different methodology in the future.  

Choose Your CECL Methodology

There are many intricacies involved in choosing a CECL Methodology. Each organization should determine both its capabilities and its needs. For example, the Vintage method has relatively simple calculations and limited data requirements, but provides little insight and control for management, and does not yield early model performance indicators. On the other hand, the Discounted Cash Flow method provides many insights and controls, and identifies model performance indicators preemptively, but requires more complex calculations and a very robust data history. It is acceptable to implement a relatively simple methodology at first and begin utilizing more advanced methodologies in the future. Banks with limited historical data, but with procedures in place to ramp up data gathering and data warehousing capabilities, would be well served to implement a method for which all data needs are met. They can then work toward the goal of implementing a more robust methodology once enough historical data is available. However, if insufficient data exists to effectively implement a satisfactory methodology, it may be necessary to augment existing historical data with proxy data as a bridge solution while your data collections mature.  

Augment Your Internal Data

Choose Proxy Data

Search for cost-effective datasets that give historical loan performance information about portfolios that are reasonably similar to your go-forward portfolio. Note that proxy portfolios do not need to perfectly resemble your portfolio, so long as either a) the data provider offers filtering capability that enables you to find the subset of proxy loans that matches your portfolio’s characteristics, or b) you employ segment- or loan-level modeling techniques that apply the observations from the proxy dataset in the proportions that are relevant to your portfolio. RiskSpan’s Edge platform contains a Data Library that offers historical loan performance datasets from a variety of industry sources covering multiple asset classes:

  • For commercial real estate (CRE) portfolios, we host loan-level data on all CRE loans guaranteed by the Small Business Administration (SBA) dating back to 1990. Data on loans underlying CMBS securitizations dating back to 1998, compiled by Trepp, is also available on the RiskSpan platform.
  • For commercial and industrial (C&I) portfolios, we also host loan-level on all C&I loans guaranteed by the SBA dating back to 1990.
  • For residential mortgage loan portfolios, we offer large agency datasets (excellent, low-cost options for portfolios that share many characteristics with GSE portfolios) and non-agency datasets (for portfolios with unique characteristics or risks).
  • By Q3 2018, we will also offer data for auto loan portfolios and reverse mortgage portfolios (Home Equity Conversion Mortgages).

Note that for audit purposes, limitations of proxy data and consequent assumptions for a given portfolio need to be clearly outlined, and all severe limitations addressed. In some cases, multiple proxy datasets may be required. At this stage, it is important to ensure that the proxy data contains all the data required by the chosen CECL methodology. If such proxy data is not available, a different CECL model may be best.  

Prepare Your Data

The next step is to prepare internal data for augmentation. This includes standard data-keeping practices, such as accurate and consistent data headers, unique keys such as loan numbers and reporting dates, and confirmation that no duplicates exist. Depending on the quality of internal data, additional analysis may also be required. For example, all data fields need to be displayed in a consistent format according to the data type, and invalid data points, such as FICO scores outside the acceptable range, need to be cleansed. If the data is assembled manually, it is prudent to automate the process to minimize the possibility of user error. If automation is not possible, it is important to implement data quality controls that verify that the dataset is generated according to the metadata rules. This stage provides the final opportunity to identify any data quality issues that may have been missed. For example, if, after cleansing the data for invalid FICO scores, it appears that the dataset has many invalid entries, further analysis may be required, especially if borrower credit score is one of the risk metrics used for CECL modeling. Once internal data preparation is complete, proxy metadata may need to be modified to be consistent with internal standards. This includes data labels and field formats, as well as data quality checks to ensure that consistent criteria are used across all datasets.  

Identify Your Augmentation Strategy

Once the internal data is ready and its limitations identified, analysts need to confirm that the proxy data addresses these gaps. Note that it is assumed at this stage that the proxy data chosen contains information for loans that are consistent with the internal portfolio, and that all proxy metadata items are consistent with internal metadata. For example, if internal data is robust, but has a short history, proxy data needs to cover the additional time periods for the life of the asset. In such cases, augmenting internal data is relatively simple: the datasets are joined, and tested to ensure that the join was successful. Testing should also cover the known limitations of the proxy data, such as missing non-required fields or other data quality issues deemed acceptable during the research and analysis phase. More often, however, there is a combination of data shortfalls that lead to proxy data needs, which can include either time-related gaps, data element gaps, or both. In such cases, the augmentation strategy is more complex. In the cases of optional data elements, a decision to exclude certain data columns is acceptable. However, when incorporating required elements that are inputs for the allowance calculation, the data must be used in a way that complies with regulatory requirements. If internal data has incomplete information for a given variable, statistical methods and machine learning tools are useful to incorporate the proxy data with the internal data, and approximate the missing variable fields. Statistical testing is then used to verify that the relationships between actual and approximated figures are consistent with expectation, which are then verified by management or expert analysis. External research on economic or agency data, where applicable, can further be used to justify the estimated data assumptions. While rigorous statistical analysis is integral for the most accurate metrics, the qualitative analysis that follows is imperative for CECL model documentation and review.  

Justify Your Proxy Data

Overlaps in time periods between internal loan performance datasets and proxy loan performance datasets are critical in establishing the applicability of the proxy dataset. A variety of similarity metrics can be calculated that compare the performance of the proxy loans with the internal loan during the period of overlap. Such similarity metrics can be put forward to justify the use of the proxy dataset. The proxy dataset can be useful for predictions even if the performance of the proxy loans is not identical to the performance of the institutions’ loans. As long as there is a reliable pattern linking the performance of the two datasets, and no reason to think that pattern will discontinue, a risk-adjusting calibration can be justified and applied to the proxy data, or to results of models built thereon.  

Why Augment Internal Data?

While the task of choosing the augmentation strategy may seem daunting, there are concrete benefits to supplementing internal data with a proxy, rather than using simply the proxy data on its own. Most importantly, for the purpose of calculating the allowance for a given portfolio, incorporating some of the actual values will in most cases produce the most accurate estimate. For example, your institution may underwrite loans conservatively relative to the rest of the industry—incorporating at least some of the actual data associated with the lending practices will make it easier to understand how the proxy data differs from characteristics unique to your business. More broadly, proxy data is useful beyond CECL reporting, and has other applications that can boost bank profits. For example, lending institutions can build better predictive models based on richer datasets to calibrate loan screening and loan pricing decisions. These datasets can also be built into existing models to provide better insight on risk metrics and other asset characteristics, and to allow for more fine-tuned management decisions.