Kaan Etem, Author at Cogent QC: Award-Winning Loan Quality Control & Compliance Software

Cogent Quality Trend Reports Demystified – Infographic

October 23, 2013 By Kaan Etem

Cogent QC Systems ship with numerous standard reports, organized by category. Loan Status reports help managers to track the progress of audit activities; Audit Findings reports show audit findings from various perspectives, from granular detail to summary overview, but always for a particular period; and Feedback and Letter reports are designed to track specific audit activities.

In contrast, most Management reports show quality trends across multiple periods for specific sample groups (such as the Statistical Sample or the Stratified Sample, each of which have specific definitions in Cogent.) In addition, and perhaps most importantly, Cogent’s Quality Trend reports show quality levels for particular sample periods and specify the precision with which these may be inferred to the population.

It’s one thing to report the audit results of a selection of loans sampled from a population of loans (“we found 2 defective loans out of the 30 we audited, for a defect rate of 6.7%”); it’s another thing to make inferences, based on the results of the sample, to the population as a whole (“we are 95% confident that the population from which we sampled has a defect rate of 6.7%, plus or minus 2%.) In order to make valid statements like this, sampling and auditing and reporting must be controlled to eliminate bias and maintain statistical integrity. Cogent QC Systems do this for you automatically (while providing leeway to separately do non-random, non-statistical sampling.) The results are presented in Cogent Quality Trend Reports.

Since most clients do not live the dream of statistical analysis on a daily basis, like we do at Cogent, it’s possible that statistical terminology is not top of mind. So we have created this infographic to help with the interpretation of Cogent’s Quality Trend Reports (click on the image for a larger version). Please pass the link around among users of Cogent in your organization. We welcome insights and feedback at support@cogentqc.com.

Secondary Review Options in Cogent QC Systems

October 14, 2013 By Kaan Etem

In the most recent issue of American Banker, the CEO of Cape Cod Savings had this to say about the burden of regulatory compliance:

“…Because of HMDA and RESPA, we have checkers who check the checkers. Then we actually have another third layer of checkers who check the checkers who check the checkers. Then we have two outside consulting firms that check again.”

Sound familiar? All too familiar for some Cogent clients, who have multiple layers of QC and compliance operations – often at the corporate level, at the business unit level, and on an outsourced basis. And they invest so much in quality control and compliance because the alternative is painful. To cite just one instance, the Mortgage Bankers Association (MBA) sent this around last week:

“CFPB Assesses Civil Money Penalties For HDMA Data Errors
The CFPB has announced that it assessed civil money penalties against Mortgage Master, Inc., a non-bank, and Washington Federal, a bank, after examinations identified significant data errors in mortgage loans reported pursuant to the Home Mortgage Disclosure Act (HMDA). CFPB followed the announcement with a bulletin outlining the elements of an effective HMDA compliance management system and the resubmission thresholds, as well as other factors that the Bureau uses to determine if they will pursue a public HMDA enforcement action and associated civil penalties.”

Among other things, that bulletin states that “effective HMDA compliance management systems frequently include … comprehensive and regular internal, pre-submission HMDA audits.”

Aside from under-scoring the sheer scope of today’s regulatory compliance requirements, this reality also highlights the need for efficiency in performing secondary and tertiary audit reviews. This is why Cogent has been introducing more extensive secondary audit review options. The latest Supervisor Override functionality was covered in our recent ‘Version 4 Overview’ webinar (clients may contact support@cogentqc.com for a link to the recording.) With that, the possibilities now include:

Revert a completed loan audit and make changes to the original loan audit.
Use Supervisor Review to conduct a parallel supervisor audit, while preserving the original auditor’s work as the official audit of record.
Use Supervisor Override to override individual findings, thereby modifying the official audit of record but preserving a record of the original auditor’s findings.

These different approaches can be combined with appropriate pending and completion of loan reviews to tailor different secondary reviews to different situations. When all eyes are on you, it’s always good to have options.

Categorize Audit Questions for Streamlined Reporting of Regulatory Data

September 30, 2013 By Kaan Etem

Sometimes it makes sense to organize audit questions by regulation. Compliance audits are often organized this way, with names of regulations comprising audit category names and audit questions clustering within those categories. This organization can be reinforced by audit category codes such as TIL, ECOA, CLA, FCRA and so on (hence, question number ‘TIL-012a’). This approach makes it quick and easy to report on regulation-specific audits using Cogent.

But frequently, audit questions are organized by category of defect, such as Assets, Credit, Liabilities, and Income. Traditional post-closing audits continue to be organized this way, as confirmed by FNMA recently (see recent blog post) In such a case, how do you report on specific regulations when a regulator comes in for an audit? The answer is via Categories.

Cogent’s loan audit software allows any audit question to be tagged with one or more Categories. Standard Categories include ‘area tested’, ‘regulation’, and ‘federal/state’. Additional Categories may also be created. With Category tagging, it is a simple matter to include in a report only those audit questions that are relevant to a regulatory audit. Access Category tables via your system’s Audit Lookup Table Manager under Administrator Tools.

MBA and FHA and Statistical Sampling in Quality Control

September 25, 2013 By Kaan Etem

We are pleased to see the FHA proposing to introduce more statistical sophistication into its Quality Assurance Process (QAP) and to see the MBA responding with reasonable critiques. There are a number of items under discussion which have been long-standing issues in the industry, including what defines a loan manufacturing defect, what are appropriate tolerance and severity levels for defects, and what are appropriate remedies. “Loan quality” must have a standardized definition to be useful. But the item that caught our attention was the discussion of statistical sampling.

FHA is proposing the following in its solicitation of information:

“Statistical sampling. FHA is also considering whether to establish a process to review a statistically significant random sample of loans for each mortgagee within a prescribed time frame after loan endorsement. Lenders would receive feedback on findings within an established timeframe. FHA would use the statistical sample, to estimate the defect rate on each lender’s overall FHA portfolio and then extrapolate the origination defect rate to all lender originations during the sampled time period, and thus have the lender compensate FHA for the estimated total risk to FHA resulting from the lender’s origination processes.The purpose of this process would be to increase the efficiency of FHA’s post-endorsement review process. HUD invites comment on the use of and optimal methodology for a statistically significant random sample, including the nature of the loans that should be included or excluded from the sample.”

The MBA has responded with this:

“Most importantly, MBA has serious concerns about the impact of a sampling methodology on independent mortgage bankers and community banks and the number of lenders participating in the FHA program. While larger lenders may be able to originate enough loans to generate statistically significant sample sizes, many smaller lenders would be challenged in this regard. It is unclear how HUD would address this situation and what, if any, allowances would be made for small lenders. Moreover, depending on the structure of the penalty system, paying an upfront percentage could have a much greater impact on smaller lenders than larger lenders. The possibility of sampling bias that results in “overpaying” for smaller lenders has potentially devastating consequences reducing competition and increasing the price for consumers. Companies could be forced out of business or cease originating FHA loans.”

Given the number of lenders we have seen who report only on the number of “findings” in their reviews, with no mention of defect rates or sampling method or population counts, it is encouraging to hear influential industry players talking about sample sizes and valid inferences to populations and statistical significance (albeit in a slightly different context.) If nothing else, it reminds us that the loan audits that take up so much of our time represent a small fraction of the loans we originate (or service). And that what matters is the quality of the entire origination (or servicing) pool, not just the samples we draw (which are simply proxies for the population.)

We say let the discussion continue. The more informed lenders are about what constitutes loan quality, the better they can do their jobs.

Fannie Mae releases new list of Loan Defect Categories

September 25, 2013 By Kaan Etem

In case you missed it, Fannie Mae released its new list of Loan Defect Categories on August 27, 2013.

QC- loan-defect-categories-FNMA

Says Fannie, “The list shows the loan defects, by categories, identified by Fannie Mae in post-purchase review of our acquisitions. These defects (which may be eligibility violations) are referenced in reporting to lenders on the quality of their deliveries.”

So if you are setting up or overhauling your standard post-closing audit checklist, this is a good generic template to begin with. It covers all of the essential categories of loan QC errors (or of loan quality, to put it another way). If you are using Cogent’s loan audit software, in which these are called audit question categories, all that’s missing is a category and question coding scheme. With that, your reporting possibilities are almost limitless, allowing you to report by defect category or sub-category, by individual audit question, and so on.

Thanks to Fannie for taking another step towards standardizing the scope of loan quality reviews.