One of the most far-reaching outcomes of the mortgage meltdown is the recognition that when the data underlying an application is inaccurate, the loan is not likely to be either suitable or sustainable.
This recognition is reflected in FHA verification mandates, the FNMA Loan Quality Initiative, the documentation requirements under HAMP, the discussions surrounding the definition of a “qualified residential mortgage” under Dodd-Frank, the put-backs and rescissions, and in the myriad of lawsuits and investigations in the “robo-signing” mess.
The resounding message coming out of Washington and elsewhere is that lenders will soon have to devote significant attention to ensuring that all of their mortgage credit decisions are made on a solid foundation of reliable and accurate data. Practically speaking, this means that risk controls will be needed at every phase of the mortgage life cycle, from origination to servicing and through loss mitigation.
While this may seem to be a daunting task, many lenders already have a very powerful risk control tool in their tool kits. If it were to be deployed across the full spectrum of the loan life cycle, with the results being fed back to the appropriate departments, this tool would not only help insure loan quality but it would provide extremely useful business intelligence and enable more effective enterprise risk management.
The tool is an automated fraud detection system. At its most basic, it functions as a threshold check on data integrity in loan originations that, if not caught, could lead to default or which could cause lenders to inadvertently violate TILA.
When deployed in loan modification efforts, it could identify borrowers who present a significant risk of redefault or expose a lender to False Claims Act liability because they’ve misrepresented their Social Security number, or their income or collateral value. When deployed in loss mitigation efforts, this tool can spot attempts to fraudulently manipulate collateral values, undisclosed business affiliations, and collusion by transaction participants in short sales that, if not detected, will increase the borrower’s deficiency and the banks’ liability to investors.
Automated fraud prevention systems can also provide critical business intelligence at every stage of the mortgage loan life cycle. These systems help identify problems with employee training and program implementation, internal process and product weaknesses, geographic hot spots and trends.
For example, one lender using metrics relating to alerts for foreclosures determined that while at the beginning of the sampling period, only 1% of loans were flagged for this issue. Six months later, almost 40% of loans were being flagged—and in one neighborhood, the incidence went up by 500%.
Knowing this allowed the lender to change its process and to implement additional risk controls. These tools can also be used to measure the effectiveness of process and policy changes.
Another lender analyzed its fraud alerts and discovered that 32% of all findings related to collateral valuation issues. One year later, after the implementation of the HVCC and internal process changes, those findings dropped to only 4%, a sure sign that the changes were working.
While it will take time to fully develop and implement such a holistic approach to risk management, there are some steps that can be taken now.
The first critical step is to train all credit decisioning personnel in how to recognize when a borrower’s supporting data and documentation is improbable, incorrect or manipulated. Next, a communication feedback loop should be developed. Ideally, it would funnel significant findings and patterns from originations, servicing, loss mitigation and quality control to special investigations or fraud units. The results from the SIU and fraud department reviews should be used to identify the root causes of the findings so that senior management can evaluate current processes and controls and make corrections where needed.
Last, but certainly not least, is management’s need to hold everyone accountable for loan quality, and to communicate that expectation to employees. While training makes them aware of what is, and is not, acceptable, compliance is increased when the employees acknowledge in writing that they know what the policies are, that they know what the penalties are for violations, and that they agree to abide by them.
The industry needs to recognize that these systems provide both a means to ensure data integrity and serve as an integral part of business intelligence and enterprise risk management solutions. The key is that lenders who take these necessary steps will establish a firm foundation from which to restore the reputation of the mortgage industry, rebuild confidence in loan originations, and help secure their own future success.
Ann Fulmer is the VP of business relations at Interthinx.
