How a Universal Mortgage Repository Improves Customer Service

How a Universal Mortgage Repository Improves Customer Service

Posted on May 24, 2016 0 Comments

This is one in a series of blogs on building a Mortgage Document or Metadata Repository to help firms in the mortgage industry handle today’s needs and exploit its opportunities.

The mortgage industry is a major part of the economy. At the end of 2015, outstanding mortgage debt in the United States totaled almost $13.8 trillion. For those making mortgage payments, payments as a percentage of income ranged from 13.6 percent for those aged 45-54 to 25.7 percent for those under 25.

In recent years firms have been raising credit standards and otherwise taking action to restrict the availability of mortgages. There are a number of reasons for this but an important one is that the labor-intensive nature of servicing delinquent loans make handling them very expensive. A universal repository that makes it possible to automate more of the servicing workflow can make delinquent loans less costly which can then make broader homeownership possible.

With mortgages playing such an important role in the lives of millions of people, high-quality customer service is key. Having a mortgage servicing infrastructure that allows firms to quickly evaluate, respond to, and resolve customer complaints is essential to keeping costs low, maintaining a good reputation, and thriving in today’s mortgage industry.

A Perfect World

In an ideal world, customers service themselves. They log onto the firm’s website and easily to find desired information about their account and quickly and accurately resolve questions and issues with little or no assistance. Access to customer support representatives is a rare event and only needed in especially unusual cases.

Today’s World

We are as pretty far away from that perfect world as one can be. According to the Consumer Financial Protection Bureau (CFPB), consumers have complained more about the mortgage industry than any other financial product. Because of the complexity of the mortgage process, many customer complaints may be as a result of customers making mistakes or not fully understand the process. In other cases, though, the mortgage servicing firm may be in error. Either way, if the issue is not resolved the customer can bring their complaints to the CFPB and each month tens of thousands do so. And in a world where we can find information at our fingertips, often mortgage customers cannot resolve their own questions (even for simple requests) but must depend on customer service staff.

Causes of Poor Customer Service

A mortgage can last 30 years. During this time, the firm that owns the mortgage may change several times and the servicing of the mortgage may be separated from its ownership. When mortgage servicing moves from one firm to the next the servicing history of the mortgage has not always moved with it.

The complete history of a mortgage may include documents generated and maintained by dozens of systems from mortgage origination to check processing to securitization. Any of these systems may need to be accessed to respond to a complaint or query. Hundreds of different types of documents and thousands of variants of these document types may need to be reviewed. Sifting through all these data silos and document types to find the information needed to respond to a specific customer issue is a major and often labor-intensive task.

With such a complex environment, providing comprehensive first-class customer service will stretch even the best IT infrastructures. The relational technology-based approach generally used to aggregate data is poorly suited for mortgage servicing. In a relational environment, creating a consolidated data store begins with a common data model to describe and store the data. Relational approaches then requires ETL to transform data when it is moved data from the primary systems to the common repository. In an environment like the mortgage industry where there are thousands of document types with new documents and variants of documents constantly being generated, creating a common data model is a major endeavor. Even if a common model is finally created (not a given), when the underlying documents or their metadata change it is necessary to retool the ETL.

For a more in-depth discussion on these issues see How to Drive the Mortgage Industry Forward.

Improving Customer Service

To efficiently and accurately handle customer service, firms need a universal document or metadata repository.

  • A universal repository pulls together all the information relating to the firm’s mortgages into a single location.
  • It allows the diverse data making up the mortgage process to be accessed as a unified whole.
  • A universal repository must be secure as it will handle critical customer information.

For more details on the mechanics of building a universal repository see Building a Universal Mortgage Repository. And if you are concerned about security in such a repository see Enterprise Ready Mortgage Document & Metadata Repositories.

Let’s dig a little deeper into each of the above bullets.

Pulling Documents Together

For firms in the mortgage industry, documents are often spread out over possibly dozens of systems. Gathering the appropriate documents together for an integrated view of a single account or for some other criteria can require performing multiple separate searches and queries against different systems and manually consolidating the results. When this kind of workflow is needed to answer even simple questions like: “show me all the documents for a specific account” it is difficult to allow customers to log onto a website and answer questions themselves except in for very narrow circumstances. This can be avoided by centrally storing references to the documents, metadata about them, and possibly even copies of the documents in a universal repository. Instead of multiple searches against siloed data a single search or query can be conducted against the repository.

This substantially increases efficiency to the point of making it possible to expose the search and query functionality directly to customers and allowing them to research their own questions.

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Handling Diverse Data

When filtering through possibly billions of mortgage documents it can be necessary to filter on dates, balances, how long payments are overdue, property location, type of loan, and an almost inexhaustible variety of other criteria.

With most technologies, providing access to all these different data types and document types requires the use of multiple arcane search and query technologies. This makes it difficult for even skilled customer service representatives to respond to questions and handle issues. It makes it even harder to build an infrastructure where customers can handle their issues themselves.

Cost Efficiencies

Requiring customers to contact customer service representatives to handle common inquiries is obviously not cost effective. When IT systems require these representatives to engage in multiple searches and queries against different systems and then force them to consolidate the results themselves costs rise even more. When the complexity of the data and distribution systems mean that the result of the searches, queries, and consolidations often contain errors then the result can be a disaster in terms of angry customers and regulators.

A universal central repository can greatly alleviate these issues by bringing together all the customer information into a single location and making it easy to search and query, no matter where the data originated or what kind of data it is.

Wrapping Up

Improving customer service in the mortgage industry is crucial. Start by providing a central repository where all the information and documents relating to mortgages can be brought together — and quickly queried. You will keep costs down, keep customers happy — and avoid lawsuits and regulatory fines.

David Kaaret

David Kaaret has worked with major investment banks, mutual funds, and online brokerages for over 15 years in technical and sales roles.

He has helped clients design and build high performance and cutting edge database systems and provided guidance on issues including performance, optimal schema design, security, failover, messaging, and master data management.


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