Inforte Delivers Data Quality Solution For Wholesale Mortgage Lender

Company Background

Inforte's client is one of the largest, wholesale lenders in the U.S., offering an array of mortgage loan products to consumers through independent mortgage brokers nationwide. As a rapidly growing wholesale mortgage lender, our client constantly strives to develop a deeper relationship with their broker customers and subsequently increase customer loyalty in an industry where pricing has historically driven loyalty.

Business Issue

Inforte's client wanted to implement a customer-centric program that aligned its corporate strategy with its customer strategy. Inforte began with a Customer Intelligence Assessment and Data Warehouse Strategy project that outlined the client's data warehouse capabilities and established a roadmap for delivering an enterprise level data warehouse.

During the assessment, it was apparent that data quality would be one of the key barriers to a successful data warehouse implementation. While reviewing the client's various systems, Inforte identified the following data quality issues:

• Duplicate brokerages across its Sales and Loan applications, thus misrepresenting sales numbers and skewing sales per brokerage figures.
• No standardization of brokerages or contacts (individual brokers at the brokerages) resulting in duplicate data and incorrect linkage between loans and brokers.
• Proliferation of temporary brokerages (brokerages created on a one-off basis when the contact center agent could not find the brokerage in the loan system), thereby creating duplicates and unused data.
• Loan data being rejected from the loan servicing third party because of data entry errors in the loan creation/approval process.

Researching these issues further and tying "numbers" to these, the results were stunning. Inforte identified that:

• Approximately half of the brokerages found in the loan system were "temporary brokerages," meaning that the client's customer population (either prospects or customers) was significantly less than they originally thought.
• Approximately 25% of the loans in the loan system were tied to "temporary brokerages," which misrepresented sales statistics per brokerage since these loans were not associated with the correct brokerage. For example, pull through ratio (loans submitted versus loans funded) was off by 20-25% on average.
• Lack of trust between account executives and loan creators (contact center agents and account managers) because of continued data issues.
• Brokerages with whom they had already done business were getting "Prospect" mailings because these brokerages existed as both customers and prospects in the CRM application.
• Account executives were getting incorrect commissions because of assignment of temporary brokerages/brokers to loans and duplicate data.

The data quality issues stemmed from a variety of factors, including lack of:

• Data standards across various customer applications
• Enforcement of accurate data entry
• Business/technical processes that ensured data integrity throughout different applications

Inforte's Solution

Inforte developed a comprehensive data quality solution which outlined several options that the client could choose from to solve/limit its data quality problems, identifying the pros and cons of each. These options included:

• Making changes to the existing CRM and loan systems to prevent data entry and duplication errors (correcting the problem at the source).
• Cleansing the data as it was loaded into the data warehouse implementing a data cleansing tool.
• Exporting the data out of the data warehouse periodically and utilizing a third-party vendor to cleanse the data on an outsourced basis, then reload the cleansed data back into the data warehouse.

In addition, Inforte recommended business processes and practices that could immediately be adapted by the contact center agents to try and limit ongoing data quality issues from occurring.

One of the constraints the client faced was that it would have been very costly and resource intensive to make changes to the existing CRM and legacy loan systems. They needed a solution that would limit the amount of work that was required on the source systems, while still being able to provide accurate reports and metrics from the Enterprise Data Warehouse. The client decided on the outsourcing option, which meant little to no changes on the source systems but would require complex logic and ETL mappings to be developed in the data warehouse.

The business solution involved partnering with a third-party data cleansing vendor and identifying de-duplication and standardization rules. On a monthly basis, the client sends the vendor files containing brokerage and broker data. The vendor de-duplicates and standardizes the data and then sends it back to the client.

The technical solution involved creating the concept of "Master" brokerage and "Master" contacts, whereby loans would be tied to both the existing brokerage (which reflected the data from the loan system) and newly de-duplicated or "Master" brokerage identified by the vendor. In addition, ETL mappings were developed to accurately calculate sales numbers and campaign effectiveness at the "Master" level.

Business Benefits Delivered

The data quality solution allowed the client to better understand its customer base and more accurately assess the performance of its account executives.

Some of the key benefits of this solution include:

• Accurate representation of sales per brokerage and the value/impact of a brokerage on its bottom line.
• Accurate campaign metrics based on sales attributed to a campaign.

In addition, with the implementation of this data quality solution in the data warehouse, the client was able to utilize this "Master" concept in other projects including customer segmentation and understanding broker value (i.e. lifetime value of a brokerage/broker).