Self-Service Data Prep Provides Business InsightBy Guest Author Print
QIE Partners, an investor accounting service provider, achieves precision loan reconciliation and meets clients’ expectations with self-service data preparation.
By Dirk Schulze
QIE Partners provides investor accounting services to mortgage companies, credit unions, servicing entities and banks. We handle the investor reporting and bank reconciliations for all remittance types of Fannie Mae, Freddie Mac and Ginnie Mae loans, as well as for private investor portfolios.
Since our clients trust us with vital financial information, we must ensure its accuracy and protect it, as well as transform large volumes of information into actionable intelligence that can drive their business forward. Although a variety of analytics tools are available, we’ve found that it would be impossible to do our jobs without self-service data preparation.
Often, the data that provides the most analytical value is locked away in multistructured, semistructured and unstructured documents, such as text reports, PDFs and log files. Also, source data is diverse and rarely presents itself in an analysis-ready format. Historically, the only way for analysts and auditors to access and use this information was to manually rekey and reconcile the data—a time-intensive and error-prone processes.
The combination of these challenges forces many analysts to spend more time gathering and preparing data (up to 80 percent of their time, research has found) than they do analyzing it. That's an unfortunate reality that incurs costs and causes delays in decision making.
Every month, our clients provide various loan reports that our staff manipulates, reconciles and runs calculations on for third-party investors. The reports show details such as balances, loan status, payment collections and projected remittances due. Most of the reports come in as static PDF, text and comma-separated values (CSV) files that often don’t lay out cleanly. Even if the incoming data is in decent shape, each servicing system has its own unique report formatting and challenges.
Adding to the complexity, our clients’ portfolios contain anywhere from 250 to 130,000 loans. We also work with many investors and different types of portfolios, each of which can have different reporting and reconciliation requirements, as well as different ways to calculate remittances due.
Bank reconciliations can be performed with traditional mortgage servicing reports, but using static reports can make it difficult to balance with precision, and our clients depend on us for accuracy in monthly investor reporting and bank reconciliations. We can’t deliver the quality and precision they require if we handle data retrieval and prep processes manually.
Regardless of the preventive measures put in place, manual rekeying of data results in a margin of error. And business decisions made with incorrect data can have a negative impact on the bottom line.
Accuracy aside, we also don’t have the time or staff to devote to manual data reconciliation. Without a tool to automatically extract, manipulate and organize large volumes of data, we would not be able to meet our clients’ expectations.
Manipulate, Blend, Enrich and Prepare Data
We overcome the data challenges that plague so many other businesses with Datawatch’s Monarch self-service data prep solution. With Monarch, we can rapidly and easily mine data from nearly any type of report, and can quickly manipulate, blend, enrich and prepare it for the reporting and reconciliation tasks required by investors. A static picture of data is transformed into an interactive pool of information that we can harness for our clients’ needs.
With data prep, we can replicate processes that are applied to multiple clients, resulting in less time spent on training, as well as more capacity for reconciling and research. By automating the data retrieval and prep processes, we can flawlessly deliver the precision our clients demand.
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