The Impact of Business Intelligence on the Mortgage Banking Sector

Posted by Elijah on August 26th, 2022

A BI solution combined data from various systems to provide analysis capabilities that allowed a mortgage banker to better understand its customers, markets, and risk when faced with systems that were unable to effectively track, alert, and analyse data.

In the past few years, the mortgage banking sector has experienced significant growth and change.

In the past two to three years, loan originations and refinancing volume reached an all-time high due to historically low interest rates. The legacy applications that handled the loan origination process were burdened by this astronomical growth. Technology investments mainly consisted of new applications that improved the operational procedures in loan origination in order to handle this growing transaction volume. While these investments helped mortgage companies run more smoothly and provided a slight competitive advantage, bigger problems remained that were largely ignored during the loan origination gold rush.

Another significant change was the heightened competition and lack of oversight brought on by new consumer mortgage shopping websites and Internet-based mortgage banks. mortgage business intelligence to the commoditization of basic goods and decreased profit margins in a historically lucrative industry. Customer acquisition campaigns were generally ineffective and lacked differentiation. Mortgage bankers had to rely on rising loan originations to make up for the massive servicing runoff caused by refinancing. The emphasis remained on raising loan originations as a means of raising revenues.

The Greater Obstacle

Although the emphasis on loan origination produced front-end operational advantages, the lack of systems and procedures that effectively track, alert, and analyse data is the bigger problem facing lending institutions today. The asset management, finance, and servicing functions must manage the vast amounts of data produced by the loan origination process. The majority of mortgage banking operations, including risk management, portfolio analysis, identification and quantification of trends in delinquency rates, and compliance metrics tracking, depend on key management reporting and data analysis capabilities. Additionally, these functional areas are in charge of fulfilling the disclosure demands of regulators, rating agencies, and investors.

In addition, a lot of banks still carry out a lot of tasks semi-manually.

For instance, banks extract data from various systems, frequently located in various locations or on different PCs, to produce data for monthly servicing valuations, hedging, tax, management reporting, and forecasting. Following this time-consuming process, the data is manually combined in spreadsheets, requiring additional time and resources and increasing the risk of human error. When reports are finally put together, they only offer a brief, inaccurate snapshot of the situation that is weeks behind. Overall, these inefficiencies raise the mortgage company's operating expenses, risk, and potential losses.

Lack of a cross-LOB view 

The customer to calculate total customer value is another significant issue in large financial institutions and mortgage banks with multiple lines of business (LOB). In this case, each LOB treats the customer as a distinct entity, which causes duplication and unnecessary movement. Additionally, because there isn't a cross-LOB analysis of customer value, every customer receives the same treatment. No distinction is made between a customer's high one-time or short-term value to the business and their high long-term value.

Business intelligence holds the key to these issues being resolved (BI).

Simply put, BI provides analysis capabilities and integrates data from various data sources to help users better understand customers, markets, and risk while also giving them more visibility into business operations. BI is a technology that combines various corporate data sources into a single resource, uses it as a single source of company truth, and uses it to help an organisation accomplish its strategic goals (see figure at end of story). The information is analysed and presented in a way that supports tactical and strategic choices that have an effect on revenues and profitability.

Across the Gap

The cost of finding, gathering, and processing data is decreased by a BI solution's single point of access. It guarantees that key executives and operational managers are basing decisions on information that is factual and has been vetted using established business rules. BI can be used to analyse loan portfolios to identify customer buy zones, or the demographics of the customer sweet spot and similarities among potential defaulters, as well as to analyse the geographic distribution of mortgaged properties to better understand concentration of credit and pre-payment risk based on differences in regional economies.

Wells Fargo is a good illustration. Information Week recently wrote about.

how Wells Fargo Home Mortgage used BI to forecast the performance of both its portfolio of mortgage loans and individual loans. The BI system generated a number of quantitative advantages. First, Wells Fargo predicted that its loan default rates would be half of those predicted by Moody's and Standard & Poor's. Wells Fargo was able to renegotiate loans, take on more risk, and reduce interest costs by 0,000 thanks to this information. The bank was able to partner with mortgage insurance companies thanks to the more precise predictions. An annual revenue of almost million was produced by these partnerships.