How Data and Analytics are causing Mortgage Disruption?

By inflooens on November 27, 2020

how data and analytics are causing mortgage disruption


Need information at your fingertips about a mortgage? Data Analytics helps you in getting real-time information. For providing mortgage information instantly, Automation and Big Data play a vital role in the systems of lenders and Mortgage firms. We have discussed how Mortgage Process Automation is transforming the Mortgage Lifecycle in a previous blog. In this blog, we will discuss how Data and Analytics are causing Mortgage Disruption.

Data Science and the Mortgage Industry

Data science is useful in the mortgage industry in different areas. The areas such as loan origination, loan underwriting, loan insurance are under the purview of data science. Banking and FinTech companies are actively using datato derive business insights and improve scalability. The mortgage industry generates a large amount of data, but not everyone is using important Data wisely. According to Ellie Mae’s Data Survey, 24% of lenders are using Data to predict trends that affect their business. Data and analytics tools are providing benefits to the mortgage industry by utilizing data to get insights.

data science and mortgage industry

Figure 1: Data science and Mortgage Industry Data Source:

Mortgage Companies and lending firms can forecast the financial aspects of the mortgage industry by using Data analytics.

Data and Analytics -cause of Mortgage Disruption

Data analytics is helping to provide personalized services to customers. It provides Robo-advisor services to customers to help them in their investments. Machine language algorithms are offering chatbots on lender’s sites to address customer’s needs and inquiries. Big Data and Analytics is providing insights into a competition among lenders. It is predicting market trends and mitigating risks.

Let us see how Data and Analytics are changing the Mortgage Industry in the following ways.

  • 1. Faster processing of Mortgage applications

    Big Data from an applicant’s bank records, publicly available databases, social media sites,and third-party databases is collected. This information is pre-entered into the mortgage application which the applicant checks for accuracy. Alternatively, a customer fills a mortgage application. It is checked by Big Data software with data publicly available for its discrepancies.Computer algorithms will approve or reject these mortgage applications instantly, reducing the processing time. The use of manual evaluation of applications is outdated. faster processing time of applications

    Figure 2: Faster processing time of applications Data Source:
  • 2. Understanding Borrowers segments

    Lenders can get insights into borrower’s credit scores, DTI, LTV, property type, income tax returns, and more. With this information, lenders can segment the borrowers and market the products that are best suited for their needs. By segmenting delinquent borrowers, data analytics is helping predict the risk of loan delinquency and recommend collection strategies for on-time payments.
  • 3. Detecting Fraud and Mitigating Risks

    Lenders, FinTech vendors can distinguish fraud by using Big Data Analytics. The technology can weed out questionable transactions within seconds and mitigate risks. Thus, saving time and costs to detect fraud instead ofrelying on legacy systems. It helps in identifying thefts, false identities, and wrong insurance claims. Mortgage bankers are using analytics against a variety of datasets for various products, reducing risks. The use of data and analytics have improved risk monitoring and better risk assessment of portfolios.
  • 4. Predictive analysis

    Predictive analytics helps lenders or organizations forecast trends, improve decisions by capturing data from databases, industry data, or data from third parties. Analytics helps lenders understand the behavior trends of borrowers, their purchase history. It helps in predicting the next-action that borrowers to get the desired results.It also helps in identifying borrowers that are willing to switch to other lenders and helps in retaining borrowers. The process of Predictive analytics involves data collection from multiple sources, analyzing it, and modeling it to get useful information. Further, testing the data with statistical models, creating predictive models, and deploy it to get results or responses. predictive analytics process flow

    Figure 3: Predictive Analytics process flow Infloeens’ Mortgage CRM predicts trends to allocate resources giving maximum profitability. It uses Big Data efficiently by using targeted and personalized marketing techniques for lead generation.
  • 5. Regulatory compliance

    Data and analytics help lenders remain up-to-date with new regulations. With the use of a streamlined review, there is a reduction of process costs and the elimination of common mistakes. To comply with federal and state regulations, Checklists and quality-controlled processes are in place. It assures compliance with the SAFE Act and OFAC compliance.Anadvantage of incorporating Data and analytics tools is that lenders or mortgage firms can do civil/criminal background checks of third-party business partners. Inflooens’ interactive Platform is bringing transparency to the Mortgage Process. It allows users to gather data, answer user’s queries, create and implement machine-learning models.


Inflooens' Mortgage LOS allows Mortgage loan officers to originate and close loans with real-time data analytics. Lenders or Mortgage Loan officers can contact to take advantage of Big Data Analytics and grow their business.

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