Mortgage customer analytics helps mortgage companies manage workflows and automate regulatory processes. However, if loan officers aren’t equipped with the tools and training needed to use the data they collect, results will fall short.
What is data analytics for mortgage companies?
Data analytics is the process of analyzing data to answer common questions and find trending terms, features, or characteristics. This process requires large amounts of raw data to answer questions and discover trends.
Like data analytics in general, mortgage analytics is all about examining data and drawing conclusions about the lending process and borrowers. Through this process, companies can identify customer questions, mortgage professionals’ pain points, and potential compliance issues.
Mortgage companies have access to large amounts of raw data that can be leveraged to improve the mortgage experience. Here are just a few examples of mortgage customer data:
- Bank statements
- Credit card statements
- Loan files
- Credit scores
- Tax returns
How loan officers can use analytics to improve customer retention.
Enhanced customer experience features start with mortgage customer analytics. These analytics help businesses identify roadblocks to customer retention.
One major roadblock to customer retention is a lack of customer understanding. Mortgage lending professionals can use customer data to identify behaviors that can predict whether customers are preparing to purchase another home or considering refinancing their home.
Customer analytics can monitor the current state of a customer. This data can be used to identify or predict when the customer is requesting information about a new loan. Like submitting a rate inquiry, key triggers may be buried in a mountain of customer data without the right analytics in place.
Once a customer requests information for another mortgage or refinancing option, a loan officer needs to provide relevant information quickly. Customer analytics can help identify customers who may be looking for a new loan and recommend the right content for that particular customer. From in-depth articles to engaging videos, customer analytics can track the success of these materials with specific demographics.
Detecting fraud in the mortgage industry.
Understanding the data from borrower analytics can help businesses identify potential fraud. A mortgage company has significant amounts of financial data to monitor, so a manual fraud detection strategy may not be thorough enough to spot fraudulent activity.
According to the United States Sentencing Commission, mortgage fraud being committed has decreased by approximately 86% from 2016 to 2020. However, that doesn’t mean fraud has been completely eliminated.
Falsified borrower data, like employment status, appraisal figures, or credit scores, can be dangerous for a mortgage company. Customer analytics helps synchronize data entering in various forms to ensure accurate reporting from customers.
Ensure regulatory compliance during the lending process.
The Truth in Lending Act, Equal Credit Opportunity Act and other regulatory acts protect consumers from unfair lending practices. The burden of proof falls on lenders to ensure the entire lending process is compliant with these and other regulations.
Customer analytics gathers information on loan-specific and company-wide levels. This data can be used to alert a lender to any cases of out-of-compliance activities, like denying a loan to an individual based on gender, sex, race or color, or other out-of-compliance issues.
An out-of-compliance decision made by a mortgage company doesn’t have to be intentional to lead to a fine. Customer analytics can review documents for unintentional loan denials or other decisions.
Customer analytics can also be used to create an accurate and current applicant scorecard. This helps lenders create an auditable record of all communication and decisions, as well as the required appraisals and other written valuations necessary for compliance under the Dodd-Frank Act.
Leverage mortgage customer analytics with Capacity.
AI-powered support tools use customer analytics to calculate the risk of mortgage fraud. Mortgage companies can utilize public records and other data sources to verify identities and borrower data by automating fraud detection systems. The latest support automation platforms can create models that help identify fraudulent activity earlier, thanks to machine learning.
Companies can tailor AI-powered fraud detection to their own risk tolerance level for personalized protection. The result is fewer manual reviews, faster fraud detection, and less investment in the pre-screening process.
Support automation platforms review income statements, automate paperwork, and verify the information submitted matches other submitted paperwork. Creating a step-by-step workflow, automating steps, and adding information to an AI knowledge base can help prevent loan officers from accepting falsified documents.