The mortgage origination and servicing processes include hundreds of touchpoints with borrowers and a treasure trove of data in each transaction. When combined with aggregate data across the consumer lending industries, mortgage professionals have access to information that could transform how they operate.
Historically, this data was largely invisible to lenders or, at best, was presented in limited and standardized reports that left them with more questions than answers. Today, however, the move toward data democratization is transforming the landscape and getting more data into the hands of decisionmakers throughout lending organizations.
“When data is the guide, rather than best guesses, servicers can initiate changes to processes that improve and simplify the payment journey.”
With the increase of data warehouses, where information can be retrieved and analyzed easily, mortgage companies are gaining access to varying sources of data in the same location. When things like payment data and user behavior data are combined, shared and analyzed, lenders can be empowered to make key decisions that could improve their operational model.
At the same time, the evolution of artificial intelligence (AI) is making real-time, in-depth analysis of large swaths of data possible, allowing lenders to glean and act on insights that directly benefit business performance. Mortgage originators will want to know how lenders and servicers are using this information and what it could mean for them.
Forecast trends
Data analysis can identify points in the homeowner journey that can be adjusted, augmented or expanded to help mortgage borrowers pay on time through self-service channels. For instance, payment data can reveal common points where borrowers fall out of the funnel.
If a servicer’s data indicates that a significant number of clients run into login problems when they try to pay their bill, you can reach out to them with personalized links or QR codes that take them directly to a unique payment screen. This eliminates the need to remember passwords or account numbers.
Additionally, a payments provider can aggregate industry data to help forecast trends in the types of payments that homeowners prefer to use to pay their mortgage. This can prompt servicers to accept new payment types — like mobile wallet transactions — that may appeal to existing and future borrowers.
When data is the guide, rather than best guesses, servicers can initiate changes to processes that improve and simplify the payment journey. This can remove any friction that could potentially hamper on-time payment.
Resolve problems
Servicers can analyze data to find places where making small incremental improvements can drive operational improvements and efficiencies. One example is data related to engagement messaging.
Servicers can examine click-through rate data for messaging categorized by method (email, text or push notification) or timing (hour of the day, day of the week or day of the month) to determine the most efficient and effective way to use communications to drive on-time payments. You can even break this into more specific categories. Servicers may discover, for example, that higher-income borrowers respond best to engagement messaging one week before the payment due date, whereas lower-income borrowers pay attention to messaging on the 15th and 30th of each month when they get their paychecks.
They can also tap into data to help them make significant strides in driving self-service loan payments. Analysis can reveal how a company’s self-service rates stack up against the industry as a whole. If a servicer is well below the industry average, the company can work with its payments provider to implement tools that encourage self-service. These may include payment reminders with personalized links to take customers right to their payments screens, or interactive voice prompts to redirect callers to make a payment via self-service channels.
As AI gets more sophisticated, data is also being used as a tool to drive self-service collections that are personalized to borrower circumstances. For instance, an AI algorithm can create customized strategies for accounts that are past due. For clients who have been proven to need more assertive prompts to pay, the AI algorithm may initiate an automated workflow that messages the borrower, suggests an adapted payment plan and walks them through steps to make a payment.
This customized approach to self-service earns points with clients who prefer not to call their lender to make a payment or resolve an issue. Moreover, it can achieve considerable savings when you consider that the average customer service call costs a company $8, compared to 10 cents for self-service payment methods, according to Gartner. Self-service payments also free up a customer service team to focus on more complex issues, which is a better use of their time.
Course corrections
When a payments provider distributes regularly scheduled data reports, a mortgage servicer will be able to notice immediately when performance targets are off course, so they can pivot quickly and implement new strategies in response. For example, suppose these data points reveal rising auto-pay failures within the industry.
In this case, an AI-directed workflow could be automatically initiated to send alternative payment suggestions as soon as the issue is detected. The payments provider can also use AI to flag fraudulent payment behavior that is impacting one lender and initiate a warning to all clients to implement precautionary measures.
Data-driven predictions can also guide larger strategic decisions, such as prompting a lender to invest more in origination efforts to compensate for an anticipated drop in revenue. In that case, applying AI to millions — or possibly billions — of data points over time and across the industry can help identify the ideal client profile, making sales efforts more focused and productive.
When a payments provider consolidates and analyzes payments data on a regular basis (and provides it in a digestible format), it can make reporting on key performance indicators more immediate, thorough and accurate. A company can see where it’s on track to meet goals and where it will need to make adjustments to get the desired results. Payments data can also be distributed beyond the silos of accounting and finance to inform decisionmaking and improve revenue generation across the organization — in sales, marketing and beyond.
● ● ●
Data can become an accessible and tangible tool in your company’s arsenal thanks to AI, sophisticated analytics and the democratization of data. A payments provider with a data warehouse backed by strong protections and standards can get lenders started.
The provider will centralize and scrub the payment and user behavior data so it’s ready for analysis, and it will provide aggregate data for use in servicing. The lender will gain new insights to improve the client journey, new tools to drive operational efficiencies, and new evidence to assist with decisionmaking and reporting. ●
