Data trails have become an integral part of the modern consumer’s lifestyle. Every day, people leave traces of data on the internet, through bill payments, and even when making phone calls. 90% of the data present in the world today was produced in the last two years. For attentive lenders, these data trails can be a great lead generation tool.
Behind these sets of data sits information that can guide lenders into establishing the risk profiles of potential borrowers as well as unearth new business opportunities. The science lies in identifying the type of data on which to concentrate. The art is determining the kind of insights for which to look. Luckily, with the help of big data analytics tools, machine learning, and the right resources, it can be easy to use such data to revolutionize lead generation and customer retention in the mortgage industry.
Here is how big data can revolutionize lead generation in the mortgage industry:
Building the Right First Impression
The customer journey that lenders take potential customers through will have a significant impact on their final decision. Nowadays, digital properties have been playing a pivotal role when it comes to interacting with potential customers, as well as presenting the nitty-gritty details of loan offerings to them. In many cases, the customer’s experience with the company might start with a personalized marketing campaign that drives a prospect to a lender’s website.
If the experience raises some red flags or seems tedious to them, then the chances are that they will look for another business with which to work. For instance, asking customers several random questions only to offer them generic loans might put off some customers. With big data, businesses can analyze both internal and third-party data to come up with a consumer journey that creates the right impression off the bat. The data collected during this experience can also translate into how lenders handle customers throughout the lifetime of their loans, increasing customer retention rates.
It is quite easy for people with thin credit files to be judged using generic credit scores. In many cases, these people could easily manage to borrow and pay back more than what lenders offer them. Big data can provide insights into the risk profiles of customers who haven’t tapped into enough credit throughout their life. For instance, a good number of millennials might not use credit cards, take out car loans, or even work as salaried employees. This generational behavior makes it unfair to judge such mortgage leads under the generic mortgage models.
However, these people do pay phone bills, own bank accounts, and use a mobile payment app. All of these pieces of data can be significant indicators of their risk profile. This information can produce a more thorough profile that can also apply to underserved communities that lack definitive credit histories.
The mortgage industry is among the most fraud-targeted sectors of the economy. While lenders want to limit fraud as much as possible, they neither want to lose legitimate business nor run afoul with regulators for making aggressive rejections to loan applicants. Luckily, big data analytics can offer the balance for which lenders are looking.
Ideally, big data helps lenders, third-party data suppliers, and FinTech vendors to move past conventional fraud detection methods. These methods involved manual fraud detection processes and siloed data. Proper analysis of big data can limit the number of false positives in fraud detection and identify questionable transactions as soon as they are made. Artificial intelligence can help score the risk profiles of the different transactions against a number of variables. Although these analytics can reduce the cost of relying on conventional detections strategies, they require a complete change in how managers approach risk management.
Other than controlling costs and improving profit margins, the efficiency at which lenders can handle a loan throughout its entire life will have a significant role to play in how they generate mortgage leads and improve their customer retention rates. Data analytics can have a vital role to play in improving the entire loan application process, enhancing the customer onboarding process, and speeding up loan underwriting. With big data analytics and the consent of the customer, lenders can gain access to consumer data from third-party data providers. These data sources can include banks, employers, and credit bureaus- allowing them to form a better picture of the financials of their mortgage leads.
Machine learning can also be pivotal in preventing last-minute delays in the loan application process by flagging suspicious data points. For instance, if the suspicious activity is that the borrower had made large withdrawals or deposits into their bank account, the system will pick up on this and allow the underwriter or processor to request clarification. The customer can then send their feedback through the analytics application, making it easy to analyze their inherent credit risk.
With this better organized, more comprehensive, and easily searchable data, lenders can rely on the data points to provide high-quality customer credit files. Other than making the underwriting process smooth, these files can provide insights throughout the lifetime of the loan, offering ideas that can improve the experience of a borrower. Lenders can identify ways to improve their loan offerings, respond to customer feedback, and help customers out of tricky situations, all of which can improve their chances of them turning into repeat customers.
Big Data to Generate Mortgage Leads
Big data improves the scope and quality of insights drawn from borrowers’ data. With more emphasis on the analysis of data, lenders can both improve the experience they offer current customers and extend their services beyond the typical client base through the generation of quality mortgage leads. The onus is upon lenders to embrace big data analytics to be part of this remarkable revolution.
Reach out to our team at Insight Financial Marketing today to learn how your business can get started using big data to generate mortgage leads in a way that optimizes the engagement with each customer.
Rob Reale is an Associate Partner and National Sales Manager responsible for business development and sales at Insight Financial Marketing. Rob began working in the Mortgage Banking industry in 1990 and currently helps the financial service industry leverage unique and innovative solutions.