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Using Big Data to Generate Mortgage Leads

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.

Better Assessments

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.

Detecting Fraud

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.

Increasing Efficiency

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.

 

A Quick Guide To Bank Deposit Growth

 

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Banking Tech – What is the new role of technology in banking?

A new age of technology exists, and things are moving very fast. Because of this, the banking sector has begun to utilize more of the modern technological advancements. Many of these advancements are leading banks to use data and artificial intelligence (AI) for higher levels of personalization and consumer fulfillment. These trends are only going to grow. Here is a look at the role new technology plays in modern banking.

Keeping Up With The Disrupters

While many in the banking sector were trying to maintain the status quo, disrupters turned up with digital banking and financial solutions utilizing modern banking tech and the mobile devices. These developments forced all banks to reevaluate and start to implement changes to how they service their customers. Now, most banks have applications that work fast and provide services people want their banks to provide.

Mobile technology is playing a significant role for banks, and fine tuning those digital offerings is something into which these banking institutions have put a lot of effort. Artificial intelligence (AI), big data, and predictive analytics are becoming the norm in the banking industry for these reasons.

Bringing Core Services Online

Along with the mobile push comes the push to put core banking services online in general. Banking portals of the past contained heavy limits and often did little to improve a customer’s experience.

Forward-thinking banks have started to utilize modern techniques to attract customers to their financial solutions, and allow customers to make banking decisions quickly and without hassle. Responsive design and a focus on turning technology towards customer service have created vast improvements for the banking industry.

Once again, a lot of this comes from investments in analyzing data and AI. This is especially true of online banking services that can help someone in real-time. By anticipating their questions, banks can provide automated and personalized solutions. Banking tech is assisting financial institutions to do more while saving money in the process.

Creating and Fostering Greater Levels of Personalization

People want to feel like their financial institutions know them, care about them, and are looking out for them. People interact with their banks and bank products all the time and with greater frequency. Each of these interactions is an opportunity for a bank to learn more about their customer.

For example, consider someone who regularly loads funds to their favorite coffee store mobile app every week for a year, from their bank account.  This transaction trend reveals one particular behavioral characteristic about the customer that, along with other data points, present your financial institution with information about the customer’s preferences and lifestyle.  Over time, as new transactions and transaction types are analyzed, and the historical amount of data available to analyze increases, new opportunities to cater to specific customer needs will be identified.

In the near future, a financial institution will be able to anticipate a customer’s needs and gain insight into what the bank can further do to retain that person and other people like them.  Advanced data analytic solutions make it easier for banks to identify trends and make individual suggestions for how to engage customers, that work on a more personal level.

AI, along with advanced data analytic capabilities, help banks learn more about their customers and how to take care of them on a personal level.  The fact that data analysis can create a more human experience for customers is one of those areas in which banks are finding a lot of value in, and is one area that will also create a healthy return on investment into the future.

Data Science and data analytics plays a crucial role for banks at every level and in every department. In this way, technology serves to unify many of the disparate banking departments so the bank can create a better, more secure, and personal experience for each of its customers. Financial Institutions who aren’t doing this, or planning to do this, will find themselves left behind. Reach out to our team at Insight Financial Marketing today to learn how your business can get started with the latest innovative solutions that will increase your bank’s ability to engage each customer with a more personalized experience.

 

A Quick Guide To Bank Deposit Growth

 

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