Insights

What is the importance of data analytics in the mortgage industry?

February 22, 2022
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Certified Credit

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Data is everything … if it’s translated into actionable insights. Enter the field of data analytics. Breaking down and manipulating big data can have huge payoffs for both lenders and consumers. Our readers explained how below.

David Proctor

David Proctor

David Proctor, Senior Database Manager at Everconnect Remote Database Support.

Holistic Customer Profile

Data analytics is essential to the mortgage industry because it gives lenders a holistic look at a customer. The mortgage sector uses data tools to gather information from across the Internet including payment history, social media posts, etc.

All of that data is used to create a picture of a customer. This image informs them of the customer’s behavior, underlying habits, and the highly probable outcomes of giving them a loan or another service. This means that workers can make more informed, customer-based decisions that guarantee increased profit and security for the business.

Data analytics is also helpful for detecting potential threats for companies (mainly fraud). In these cases, analytics tools gather the information that can be used to identify and authenticate customers.

Corey Tyner

Corey Tyner

Corey Tyner, Founder, and President of Buy Yo Dirt.

Customer Retention, Detecting Fraud, and Predicting Creditworthiness

Customer Retention
Lenders can gain a better understanding of their customers by using data analytics.

Lenders can better propose services that may interest a customer by combining external data sources with internal customer information. Analytics can help you figure out which customers are at risk of defaulting and interested in extra mortgage services.

Mortgage lenders can boost client retention and reduce losses by analyzing purchasing behavior, social media profiles, job history, and other facts about their customers. Lenders can get a complete picture of a customer by supplementing existing data with external sources without putting any new demands on the customer’s time.

Detecting Fraud
Lenders use artificial intelligence (AI) and machine learning to improve fraud detection, compliance reporting, and risk management. Outlier data items can be flagged for further study and follow-up using analytics. Instead of wasting time on randomized checks, this type of targeted management-by-exception method saves time by allowing qualified staff to focus on cases most likely to represent an actual risk.

Pattern recognition is a field where AI and machine learning excel. As an enormous amount of historical data becomes available, those capacities will improve.

These are just a few of the mortgage industry’s many use cases for big data analytics.

Predicting Creditworthiness
In the mortgage market, there are numerous chances to use data analytics. Many industry experts have noticed that determining creditworthiness is more complicated than it used to be. Because millennials are less likely to have credit cards or vehicle loans, they are less likely to have a long credit history. Many people in the new “gig economy” are also self-employed.

This is reflected by the recent rise of non-qualified mortgages (“non-QM” mortgages). According to Moody, residential mortgage-backed security issuances for non-QM loans climbed from $570 million in 2016 to more than $25 billion in 2021. Because many non-QM consumers lack a traditional credit history, mortgage lenders turn to new ways to determine trustworthiness. Big data and data analytics can assist in filling that void.

Callum Dodsley

Callum Dodsley

Callum Dodsley, from Kingdom Mortgages.

Unique Market Focus

Data allows us to focus on particular markets and learn their behavioral patterns (when our target audience is looking to buy, whether they are looking to buy). It can also prove crucial in client retention as predictive analysis can showcase clients most likely to move firms. This is particularly useful when looking at first-time buyers as this is so competitive and knowing when they are likely going to invest and where is so useful.

Zac Houghton

Zac Houghton

Zac Houghton, CEO at Loftera.

Data Drives Understanding, Improved Performance

A data analytics tool makes segmenting customers easier for banks and lending companies. This type of information allows lenders to determine a customer’s financial situation, their spending patterns, and their credit preference. When a lender becomes more data-driven, it gains a better understanding of the consumer.

By analyzing data, businesses can optimize their performance. Businesses can reduce costs by identifying more efficient ways to conduct business by incorporating it into their business models.

It is possible that loans are not offered the same way to every customer because each one has a unique set of characteristics. A completely customized offer will optimize loan allocation and pricing for mortgage businesses. You can customize your loan amount, tenure, and interest rate. Borrowers with good credit histories and stable income will also be offered a low-interest loan. By analyzing data in real-time, lenders can create customized loans. This increases conversion rates dramatically as well.

Kathleen Ahmmed

Kathleen Ahmmed

Kathleen Ahmmed, Co-founder of USCarJunker.

Complex Creditworthiness

Leveraging big data and analytics within the mortgage industry has become extremely vital because these days, assessing creditworthiness has become significantly more complicated than it once was. For instance, many millennials don’t have a well-established credit history because few of them have credit cards or car loans. Plus, in this new “gig economy,” many of them are self-employed.

As a result, many mortgage companies now require alternative ways to properly assess creditworthiness. And luckily, through big data and predictive analytics, they can use these systems to access a wide range of internal and publicly available data, ranging from physical addresses to social media data to email accounts to phone numbers and more, to identify any hidden patterns within loan applications that may point to fraudulent information or activity.

Daniel Osman

Daniel Osman

Daniel Osman is Head of Sales at Balance Homes.

More Control over the Metrics

Data analytics are the foundation of whether or not a mortgage is approved. While approval used to be up to individual banks, the ability to approve mortgages has been largely stripped away from a purely human perspective.

This gives a good opportunity to those seeking a mortgage to improve their chances to be approved. Knowing that a bank’s analytics programs tend to favor a higher credit score, for instance, allows potential borrowers to focus on simple, effective tricks that provide an outsized benefit to their chances.

John Fordice

John Fordice

John Fordice, Analytics Lead at Bonsai.

Fraud Detection, Customer Profiles, and Targeted Marketing

Data is everywhere and all organizations have to make the best use of it. Data analytics services can be used to decide if credit should be authorized.

Fraud detection
The third-party customer data such as the social media presence, government IDs, etc., will assist in the confirmation of the customer’s credibility.

For a better understanding of the customers
Data analytics can be used to understand customers. The customers are sorted out into micro-segments to reach quicker decisions on the loan approval.

Targeted Marketing
By using data analytical services, mortgage companies can select their customers easily. You can also get customers’ acumen. The mortgage organization can directly market its pitch to the right customers.

Samantha Odo

Samantha Odo

Samantha Odo, Licenced Real Estate Expert at Precondo.

Better Understanding of Borrower’s Status

Data for any field is available to companies in abundance. Data processing is necessary for companies as it allows them to gain valuable insights from their data. Analyzing that processed data proves beneficial for every industry. The mortgage industry is just another industry that requires data analytics to segment and understand customers. A data-driven mortgage lender understands the borrowers’ financial status, credit preferences, and spending patterns. It also enables lenders to understand the ability of the borrower to pay back the loan.

Martin Orefice

Martin Orefice

Martin Orefice, CEO, Rent To Own Labs.

Getting the Best Mortgage Offer

Analytics works best on big data sets, and the mortgage industry offers a huge set of homes for sale, potential buyers, and existing mortgages to crunch the numbers on. All of this information is really in the hands of lenders, but the power of it works to everyone’s advantage. When lenders use analytics, they end up more accurately appraising homes, judging the creditworthiness of borrowers, and pinpointing the best competitive rates they can offer to get business. All of this works out in favor of the lenders and the borrowers. Nobody wants to get a mortgage they can’t afford, and analytics does a lot to prevent that.

Christian Velitchkov

Christian Velitchkov

Christian Velitchkov, Co-Founder of Twiz LLC.

Determining the Probability of Delinquency

Selecting customers
Data analytics help companies to find their right customers. They enable their leaders to get insights into customers’ financial backgrounds, spending patterns, and credit preferences. By using this data, the mortgage industries can contact the right customers without wasting any time.

Determining the probability of delinquency
Sometimes some customers might pose as perfect customers to get a loan. Here, data analytics help mortgage companies by estimating the probability of delinquency. The prediction is based on previous transactions and loans. It also includes how many times the borrower has not paid the full amount earlier.

Understanding their customers better
Mortgage companies use the data provided by data analytics to understand their customer better and target the right audience.

Christopher Sioco

Christopher Sioco

Christopher Sioco, Chief Operations Officer at Tax Robot.

Preventing Fraud

Analysis of customers helps the companies understand the person’s financial status, spending habits, credit details, and much more. This can show the company the person’s entire financial history.

Frauds are also caught by studying the data. An in-depth analysis might bring out something that seems fishy. Alongside the government documents, etc., even the financial details can validate the person’s authenticity. Hence, data analysis is needed to verify the genuineness.