Big data is radically changing the business world due to the gold mine of consumer information now at the fingertips of most organizations. From social media posts to online shopping patterns, consumers are leaving a trail of data that smart businesses can utilize for important insights. The big-data scene is growing exponentially every year. How does this revolution impact the mortgage industry?
Borrowers are the backbone of the mortgage industry. With big data providing key insights, mortgage originators and lenders can improve risk management, the speed of the loan processing cycle, the reach of marketing campaigns and the efficiency of mortgage servicing. Besides the advantages, big data also comes with the responsibility of data management and regulatory compliance.
To understand how valuable data is in this age of information, take the case of five of the world’s biggest companies: Apple, Amazon, Alphabet (Google), Microsoft and Facebook. What these organizations have in common is they either deal in or have access to a large amount of data that they mine for important insights.
Using big-data analytics and technology, data is obtained, sorted and stored in a searchable format, which can then be used in the day-to-day running of the business. For the mortgage industry, data consists of third-party information (credit-card statements, cell-phone payment histories, credit scores and tax returns) and client records (brokerage accounts, bank statements and loan files).
If utilized properly, such information can be of unmatched value to an organization. Originators will want to know how big data has influenced the mortgage industry and some of the measures organizations can take to reap its full benefits.
Using big-data analytics and technology, data is obtained, sorted and stored in a searchable format, which can then be used in the day-to-day running of the business.
One challenge facing mortgage lenders is how to gauge the credit risk of individuals with thin credit files. Normally, lenders use a generic credit score based on information such as auto loans, employment salaries or credit-card use. Although this system may have worked before, today’s consumers have shown a trend of less credit-card usage, irregular employment and fewer loan accounts, making it difficult to develop a reliable credit profile.
Big data makes use of alternative information such as usage of mobile payment apps, bank accounts, phone bills and other available data to determine their credit risk. Data also can be used to build alternative profiles for applicants from communities that are underserved and therefore have no definite credit histories.
The initial interaction with a potential borrower will determine if they will use your services. Especially for digital platforms, clients are usually quick to abandon websites that ask them too many questions while failing to suggest appropriate products. In short, borrowers want lenders that can read their needs correctly and offer them the desired experience.
Drawing upon data from third parties and internal analytics, big data can help mortgage lenders. By analyzing data from previous and existing clients, as well as from third-party organizations, lenders can predict the needs and preferences of a client by simply asking them a few questions. If the prospect feels like their needs are understood in their initial interactions, they will be more likely to come back and mention you to friends or family.
The use of big-data analytics to handle the collection and processing of borrower data will increase efficiency and reduce operational costs while increasing overall revenue. Using artificial intelligence — specifically machine-learning algorithms — big data can be analyzed to help in the automatic processing of applications, speeding up the process of underwriting and onboarding clients.
Clients can consent to lenders accessing their third-party information such as data from banks, employers, credit bureaus and brokerages to aid in shortening the processing period. The use of big-data analytics and management models also enhances data integrity while preventing errors and delays, as would have been the case with manual processing. An efficient lender will have an automatic, fluid process that will help in controlling costs while simultaneously drawing more clients through great service.
Servicing is a challenging and important part of the mortgage business. After making the loan, following up with borrowers to ensure they repay is usually necessary. To control the servicing costs, lenders can use big-data analytics to help them predict and identify borrowers who are at risk of missing a future payment.
With this information, the lender can optimize its outreach efforts to focus on risky clients. The existing client data also can be supplemented using data about the payment status of other loans and credit-card balances to determine the most effective and cost-beneficial means of working with risky borrowers.
Among financial-services companies, the mortgage sector is the one that is most often targeted by fraudsters, calling for lenders and originators to stay on high alert. When focusing on security, there is the risk that lenders can become so aggressive or stringent that they scare away potential clients. The perfect balance can be found in using big-data analytics.
Big-data technology can help in minimizing the occurrence of false positives by using thousands of variables to differentiate questionable or suspicious transactions from legitimate activity. Unlike traditional measures that took too long to ascertain the validity of a transaction, big-data analytics takes only a short time, reducing the risk of losing legitimate clients due to errors.
Owing to the large number of government entities that lenders have to answer to, compliance management can be a nightmare for many organizations. This is because each of the different entities tasked with scrutinizing individual departments have their own data-presentation formats that fintech companies should adhere to.
Traditionally, regulatory compliance was managed through the documentation of each loan application as it was processed. This took time and resources, and still had the risk of errors that could be hazardous to the organization. With big data, models and systems can be integrated into different departments to facilitate the automatic processing of compliance-centered reports.
Compliance with data regulations such as the California Consumer Privacy Act and the European Union’s General Data Protection Regulation is important to financial organizations. This is not only because of the hefty fines resulting from noncompliance but also because these regulations ensure protection against data breaches. The process of data management is not limited to collection, analysis and use but also extends to protecting it from being accessed by unauthorized parties.
Data destruction is an important part of the management process as it is an additional step to ensure data protection. For their part in due diligence, lenders and originators should seek the services of data-destruction companies to handle data erasure, degaussing (demagnetizing a hard drive to erase data) and shredding of secure documents. These companies should offer video evidence and a certificate of destruction.
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The success of mortgage lenders and originators is dependent on the quality and reach of their client data, the processing and verification speed of the data, the technology used to approve or reject the loans, and how much they can meet the expectations of their clients. Therefore, mortgage companies should pay more attention to both internal and third-party data as it provides an avenue for constant growth in value. ●