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Help Your Customers Build Generational Wealth and Grow Opportunities for the Future

Interest rates continue to remain at record lows, and the recent employment numbers are showing improvement as we continue to recover from the COVID crisis.  Now that the economy is recovering, there are opportunities for financial institutions to help their customers get back on track for planning and preparing for the future.  Two of those areas include providing guidance and assistance with the planning and saving necessary to buy a home and providing the guidance, support, and services that will enable them to save for retirement.  

An article published by the US Census Bureau mentions that the most significant contributors to household wealth are home equity and retirement accounts, which accounted for 62.9% of households’ net worth.  Providing guidance and assistance in these two critical areas will help your customers build generational wealth and provide opportunities for themselves and their families in the future. 

 

 

Insight Financial Marketing® (IFM®) has an industry-leading solution that provides deep insight into customer behavior and financial activity.  Using IFM’s advanced data analytics, your financial institution can identify key characteristics about your customers in near real-time and identify the segments likely to be more receptive to specific messaging in these critical areas.  

IFM offers event-based alerts and marketing campaign files that allow you to message your customers at the right time so you can provide them with the guidance and support they need to build wealth via homeownership, investing, and saving for retirement.  

To learn more about IFM’s services, you can visit our website or communicate with me directly via email or voice at rreale@infimark.com and 703-856-4060.

 

 

ACH Volume Soars At Record Pace

Wow!! Have you seen the new information that the National Automated Clearing House Association (NACHA) has posted about ACH transaction volume during the second quarter of 2021?  Volume soared at a record pace

According to the post, the quarterly volume of 7.3 billion was an increase of 9.9% from the same period in 2020. The value of those payments was $18.4 trillion, reflecting a nearly 25% increase from a year earlier. Additionally, during the first quarter, a “new record was set in February when ACH volume averaged more than 118 million payments per day, the ACH Network’s highest daily average for a month. The second was in March, when ACH volume hit 2.7 billion payments, the largest monthly volume in ACH Network history. This included approximately 110 million economic impact payments by Direct Deposit from the federal government.”

 

In March, ACH volume hit 2.7 billion payments, the largest monthly volume in ACH Network history.

 

Clearly, these numbers show that the COVID pandemic has accelerated a change in behavior

amongst consumers and businesses to adopt electronic payments and digital money movement.  Drilling down further into the Q2 numbers, we see that P2P transactions increased by 24.2% from the year-ago quarter, B2B activity increased by 28.7% and internet transactions increased by 14.3%. 

These volume numbers indicate that the rapid growth of transactions within the ACH network has not only continued to grow but are now growing at a record pace.

Question:

Why are ACH transaction volumes increasing so quickly and how will financial institutions adapt to this change in behavior from a customer intelligence perspective?

Answers: 

More and more consumers are choosing to conduct their lives online and in the mobile space. This includes entertainment, travel, paying bills, sending money to family, friends or businesses, shopping, buying digital currency, being paid by Direct Deposit, and investing. Businesses are also moving away from paper checks and adopting digital payments. From a financial institution perspective, firms will be challenged as their customers will be expecting that when moments that matter occur, their financial institution will be there for them, in near real-time with the right guidance and the financial products and services that meet their needs.

Customer Expectations Are Changing To Near Real-Time

Customers will be expecting that when moments that matter occur, their financial institution will be there for them with the proper guidance, financial products, and financial services that meet their needs.

Insight Financial Marketing has an industry-leading solution that will enable your financial institution to better understand consumer and business behavior in near real-time.  We have a proprietary analytical approach that identifies pattern changes, life events, and lifestyle changes that will help your FI to deepen relationships with your consumer and business customers. To find out how IFM can help your team better engage with your customers, visit us at our website, Infimark.com, or contact Rob Reale at rreale@infimark.com.  

 

The past year has brought many changes to the Financial Services Industry and so far in 2021 the indications are there is more to come. Mergers and acquisitions within the top 50 largest banks in the U.S. are reshaping the landscape, changes at the periphery of the industry are also having an impact.  Several of these developments include Lending Club’s acquisition of Radius Bank, Square receiving an industrial bank charter, the growth of SoFI and Cryptocurrency (One Bitcoin > $50,000), as well as usage increases of retail merchant apps (i.e. Starbucks and Chick-Fil-a), and digital signature technology.  Additionally, the payments environment is also shifting as ACH transaction volume surges and P2P transaction volume continues to increase with more usage of PayPal, Venmo, Square’s Cash App, Apple Pay, Apple Cash and the Apple Card, and Zelle (btw… JPMorgan has ended its ChasePay service).

How will your Financial Institution adapt to these changes?

 

These changes indicate that your FI’s customers are being enticed to move an ever-increasing portion of their financial relationships away from your institution.  What steps can you take to mitigate the depletion of deposits, loans, credit, and payments?  One critical step will be to identify when to communicate with your customers and with what message.  Using advanced data analytics that can shed light on customer behavior changes and provide customer insights are critically important.  Being able to predict future needs, and delivering timely offers that provide solutions, will give your institution an early ability to retain and grow relationships with your consumer and business customers.

Technology is available today to protect your tomorrow.

 

IFM’s industry-leading solution can help alleviate your institution from flying blind when it comes to understanding current and future trends and identifying opportunities to deepen engagement with your customers.  When FinTech firms like Venmo and Robinhood were launched, early transaction volumes and dollar amounts were low. However, what was significant was the tremendous rate of growth.  In today’s environment, it doesn’t take long for new technology to go viral and impact behavior.   When your customers experience life-changing and lifestyle events your ability to be there, in a timely manner to support your customers as they navigate through these experiences, is increasingly important. Now is the time to implement IFM’s solution to protect your institution from competitive forces in your ever-changing industry.   Contact us today to learn how your FI can leverage our free evaluation and industry-leading customer insights.

 

The Rise of Digital Solutions in the Banking Industry: Is Your Bank Positioned Well in Our New World?

In a world dominated by the global pandemic, consumers and businesses have turned to contactless solutions and have minimized the need to conduct business in person, even as we slowly return to “normal”. These solutions have given rise to a swift increase in digital transactions as technology has enabled many financial activities to be conducted on a laptop, tablet, or mobile phone app.

The increase in digital transactions includes bill payments, P2P transactions, money movement, investment transactions, and even gambling and sending money overseas.

Another aspect of the global pandemic has been the need for financial institutions to “be there” for their business and consumer customers.  As banks and credit unions position their products and services to “be there” for their customers, this has given rise to a need for a deeper understanding of their customers’ lives and financial situation in order to provide the best possible service while at the same time maintaining profitability.

How will your Bank or Credit Union compete successfully in this New World?

One asset that is unique to your financial institution is your bank’s data, specifically the unique transactions that your customers conduct.  While the general activities and behaviors of your customers may be like other bank and credit union customers, the quality and quantity of their digital behavior becomes more unique.  Understanding what makes your customers unique is an opportunity for your institution to offer a personalized mix of financial solutions tailored to meet their needs.  This will lead to deeper engagement with your customers and your ability to maintain profitability. 

Insight Financial Marketing provides an industry-leading solution to help your bank or credit union deepen relationships with your customers even while the world is quickly changing around us.  Our innovative technology provides near real-time customer intelligence so that you can “be there” for your consumer and business customers.   

To find out more information about IFM’s solution or to get details on the free no-obligation trial of our service, please reach out to me via our website at Infimark.com.

 

 

As Digital Banking takes off, Personalized Marketing will become more important for the Banking Industry

A recent Bain & Company article mentions that financial institutions should be concerned that “many consumers look outside their primary bank for high-margin products”.   However, the article also contains recent survey results that note “most customers who received direct offers would be willing to buy more from their primary bank if it made a personalized offer”.

The article discusses how the rise of digital banking, in part caused by the global pandemic, has led to the hidden defection of bank customers.   As more consumers turn to digital banking it will be imperative for banks to personalize their messaging to customers to compete more effectively in today’s challenging environment.

What is Personalized Marketing?

The term personalization means reaching consumers with messages, pricing, and offers tailored specifically for them. This form of one-to-one marketing uses sophisticated analytic tools to analyze customer data to determine customer needs.

Banks are moving rapidly to leverage personalized messaging by using machine learning and data science tools to process a vast amount of customer data. Insight Financial Marketing is a leading innovator in this segment of the industry. IFM’s service enables banks to analyze the patterns in their transaction data to anticipate specific customer needs. A customer is more likely to purchase a bank product or service if the right offer is presented to them at the right time. This creates a win-win situation where the customer gets the service or product they need, and the bank generates revenue.

The Future of Bank Marketing

By leveraging IFM’s technology, financial service firms will be able to utilize customer intelligence to structure their products, services, and pricing based on customer needs.

For instance, combining artificial intelligence and machine learning with customer insights via IFM’s service has the potential to be a real game-changer. By leveraging IFM’s technology, financial institutions have greater precision in assessing risks, predicting life events, and lifestyle changes.

Personalization has made it possible for customers to save money by presenting the right products and services that meet their current and future needs, while also allowing financial institutions to prosper by developing deeper relationships with their customers. The future of marketing in the financial services industry is being shaped by a rapid shift to the personalization of communications brought on by modern data analysis tools provided by IFM.

Conclusion

With IFM’s industry-leading customer intelligence solution, financial institutions will have the technology and experience to leverage personalized marketing that will drive revenue growth and enable your firm to compete more effectively in today’s challenging environment. To discover how your firm can engage IFM’s solution please contact me via our website, Infimark.com, or via my LinkedIn page.

Cutting edge technology has fostered rapid innovation throughout various industries over the past ten years. This rapid change is impacting the financial services industry with full force, especially during this difficult COVID environment. Consumers and businesses are rapidly searching for solutions to meet their needs. Examples of this change include real-time P2P and contactless payments, investment robo-advisors, free stock trading, and app-based financial technology such as Dave™, Chime™, and Square’s CashApp.  

Additionally, historically low-interest rates, coupled with an uncertain economic environment that has stunted the appetite for lending at many institutions, have put pressure on banks to find ways to generate revenue.

Deepening relationships with existing customers is one avenue financial institutions will travel as they revamp their strategy to generate revenue while also helping customers navigate their financial journey. One challenge with this strategy is the fact that every customer has a unique journey. A one-sized approach to helping consumer and business customers in their time of need is no longer effective. Data-driven intelligence is necessary in today’s world to create personalized solutions for customers that meet their immediate and future financial needs. A powerful way to develop insights that will assist FI’s in deepening relationships is through analysis and evaluation of electronic financial transactions. IFM provides an easy to deploy solution that provides near real-time customer insights.  

Using IFM’s service and capabilities, FI’s can –

  • Utilize powerful customer insights to aid in the development of new and innovative products and services
  • Identify changes in customer behavior and predict future needs in near real-time
  • Have a resource that provides clean data to power AI and machine learning initiatives
  • Know whom to communicate with and when to communicate personalized messages

If you’d like additional information on IFM’s service and capabilities, or would like to learn more about IFM’s free, no-obligation evaluation of ACH and Card data, please contact us via our website, or reach out to me directly via my LinkedIn page or my email address at rreale@infimark.com. I’ll look forward to communicating with you and helping you along your journey toward developing an enhanced data-driven customer intelligence capability.  

Check out my latest vlog where I share more about the challenges customers are facing during the global pandemic.

 

 

 

Consumers and Businesses shift to enhanced digital functionality

Even before the COVID crisis, consumers and businesses were rapidly shifting to solutions provided in a digital transaction environment. For traditional financial institutions, adapting to these fast-moving changes is paramount to consumer and business banking customer retention and deepening relationships.

How is competition evolving as the shift to digital financial services has been accelerated during the COVID crisis?

Intuit’s QuickBooks Cash:

Earlier this Summer, Intuit’s QuickBooks™ rolled out QuickBooks Cash™, a component of their service to small businesses. Intuit partnered with Green Dot Bank to integrate access to a bank account directly within the QuickBooks platform.   Within QuickBooks and QuickBooks Cash, businesses now have access to a debit card, instant deposits, and bill payment. Many small businesses leverage QuickBooks to help manage their accounting, payments, and payroll within the digital platform. The addition of QuickBooks Cash may entice businesses to move more of their business banking relationship to Intuit’s ecosystem.

SoFi Money:

SoFi is a fast-growing fintech firm that began by offering student loans on a digital platform. Since its founding, it has rapidly moved to become more bank-like by expanding its digital payment services. This includes its new offering of SoFi Money™ a high-interest cash management account that also includes a debit card, integrates P2P transfers, and a mobile app.

What strategy can your Financial Institution deploy to compete more effectively:

As your financial institution shifts to offer more digital functionality, your FI has a wealth of information about your customers that is unique to your firm. By leveraging this information, your FI can identify shifts in digital behavior and target communications and messaging to specific segments of your customers. As an example, many of your customers have an existing relationship with QuickBooks and SoFi, and the growth of engagement with these companies continues at a rapid pace. By using a data intelligence service like the one offered by IFM, you can identify which of your customers have a relationship with these firms and track any changes in activity to enable targeted communications to be delivered in near real-time to limit relationship depletion as new services and capabilities are added by competing non-traditional financial institutions.

For more information on how your FI can leverage IFM’s services, we’ve added additional information on our website at infimark.com. IFM offers a free evaluation and analysis giving FIs a detailed view of competitive customer relationships and identifying opportunities for the retention and deepening of consumer and business banking relationships.

Check out the latest vlog from Rob Reale where he discusses how consumers and businesses are finding digital solutions to their financial services needs.

 

 

According to the book, ‘Marketing Metrics’, it is easier to sell a product to an existing customer (a 60-70% conversion rate) than to sell a product to a new qualified prospect (a 5-20% conversion rate). With existing clients, businesses already know their clients’ pain points, and the clients may have already become loyal to the financial institution. In the banking industry, banks often have a variety of products, but a good fraction of current customers might only utilize one or two products.

It can be challenging for a banker to sell a full range of financial products to a single customer, so front-line employees may only master a few of the high-performing financial products. Fortunately, banks have a valuable asset: customer data. With the right approach, a financial institution can evaluate their data and generate insights on cross-selling opportunities. This strategic approach to cross-selling is where predictive analytics comes in.

Here is how predictive analytics can be used for cross selling in banking:

The Power Of Predictive Analytics

The commoditization of financial products makes cross selling in banking arduous. Since customers may feel that they can get a better deal somewhere else, they might pick the most affordable product from your financial institution and hunt for other products elsewhere. This commoditization has resulted in banks bundling multiple banking products in an effort to create higher perceived value for the customer.

However, push-based selling and “one-size-fits-all” campaigns might not suffice to lure the modern-day customer. They need access to valuable products, and they need it now. Any product you bundle with the rest of your financial offerings should add the most value to their lives.

Given that banks collect data through CRM software and online tools, they can use this data to identify what their customers need. The data provides insights into:

  1. What customer to contact first

  2. What to sell them

  3. How to communicate with them

Predictive analysis allows banks to evaluate buyer behavior through recent account activities and sometimes even online activities such as reviews and complaints. Instead of offering a single generalized offer, financial institutions can personalize their products to a specific prospect group which can improve a campaign’s return on investment.

Predictive Analytics Steps

1. Start With A Question

Banks collect vast chunks of data, and they will be nothing more than data without analyzing them. To successfully identify opportunities for cross selling in banking, they must create a question and look for its answers through predictive analysis. Unlike conventional business intelligence (BI) tools that tend to be retrospective in nature, predictive analytics tools should provide insights into the future. You can get answers to questions like:

  • What customer demographics are the most likely to churn?

  • What is the estimated number of leads the institution will get from a marketing campaign?

  • What are the odds of a customer purchasing product y after purchasing product X?

  • How profitable might a specific product package be over the next two years?

2. Collect Data

The next step is to identify and collect the data that might bring the bank close enough to the answers. However, the level of confidence a bank can have in its predictive analytics software will significantly depend on the quality of the data it collects. As long as the data meets a quality threshold, it will provide trustable insights.

For the financial institutions storing outdated, inconsistent, or even incomplete client data in their CRM, data collection can become quite time-consuming. As a result, the onus is upon bank managers to spearhead data quality management that lays the foundation for a streamlined process.

Data stored within banking CRM might not be sufficient for some predictive models. Banks might need to get data from other sources. Some of these sources include:

  • ACH transactions

  • Bill payment behavior

  • Geolocation

  • Personal financial management

  • Wire & check payment data

  • Credit cards and debit cards

3. Build A Predictive Model

Next, data analysts have to create a predictive model that will define and determine the probability of specific events happening. These analysts can leverage artificial intelligence and machine learning methods, such as deep learning or linear regressions, to predict this. Once the model is created, test data has to be used to assess the predictive power of the model. For models that do not meet the expectations of the bank, they can be fine-tuned to offer higher predictive accuracy.

Data normalization can help increase the accuracy of a data model. Normalization helps achieve greater overall database organization, reduction of redundant data, improved data consistency within the database, and makes database security more manageable.

Once an accurate model is created, it can be a game-changer. Managers only need to feed their normalized data into the models and get the output they need to make decisions for cross-selling in banking.

4. Pay Close Attention To Assumptions

The idea that the future will always mirror the past is a major assumption throughout predictive analytics models. While there is some truth to this, consumer behaviors do change with time. If changes occur to the behavioral assumptions you might have made when creating your predictive analysis models; the models can become invalid.

Also, the variables of the models might change with changing market trends or time. For instance, the financial crisis of 2008 was significantly driven to by the assumption that house prices would always go up, which was not the case. Banks should pay attention to these assumptions to ensure that their predictions are still viable.

Predictive analysis isn’t a silver bullet for achieving cross selling in banking. Not all variables can be predicted to come up with trustworthy insights. Everything from the weather to the country’s political landscape can change buyer behavior. However, predictive analytics offers a much better solution for insightfully allocating marketing dollars than running financial marketing campaigns on underdeveloped research and half-baked ideas. Predictive analytics can provide financial institutions with a much-needed competitive advantage.

Reach out to our team at Insight Financial Marketing today to learn how you can get started with predictive analytics and how to translate changes in customer behavior into opportunities for your business.

 

 

The banking industry generates an enormous amount of data every day. Some of it comes from ATM logs, ACH transactions, SMS and online banking sessions, voice response systems, and more. Years ago, it wasn’t possible to collect, process, or store massive and complex data sets. Businesses had limited ways, if any, to leverage such data.

Today, there are a variety of technologies that have made big data a pivotal innovation driver in different industries. Big data analytics allows organizations to explore vast data sets to uncover insights like patterns and correlations, customer behavior, market trends, and so forth. This information helps managers to make informed decisions.

Impact of Big Data in Banking

Any financial institution that doesn’t jump onto the big data analytics train will have itself to blame for losing revenue. Studies have shown that the banking sector can attain about 18 percent revenue growth by making use of big data.

According to C-Suite banking executives, the modern customer wants highly personalized services. Big data in banking can help to meet customer demands, grow their business, and improve security and compliance. Here’s how banks can achieve this.

Enhanced Risk Management

Banks utilize business intelligence tools to identify potential risks related to lending money. With big data algorithms, lenders can identify customers with poor credit scores and decide whether to approve or decline their loan application. Big data analytics also assists banks in evaluating market trends and determine the opportune time to raise or lower interest rates for specific clients.

Big data in banking reduces the chance of data entry errors when filling out forms. By analyzing customer data, the system detects anomalies. Similarly, the bank can detect irregular transactions and potential fraud incidents and act accordingly.

For instance, if a person usually makes payments using a credit/debit card, an attempt to withdraw all their funds via ATM should be a matter of concern. It could mean a fraudster is trying to steal from the customer. The bank can call the account holder to clarify if the withdrawal is legitimate. Analyzing transactions using big data analytics has helped banks to ward off many fraudulent actions.

Personalization of Banking Solutions

Clients today detest the traditional one-size-fits-all approach to banking. People want banks that understand their needs and present sensible solutions. Consumers are likely to ignore banks that continually send mismatched offers. Annoyed customers won’t browse the rest of the portfolio, yet it could contain more exciting products.

Insights from big data analytics can help marketers to identify the type of products customers already have and what they would possibly want. They can then target individuals with products and services tailored for them from the point of understanding their needs. By doing this, banks can solve existing problems, win customer loyalty, and differentiate themselves from other financial institutions.

Accurate Cross-Selling

Big data can help banks to cross-sell auxiliary products more effectively by performing predictive analytics using wire data, check data, bill pay data, and credit card/debit card data. To succeed, the organization should focus on the value a product brings and the propensity of an individual to purchase it. A high-saving customer, for example, may be interested in tax-free investment opportunities like mutual funds.

Without information, organizations cannot avoid spamming consumers with unwelcome offers. For instance, it’s not worth the effort to market a short-term loan to a low-spending individual who is struggling with debts.

Banking technology and big data tools such as Hadoop and Fiserv can help automate the job. They can search through large data sets and enable financial institutions to make insightful decisions.

Transaction Channel Identification

Banks can benefit from understanding their customers’ preferred payment channels. Take the example of a business customer who prefers to make payments using paper checks. A business banker can reach out to this client and discuss treasury management service options that could help the customer’s business processes.

Final Thoughts

Businesses that are lagging in the big data analytics race are undoubtedly losing out in many areas. By utilizing big data in banking, banks are winning and retaining customers by offering personalized services and heightening security. Banks, on the other hand, are discovering new business opportunities while making their workplaces more conducive for their staff.

By utilizing big data in banking, banks are winning and retaining customers by offering personalized services by learning more about their customers’ needs. Banks are also discovering new business opportunities while improving risk management.

Insight Financial Marketing has over seventeen years of experience in helping banks identify opportunities to improve customer loyalty, grow revenue, and reduce potential risk through big data processing and analytics.  Contact the IFM team to learn how your institution can begin to reap the benefits of utilizing big data in banking.

 

 

 

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.