Connecting with Millennials By Going Beyond Traditional Services


technology-8-28-19.pngBanks are at a crossroads.

They have an opportunity to expand beyond traditional financial services, especially with younger customers that are used to top-notch user experiences from large technology companies. This may mean they need to revisit their strategy and approach to dealing with this customer segment, in response to changing consumer tastes.

Banks need to adjust their strategies in order to stay relevant among new competition: Accenture predicts that new business models could impact 80% of existing bank revenues by 2020. Many firms employ a “push” strategy, offering customers pre-determined bundles and services that align more with the institution’s corporate financial goals.

What’s missing, however, is an extensive “pull” strategy, where they take the time to understand their customers’ needs. By doing this, banks can make informed decisions about what to recommend to customers, based on their major consumer life milestones.

Only four in 10 millennials say that they would bundle services with financial institutions. Customers clearly do not feel that banks are putting them first. To re-attract customers, banks need to look at what they are truly willing to pay for — starting with subscription-based services. U.S consumers age 25 to 34 would be interested in paying subscription fees for the financial services they bundle through their bank such as loans, identity protection, checking accounts and more, according to a report from EY. With banks already providing incentives like lower interest rates or other perks to bundle their services, customers are likely to view a subscription of bundled services with a monthly or annual fee as the best value.

Subscription-based services are a model that’s already found success in the technology and lifestyle sector. This approach could increase revenue while re-engaging younger generations in a way that feels personal to them. Banks that decide to offer subscription-based services may be able to significantly improve relationships with their millennial customers.

But in order to gain a deeper understanding of what services millennials desire, banks will need to look at their current customer data. Banks can leverage this data with digital technology and partnerships with companies in sectors such as automotive, education or real estate, to create service offerings that capitalize on life events and ultimately increasing loyalty.

Student loans are one area where financial institutions could apply this approach. If a bank has customers going through medical school, they can offer a loan that doesn’t need to be repaid until after graduation. To take the relationship even further, banks can connect customers who are established medical professionals to those medical students to network and share advice, creating a more personal experience for everyone.

These structured customer interactions will give banks even more data they can use to improve their pull strategy. Banks gain a more holistic view of customers, can expand their menu of services with relevant products and services and improve the customer experience. Embracing a “pull” strategy allows banks to go above and beyond, offering products that foster loyalty with existing customers and drawing new ones in through expanded services. The banks that choose to evolve now will own the market, and demonstrate their value to customers early on.

Three Ways Directors Can Solve the 3,000-Year-Old Credit Problem


credit-7-9-19.pngHistory has shown that knowledge is power. One place that could use the benefit of that knowledge is commercial credit.

Banks have been lending to businesses for 3,000 years and has yet to figure out the commercial credit process. But executives and directors have an opportunity to fix this problem using data and digital capabilities to make the process more efficient and faster, and become the lending legends of their institutions.

In 1300 B.C. Egypt, the credit process looked something like this: A seafaring trader would trade bronze bowls with a local bronze merchant for cloth and garments. But to make this transaction, the bronze merchant would need to borrow from multiple merchant lenders. This process required lenders to understand the business plans of the borrower, go “door to door,” have community knowledge and know the value of all those goods. There were a lot of moving pieces—and a great deal of time—involved for that one transaction.

Fast-forward to today. A lot has changed in 3,000 years, but the commercial credit process has actually gone backwards. It can take a lender 60 to 90 days and more than $10,000 per lead to identify potential leads—and that’s before they review the application. After a borrower applies, the lender must look up credit reports, collect and spread financial statements and decide on the terms and conditions. Finally, the application goes through the credit department, which can take another 30 to 45 days and cost $5,000 per application.

Lenders will have spent all that time and effort to process the loan—but may not end up with a new customer to show for it. Meanwhile, borrowers will have spent time and effort to apply and wait—and may not have a loan to show for it.

While this problem has persisted for 3,000 years, the good news is that executives and directors have an opportunity to fix the problem by turning their manual-lending process into a digital-lending one. This evolution entails three steps that transform the current process from weeks of work into days.

First, a bank would use a digital-lending portal to gather applicable demographics to identify prospective borrowers. In researching prospects, they see critical borrower information such as name, address, years in business, legal structure, taxpayer identification number, history, business description and management team. Rather than having to wait until later in the process to uncover this critical information, they can immediately identify whether to pursue this lead and quickly move on.

Second, a bank uses a credit-decision engine to gather and analyze the applicable borrower data. Not only can the engine pull in consumer and credit bureau information, but it can also include automated financial collection, credit score and industry data for comparison. The bank can use data from this tool to determine terms and conditions, credit structure, purpose of credit facility, pricing, relationship models and cross-sell strategies.

Third and finally, the bank’s credit policy and process integrate with its credit-decision engine to enable an automated review of a loan application. This would include compliance checks, terms and conditions and credit structure. Since the data gathering and analysis has already taken place and automatically factored into the decision, there is no need to review all those pieces, as would be required with a manual process.

These three steps of this digital lending process have distilled a weeks-long process into about five days. Executives and directors can not only grow their institution in a shortened time period; they can do so without adding any risk. A bank I worked with that had $250 million in assets was able to add $20 million in loan volume without taking on any additional risk.

By using knowledge to their advantage and implementing a digital lending solution, bankers can save not just time and costs, but their institutions as well as their communities. They can now spend their limited time and resources where they matter most: growing relationships along with their banks. Having fixed the 3,000-year-old credit problem, they can place those challenges firmly in the past and focus on their future.

Sink or Swim in the Data Deep End


data-7-1-19.pngCommunity banks risk allowing big banks an opportunity to widen the competitive gap by not investing in their own data management.

It’s now-or-never for community banks, and a competitive edge could be the key to their survival. A financial institution’s lifeblood is its data and banks can access a veritable treasure trove of information. But data analytics poses a significant challenge to the future success of community banks. Banks should focus on the value, not volume, of their information when adopting an actionable, data-driven approach to decision-making. While many community banks acknowledge how critical data analytics are to their future success, most remain uncommitted.

This comes as the multi-national institutions expand their data science teams exponentially, create chatbots for their websites, use artificial intelligence to customize user interactions and apply machine learning to complete back-office tasks more efficiently. The advantage that a regional bank manager has when working next door to a community bank is growing too large. And the argument that the human touch and customer experience of a community bank will make up for the technological gap has become less convincing as younger customers forgo the branch in favor of their phone.

Small and medium institutions are dealing with a number of obstacles, including compressed margins and a shortage of talent, in an attempt to move past basic data analytics and canned ad hoc reports. If an institution can find a qualified candidate to lead their data management project, the candidate usually lacks banking experience and tends to have a science and mathematics backgrounds. A real concern for bankers is the hiring managers’ ability to ask the right questions and fully discern candidates’ qualifications. And once hired, is there a qualified leader to drive projects and their results?

Despite these obstacles, banks have only one option: Jump into the data deep end, head first. To compete in this data-driven world, community banks must deploy advanced data analytics capabilities to maximize the value of information. More insight can mean better decisions, better service to customers and a better bottom line for banks. The only question is how community banks can make up their lost ground.

The first step in building your organization’s data analytic proficiency is planning. It is crucial to understand your current processes and outputs, as well as your current staff’s capabilities, in order to improve your analysis. Once you know your bank’s capabilities, you can determine your goal posts.

A decision you will need to make during this planning stage will be the efficacy of building out staff to meet the project goals, or outsourcing the efforts to a consultant group or third-party software. A community bank’s ability to attract, manage and retain data specialist could be an obstacle. Data specialists tasked with managing more-complex diagnostic and predictive analytics should be part of the executive team, to give them a complete understanding of the institution’s strategic position and the current operating environment.

Another option community banks have is to buy third-party software to supplement current resources and capabilities. Software can allow a bank to limit the staffing resources required to meet their data analytical goals. But bankers need to understand the challenges.

A third-party provider needs to understand your organization and its strategic goals to tailor a solution that fits your circumstances and environment. Management should also weigh potential trade-offs between complexity and accessibility. More-complex software may require additional resources and staff to deploy and fully use it. And an institution shouldn’t solely rely on any third-party software in lieu of internal champions and subject-matter experts needed to fully use the solutions.

Whatever the approach, community bank executives can no longer remain on the sidelines. As the volume, velocity and variety of data grows daily, the tools needed to manage and master the data require more time and investment. Proper planning can help executives move their organizations forward, so they can better utilize the vast amount of data available to them.

How Innovative Banks Keep Up With Compliance Changes


compliance-6-5-19.pngBankers and directors are increasingly worried about compliance risk.

More than half of executives and directors at banks with more than $10 billion in assets said their concerns about compliance risk increased in 2018, according to Bank Director’s 2019 Risk Survey. At banks of all sizes, 39 percent of respondents expressed increasing concern about their ability to comply with changing regulations.

They’re right to be worried. In 2018, U.S. banks saw the largest amount of rule changes since 2012, according to Pamela Perdue, chief regulatory officer for Continuity. This may have surprised bankers who assumed that deregulation would translate to less work.

“The reality is that that is not the case,” she says. “[I]t takes just as much operational effort to unwind a regulatory implementation as it does to ramp it up in the first place.”

Many banks still rely on compliance officers manually monitoring websites and using Google alerts to stay abreast of law and policy changes. That “hunt-and-peck” approach to compliance may not be sufficiently broad enough; Perdue said bankers risk missing or misinterpreting regulatory updates.

This potential liability could also mean missed opportunities for new business as rules change. To handle these challenges, some banks use regulatory change management (RCM) technology to aggregate law and policy changes and stay ahead of the curve.

RCM technology offerings are evolving. Current offerings are often included in broader governance risk and compliance solutions, though these tools often use the same manual methods for collecting and processing content that banks use.

Some versions of RCM technology link into data feeds from regulatory bodies and use scripts to crawl the web to capture information. This is less likely to miss a change but creates a mountain of alerts for a bank to sort through. Some providers pair this offering with expert analysis, and make recommendations for whether and how banks should respond.

But some of the most innovative banks are leveraging artificial intelligence (AI) to manage regulatory change. Bank Director’s 2019 Risk Survey revealed that 29 percent of bank respondents are exploring AI, and another 8 percent are already using it to enhance the compliance function. Companies like San Francisco-based Compliance.ai use AI to extract regulatory changes, classify them and summarize their key holdings in minutes.

While AI works exponentially faster than human compliance officers, there are concerns about its accuracy and reliability.

“I think organizations need to be pragmatic about this,” says Compliance.ai chief executive officer and co-founder Kayvan Alikhani. “[T]here has to exist a healthy level of skepticism about solutions that use artificial intelligence and machine learning to replace what a $700 to $800 an hour lawyer was doing before this solution was used.”

Compliance.ai uses an “Expert in The Loop” system to verify that the classifications and summaries the AI produced are accurate. This nuanced version of supervised learning helps train the model, which only confirms a finding if it has higher than 95 percent confidence in the decision.

Bankers may find it challenging to test their regulatory technology systems for accuracy and validity, according to Jo Ann Barefoot, chief executive officer of Washington-based Barefoot Innovation Group and Hummingbird Regtech.

“A lot of a lot of banks are running simultaneously on the new software and the old process, and trying to see whether they get the same results or even better results with the new technology,” she says.

Alikhani encourages banks to do proofs of concept and test new solutions alongside their current methodologies, comparing the results over time.

Trust and reliability don’t seem to be key factors in bankers’ pursuit of AI-based compliance technology. In Bank Director’s 2019 Risk Survey, only 11 percent of banks said their bank leadership teams’ hesitation was a barrier to adoption. Instead, 47 percent cited the inability to identify the right solution and 37 percent cited a lack of viable solutions in the marketplace as the biggest deterrents.

Bankers who are adopting RCM are motivated by expense savings, creating a more robust compliance program and even finding a competitive edge, according to Barefoot.

“If your competitors are using these kinds of tools and you’re not that’s going to hurt you,” she says.

Potential Technology Partners

Continuity

Combines regulatory data feeds with consultative advice about how to implement changes.

Compliance.ai

Pairs an “Expert in the Loop” system to verify the accuracy of AI summaries and categorization

OneSumX Regulatory Change Management from Wolters Kluwer

Includes workflows and tasks that help banks manage the implementation of new rules and changes

BWise

Provides impact ratings that show which parts of the bank will be impacted by a rule and the degree of impact

Predict360 from 360factors

Governance risk and compliance solution that provides banks with access to the Code of Federal Regulations and administrative codes for each state

Learn more about each of the technology providers in this piece by accessing their profiles in Bank Director’s FinXTech Connect platform.

Five Reasons Behind Mortgage Subservicing’s Continued Popularity


mortgage-6-3-19.pngMortgage subservicing has made significant in-roads among banks, as more institutions decide to outsource the function to strategic partners.

In 1990, virtually no financial institution outsourced their residential mortgage servicing.

By the end of 2018, the Federal Reserve said that $2.47 trillion of the $10.337 trillion, or 24%, of mortgage loans and mortgage servicing rights were subserviced. Less than three decades have passed, but the work required to service a mortgage effectively has completely changed. Five trends have been at work pushing an increasing number of banks to shift to a strategic partner for mortgage subservicing.

  1. Gain strategic flexibility. Servicing operations carry high fixed costs that are cannot adapt quickly when market conditions change. Partnering with a subservicer allows lenders to scale their mortgage portfolio, expand their geographies, add product types and sell to multiple investors as needed. A partnership gives bank management teams the ability to react faster to changing conditions and manage their operations more strategically.
  2. Prioritizes strong compliance. The increasing complexity of the regulatory environment puts tremendous strain on management and servicing teams. This can mean that mortgage businesses are sometimes unable to make strategic adjustments because the bank lacks the regulatory expertise needed. But subservicers can leverage their scale to hire the necessary talent to ensure compliance with all federal, state, municipal and government sponsored entity and agency requirements.
  3. Increased efficiency, yielding better results with better data. Mortgage servicing is a data-intensive endeavor, with information often residing in outdated and siloed systems. Mortgage subservicers can provide a bank management team with all the information they would need to operate their business as effectively and efficiently as possible.
  4. Give borrowers the experience they want. Today’s borrowers expect their mortgage lender to offer comparable experiences across digital channels like mobile, web, virtual and video. But it often does not make sense for banks to build these mortgage-specific technologies themselves, given high costs, a lack of expertise and gaps in standard core banking platforms for specific mortgage functions. Partnering with a mortgage subservicer allows banks to offer modern and relevant digital servicing applications.
  5. Reduced cost. Calculating the cost to service a loan can be a challenging undertaking for a bank due to multiple business units sharing services, misallocated overhead charges and hybrid roles in many servicing operations. These costs can be difficult to calculate, and the expense varies widely based on the type of loans, size of portfolio and the credit quality. A subservicer can help solidify a predictable expense for a bank that is generally more cost efficient compared to operating a full mortgage servicing unit.

The broader economic trends underpinning the growing popularity of mortgage subservicing look to be strengthening, which will only accelerate this trend. Once an operational cost save, mortgage subservicing has transformed into a strategic choice for many banks.

Mining for Gold in Bank Data


data-5-9-19.pngCommunity banks are drowning in customer data.

Every debit card swipe, every ACH and every online bill pay produces data that provides insight into their customers’ relationship with the institution, as well as their lifestyle and potential needs. Banks should prioritize using their proliferation of customer data to open up new service and revenue opportunities. The potential to identify untapped opportunities is enormous.

The amount of data generated by the digitization of services and customer interactions has grown exponentially in recent years. By 2020, about 1.7 megabytes of new information will be created every second for every person on the planet, according to a 2017 McKinsey & Co. report. This figure is only expected to increase: By 2021, half of adults worldwide will use a smartphone, tablet, PC or smartwatch to access financial services. The mindboggling amount of data comes at a time when companies must “fundamentally rethink how the analysis of data can create value for themselves and their customers,” according to a Harvard Business Review article by Thomas Davenport, a professor at Babson College and a fellow at the MIT Initiative on the Digital Economy.

Amazon is often cited as the model for capitalizing on data to increase sales and improve consumers’ experience. The company tracks each customer interaction—from site searches and purchases, from Alexa commands to song or movie downloads—to develop a holistic view of that consumer’s preferences and buying habits. For instance, if a consumer purchases prenatal vitamins from Amazon, she will soon see pop-up ads for other pregnancy and baby-related items. Amazon will also send her offers and reminders to repurchase the vitamins at the exact time they run out.

Banks should try to emulate Amazon’s ability to highly personalize a consumer’s experience. Organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin, according to Gallup. And personalization based on customer data can deliver 5 to 8 times the return on investment on marketing expenses and increase sales by 10 percent or more, according to McKinsey.

But in order for banks to use the data produced by their internal systems, they need to create a structure and plan around it. Institutions need to direct information to one location, figure out how to analyze it and—most importantly—develop an actionable plan. This is a challenge because many banks partner with a myriad of vendors to provide the different consumer services such as debit and credit card processing, online banking and bill pay vendors. To consumers, these disparate systems may appear to work together reasonably well; behind the scenes, they may not communicate with each other.

This is an overwhelming imperative for many community banks. Fifty-seven percent of financial institutions say their biggest impediment to capitalizing on their data is that it is siloed and not pooled for the benefit of the entire organization, according to a July 2016 report from The Financial Brand. Other impediments include the time it takes to analyze large data sets and a lack of skilled data analysts.

But banks can remove these impediments with an “intelligent” data management technology platform that aggregates information from unlimited sources and makes it available enterprise-wide, from frontline staff to marketing to management. Platforms analyze data from sources like the core processor, online banking and lending systems, as well as peer and demographic data, and develop automated revenue- and service-enhancing strategies that capitalize on the findings.

The results are better, automated and even instantaneous decisions that generate greater sales opportunities and improve customer experience.

Banks can use the data to generate personalized, targeted marketing and communications campaigns that are triggered by an increase or decrease in customer transactional activity. Reduced activity can indicate an account might leave the institution; proactive communication can reengage the customer and retain the account.

This data can improve cross-selling objectives, generate sales opportunities and track onboarding activities to facilitate the customer’s experience. The data could identify customers who use payday or other non-bank lenders, and generate omni-channel offers for in-house products. It could also flag follow-up communications on products or services that consumers expressed interest in, but did not open.

Centralizing institutional data into one platform also creates efficiencies by automating manual processes like new account onboarding, loan origination and underwriting—even customer complaint resolution. It can also introduce additional customer services provided by third-party vendors by requiring them to integrate with only one data source, instead of many.

Banks need to leverage their customer data in order to create highly personalized and meaningful offers that improve engagement and overall performance. With the assistance of a comprehensive data management platform, community banks can overcome the hurdles of unlocking the value of their data and achieve Amazon-like success.

The Transformative Impact Of Data & Voice



The biggest banks are spending billions on technology, but community banks can level the playing field by choosing technologies that personalize and enhance their interactions with customers, as Michael Carter, executive vice president at Strategic Resource Management, explains in this video. He shares how data and voice-enabled technologies could help community banks provide the digital experience that customers want.

  • Leveraging Data to Enhance the Customer Experience
  • Growing Use of Voice-Enabled Technologies
  • Opportunities for Community Banks

 

Applying the 1-10-100 Rule to Loan Management


data-4-2-19.pngImplementing new software may seem like an expensive and time-consuming challenge, so many financial institutions make do with legacy systems and workflows rather than investing in robust, modern technology solutions aimed at reducing operating expenses and increasing revenue. Unfortunately, banks stand to lose much more in both time and resources by continuing to use outdated systems, and the resultant data entry errors put institutions at risk.

The Scary Truth about Data Entry Errors
You might be surprised by the error rates associated with manual data entry. The National Center for Biotechnology Information evaluated over 20,000 individual pieces of data to examine the number of errors generated from manually entering data into a spreadsheet. The study, published in 2008, revealed that the error rates reached upwards of 650 errors out of 10,000 entries—a 6.5 percent error rate.

Calculating 6.5 percent of a total loan portfolio—$65,000 of $1 million, for example—produces an arbitrary number. To truly understand the potential risk of human data entry error, one must be able to estimate the true cost of each error. Solely quantifying data entry error rates is meaningless without assigning a value to each error.

The 1-10-100 Rule is one way to determine the true value of these errors.

The rule is outlined in the book “Making Quality Work: A Leadership Guide for the Results-Driven Manager,” by George Labovitz, Y.S. Chang and Victor Rosansky. They posit that the cost of every single data entry error increases exponentially at subsequent stages of a business’s process.

For example, if a worker at a communications company incorrectly enters a potential customer’s address, the initial error might cost only one dollar in postage for a wrongly-addressed mailer. If that error is not corrected at the next stage—when the customer signs up for services—the 1-10-100 Rule would predict a loss of $10. If the address remains uncorrected in the third step—the first billing cycle, perhaps—the 1-10-100 Rule would predict a loss of $100. After the next step in this progression, the company would lose another $1,000 due to the initial data entry error.

This example considers only one error in data entry, not the multitude that doubtlessly occur each day in companies that rely heavily on humans to enter data into systems.

In lending, data entry goes far beyond typos in customers’ contact information and can include potentially serious mistakes in vital customer profile information. Data points such as social security numbers and dates of birth are necessary to document identity verification to comply with the Bank Secrecy Act. Data entry errors also lead to mistakes in loan amounts. A $10,000 loan, for example, has different implications with respect to compliance reporting, documentation, and pricing than a $100,000 loan. Even if the loan is funded correctly, a single zero incorrectly entered in a bank’s loan management system can lead to costly oversights.

Four Ways Data Entry Errors Hurt the Bottom Line
Data entry errors can be especially troublesome and costly in industries in which businesses rely heavily on data for daily operations, strategic planning, risk mitigation and decision making. In finance, determining the safety and soundness of an institution, its ability to achieve regulatory compliance, and its budget planning depend on the accuracy of data entry in its loan portfolios, account documentation, and customer information profiles. Data entry errors can harm a financial institution in several ways.

  1. Time Management. When legacy systems cannot integrate, data ends up housed in different silos, which require duplicative data entry. Siloed systems and layers of manual processes expose an institution to various opportunities for human error. The true cost of these errors on an employee’s time—in terms of wages, benefits, training, etc.—add up, making multiple data entry a hefty and unnecessary expense.
  2. Uncertain Risk Management. No matter how many stress tests you perform, it is impossible to manage the risk of a loan portfolio comprised of inaccurate data. In addition, entry errors can lead to incorrectly filed security instruments, leaving a portfolio exposed to the risk of insufficient collateral.
  3. Inaccurate Reporting. Data entry errors create unreliable loan reports, leading to missed maturities, overlooked stale-dates, canceled insurance and other potentially costly oversights.
  4. Mismanaged Compliance. Data entry errors are a major compliance risk. Whether due to inaccurately entered loan amounts, file exceptions, insurance lapses or inaccurate reporting, the penalties can be extremely costly—not only in terms of dollars but also with respect to an institution’s reputation.

Reduce Opportunities for Human Error
An institution’s risk management plan should include steps intended to mitigate the inevitable occurrence of human error. In addition to establishing systems of dual control and checks and balances, you should also implement modern technologies, tools, and procedures that eliminate redundancies within data entry processes. By doing so, you will be able to prevent mistakes from happening, rather than relying solely on a system of double-checking.

Does Your Digital Strategy Include the “Last Mile?”


strategy-3-20-19.pngThe “last mile” is a ubiquitous term that originated in the telecommunications industry to represent the final leg of delivering service to a customer. Most of the time it referred to installing copper wire that connected the local telephone exchange to individual landlines.

More recently, the term represents what can be the final and most challenging part of a consumer interaction. Generally, it’s the point at which a broad consumer service interacts with an individual customer to deliver a personalized experience.

In banking, this is most often in the form of digital documents created to meet the exact specifications and compliance requirements of an individual transaction that allow a loan or deposit to be booked.

The last mile concept is changing the way financial institutions approach their digital strategy. Previously, many banks focused on digital services to a broad customer base that allowed end users to access account information, pay bills and transfer funds. Lesser in the strategy was the ability to originate a loan or deposit transaction through a digital channel, and even less likely to be contemplated was the customer experience while documenting and booking these types of transactions.

Often, what would begin as a digital experience through a mobile device, tablet or PC would quickly revert to a less accessible process that concluded with a customer coming to a branch to manually sign an agreement.

Banks today are recognizing that a shift in their digital strategy is required. Increasingly, institutions are reshaping their digital presence to focus on the “last mile” – the hardest part of the customer journey that requires an individualized experience. Building a foundation focused on this critical customer touchpoint requires banks to deploy technology that documents, in a fully compliant manner, consumer and commercial loan and deposit transactions while at the same time supporting a fully digital customer experience.

In seeking fintech partners that can support this digital strategy shift, institutions are identifying essential attributes and capabilities to enable effective execution:

  • Integrated Capabilities: Disparate systems require data to be imported and exported to avoid data conflict. A single system of record, integrated with digital document capabilities and a two-way data flow, supports data integrity while eliminating the need to access separate solutions.
  • In-house Compliance Expertise: Documenting transactions in a compliant manner is essential. State and federal mandates change frequently. In-house compliance expertise supported by unique research capabilities ensures the documented words are accurate and up to date.
  • Electronic Closing Enabled: The ability to leverage technology from origination to customer signature without deploying manual workarounds or static forms.
  • Reinvestment in Technology: Digital capabilities continue to evolve. Gone are the days of generic templates and static documents. A partner that’s focused on both current and future capabilities ensures an institution isn’t left behind the times.

As your bank begins to formulate a digital strategy or if you’re revising your existing strategy, ask yourself if you’ve contemplated the “last mile.” If not, focus on this part of the customer interaction first to deliver a comprehensive, compliant, and digitally enabled experience.

Strengthening Customer Engagement



Fintech companies are laser-focused on improving consumer engagement—but there is room for traditional banks to gain ground, according to Craig McLaughlin, president and CEO of Extractable. In this video, he shares three ways banks can strategically approach improving the customer experience at their own institutions.

  • The One Trait That Sets Fintechs Apart
  • Improving the Customer Experience
  • Understanding Digital Strategy