Turning Compliance From an Exercise Into a Partnership

The Greek philosopher Heraclitus once observed that no one can ever step into the same river twice. If these philosophers tried to define how the financial industry works today, they might say that no bank can ever step into the same technology stream twice.

Twenty-first century innovations, evolving standards and new business requirements keep the landscape fluid — and that’s without factoring in the perpetual challenge of regulatory changes. As you evaluate your institution’s digital strategic plan, consider opportunities to address both technology and compliance transformations with the same solution.

The investments your bank makes in compliance technology will set the stage for how you operate today and in the future. Are you working with a compliance partner who offers the same solution that they did two, five or even 10 years ago? Consider the turnover in consumer electronics in that same period.

Your compliance partner’s reaction time is your bank’s reaction time. If your compliance partner is not integrated with cloud-based systems, does not offer solutions tailored for online banking and does not support an integrated data workflow, then it isn’t likely they can position you for the next technology development, either. If your institution is looking to change core providers, platform providers or extend solutions through application programming interfaces, or APIs, the limitations of a dated compliance solution will pose a multiplying effect on the time and costs associated with these projects.

A compliance partner must also safeguard a bank’s data integrity. Digital data is the backbone of digital banking. You need a compliance partner who doesn’t store personally identifiable information or otherwise expose your institution to risks associated with data breaches. Your compliance data management solution needs to offer secured access tiers while supporting a single system of record.

The best partners know that service is a two-sided coin: providing the support you need while minimizing the support required for their solution. Your compliance partner must understand your business challenges and offering a service model that connects bank staff with legal and technology expertise to address implementation questions. Leading compliance partners also understand that service isn’t just about having seasoned professionals ready to answer questions. It’s also about offering a solution that’s designed to deliver an efficient user experience, is easy to set up and provides training resources that reach across teams and business footprints — minimizing the need to make a support call. Intuitive technology interfaces and asynchronous education delivery can serve as silent accelerators for strategic goals related to digitize lending and deposit operations.

Compliance partners should value and respect a bank’s content control and incorporate configurability into their culture. Your products and terms belong to you. It’s the responsibility of a compliance partner to make sure that your transactions support the configurability needed to service customers. Banks can’t afford a compliance technology approach that restricts their ability to innovate products or permanently chains them to standard products, language or workarounds to achieve the output necessary to serve the customer. Executives can be confident that their banks can competitively adapt today and in the future when configurability is an essential component of their compliance solution.

A compliance partner’s ability to meet a bank’s needs depends on an active feedback loop. Partners never approach their relationship with firms as a once-and-done conversation because they understand that their solution will need to adjust as business demands evolve. Look for partners that cultivate opportunities to learn how they can grow their solution to meet your bank’s challenges.

Compliance solutions shouldn’t be thought of as siloed add-ons to a bank’s digital operations. The right compliance partner aligns their solution with a bank’s overall objectives and helps extend its business reach. Make sure that your compliance technology investment positions your bank for long-term return on investment.

Making Strategic Decisions With The Help of Data Analytics

Banks capture a variety of data about their customers, loans and deposits that they can harness in visually effective ways to support strategic decision-making. But to do this successfully, they must have leadership commit to provide the funding and human resources to improve data collection and management.

Bad data or poor data quality costs U.S. businesses about $3 trillion annually, and breeds bad decisions made from having data that is just incorrect, unclean, and ungoverned,” said Ollie East, consulting director of advanced analytics and data engineering at Baker Tilly.

Companies generally have two types of data: structured and unstructured. Structured data is information that can be organized in tables or a database: customer names, age, loan balances and interest rates. Unstructured data is information that exists in written reports, online customer reviews or notes from sales people. It does not fit into a standard database and is not easily relatable to other data.

If data analytics is the engine, then data is the gasoline that powers it,” East said. “Everything starts with data management: getting and cleaning data and putting it into a format where it can be used, governed, controlled and treated as an asset.”

A maturity model for data analytics progresses from descriptive to prescriptive uses for the information. The descriptive level answers questions like, “What happened?” The diagnostic level answers, “Why did it happen?” The predictive level looks at “What will happen?” Finally, at the prescriptive level, a company can apply artificial intelligence, machine learning or robotics on large sets of structured and unstructured data to answer “How can I make it happen?”

Existing cloud-based computing technology is inexpensive. Companies can import basic data and overlay a Tableau or similar dashboard that creates a compelling visual representation of data easily understood by different management teams. Sean Statz, senior manager of financial services, noted that data visualization tools like Tableau allows banks to create practical visual insights into their loan and deposit portfolios, which in turn will support specific strategic initiatives.

To do a loan portfolio analysis, a simple extraction of a bank’s data at a point in time can generate a variety of visual displays that demonstrate the credit and concentration risks. Repetitive reporting allows the bank to analyze trends like the distribution of credit risk among different time periods and identify new pricing strategies that may be appropriate. Tableau can create a heat map of loans by balance, so bankers can quickly observe the interest rates on different loans. Another view could display loss rates by risk rating, which can help a bank determine the real return or actual yield it is earning on its loans.

Statz said sophisticated analytics of deposit characteristics will help banks understand customer demographics, and adjust their strategies to grow and retain different types of customers. Bank can use this information in their branch opening and closing decisions, or prepare for CD maturities with questions like, “When CDs roll over, what products will we offer? If we retain all or only half of CD customers, but at higher interest rates, how does that affect cost of funds and budget planning?”

Data analytics can help banks undergo more sophisticated key performance indicator comparisons with their peers, not just at an aggregate national or statewide level, but even a more narrow comparison into specific asset sizes.

Banks face many challenges in effective data analytics, including tracking the right data, storing and extracting it, validating it and assigning resources to it correctly. But the biggest challenge banks need to tackle is determining if they have the necessary data to tackle specific problems. For example, the Financial Accounting Standards Board’s new current expected credit loss (CECL) standards require banks to report lifetime credit losses. If banks do not already track the credit quality characteristics they will need for CECL, they need to start capturing that data now.

Banks often store data on different systems: residential real estate loans on one system, commercial loans on another. This makes extracting the data in a way that supports data visualization like Tableau difficult. They must also validate the data for accuracy and identify any gaps in either data collection or inputting through the system. They also need to ensure they have the human resources and tools to extract, scrub and manipulate essential data to build out a meaningful analytic based on each data type.

The key to any successful data analytics undertaking is a leadership team that is committed to developing this data maturity mindset, whether internally or with help from a third party.

A Long-Term Approach to Credit Decisioning

Alternative data doesn’t just benefit banks by enhancing credit decisions; it can help expand access to capital for consumers and small businesses. But effectively leveraging new data sources can challenge traditional banks. Scott Spencer of Equifax explains these challenges — and how to overcome them — in this short video. 

  • The Potential for Alternative Data
  • Identifying & Overcoming Challenges
  • Considerations for Leadership Teams

 

The High Cost of Bad Customer Data

Bad data may mean banks miss out on major moments in their customers lives — and big opportunities to cement and deepen the customer relationships.

Providing personalized service and exclusive offers to customers is perhaps more important for financial institutions than any other industry. Consumers expect personalization, according to a study from Epsilon, and they become more comfortable with providing personal data when they believe there is a benefit or incentive to doing so. But most consumers don’t think their primary financial institution really knows the important components of their financial lives — according to research from Accenture, less than 3% of customers felt confident their bank knows them and their financial needs well.

As bank customers, we’ve all been on the receiving end of a new product offer based on bad data. From emails touting the first-time home-owner program sent to individuals preparing for retirement to student loan offers received by recent graduates, disconnects like this can plant the seed of perceived ineptitude for an otherwise successful company.

To prevent these common, and costly errors, banks need to prioritize maintaining their customer data. Not contextualizing your bank’s marketing is bad; what’s worse is when attempts at personalization fail. Your bank loses customers’ trust and undermines its own brand.

Banks can learn valuable lessons from the healthcare industry when it comes to maintaining customer data. Before patients ever talk to a doctor, they are prompted to verify basic pieces of information and to confirm that nothing has changed since their previous visit, alerting the healthcare provider to any recent life changes. This process typically takes less than 2 minutes and is a simple step banks can and should do to ensure customer data is accurate and updated.

Bad data is generally thought of as information that is inaccurate, incomplete, non-conforming, duplicative or the result of a poor input process. But this is not the complete picture. Data that is aggregated or siloed in a way that makes it inaccessible or unusable is also considered bad data, as is information that doesn’t garner any meaning or insight into business practices or isn’t available in a timely manner. Simply put, data that is not working for your organization is bad data.

The advancement of cloud storage has lowered the infrastructure cost of maintaining data over the last few years. At the same time, the exponential growth of collectable data points and the advancements of smart technologies have compounded the growth rate, leading to increased data management cost. If your bank is not scrubbing collected data to make sure it is complete, accurate and, most importantly, useful, your bank is wasting valuable company resources.

The cost of bad data to your institution is more than just dollars spent on data management

  • It is the inability to take advantage of opportunities that utilize AI and predictive analytics.
  • It is the slowed business cycle that prevents bank executives from reacting to changes in their market.
  • It is the increased operational cost that forces managers to focus on data instead of on company initiatives.
  •  It is a marketing campaign that results in unmeasurable revenue and no focused customer insights. 
  • It is the misallocation of employee’s knowledge and potential disillusionment with the organization.
  • At its worst, it is the abandonment of your trusted customers.

Understanding the right information to collect and anticipating the future expectation to not only access, but also aggregate data in a meaningful way, is paramount to enduring success in this new “big data” era. Good data also translates into strong decision making. When an organization has access to critical consumer information or insights into market tendencies, it is equipped to make decisions that increase revenue, market share and operational efficiencies. When meaningful data is presented timely and in an easy-to-digest manner, executives can react quickly to changes affecting the organization, rather than waiting until the end of the quarter or the next strategic planning meeting.

Financial institutions that want to avoid marketing mishaps and the associated blows to their brand need to shift away from data silos and place a greater emphasis on their data quality. Providing departments across the bank with an accurate view of customers is essential to meeting their evolving needs. Institutions that ignore the growing importance of data quality risk losing customers and becoming irrelevant in today’s digital environment. Precise, up-to-date marketing and communication to your customers begins and ends with access to current and relevant data.

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.