How to Attract Consumers in the Face of a Recession

Fears of a recession in the United States have been growing.

For the first time since 2020, gross domestic product shrank in the first quarter according to the advance estimate released by the Bureau of Economic Analysis. Ongoing supply chain issues have caused shortages of retail goods and basic necessities. According to a recent CNBC survey, 81% of Americans believe a recession is coming this year, with 76% worrying that continuous price hikes will force them to “rethink their financial choices.”

With a potential recession looming over the country’s shoulders, a shift in consumer psychology may be in play. U.S. consumer confidence edged lower in April, which could signal a dip in purchasing intention.

Bank leaders should proactively work with their marketing teams now to address and minimize the effect a recession could have on customers. Even in times of economic uncertainty, it’s possible to retain and build consumer confidence. Below are three questions that bank leaders should be asking themselves.

1. Do our current customers rate us highly?
Customers may be less optimistic about their financial situations during a recession. Whether and how much a bank can help them during this time may parlay into the institution’s Net Promoter Score (NPS).

NPS surveys help banks understand the sentiment behind their most meaningful customer experiences, such as opening new accounts or resolving problems with customer service. Marketing teams can use NPS to inform future customer retention strategies.

NPS surveys can also help banks identify potential brand advocates. Customers that rate banks highly may be more likely to refer family and friends, acting as a potential acquisition channel.

To get ahead of an economic slowdown, banks should act in response to results of NPS surveys. They can minimize attrition by having customer service teams reach out to those that rated 0 to 6. Respondents that scored higher (9 to 10) may be more suited for a customer referral program that rewards them when family and friends sign up.

2. Are we building brand equity from our customer satisfaction?
Banks must protect the brand equity they’ve built over the years. A two-pronged brand advocacy strategy can build customer confidence by rewarding customers with high-rated NPS response when they refer individual family and friends, as well as influencers who refer followers at a massive scale.

Satisfied customers and influencer partners can be mobilized through:

Customer reviews: Because nearly 50% of people trust reviews as much as recommendations from family, these can serve as a tipping point that turns window-shoppers into customers.

Trackable customer referrals: Banks can leverage unique affiliate tracking codes to track new applications by source, which helps identify their most effective brand advocates.

3. What problems could our customers face in a recession?
Banks vying to attract new customers during a recession must ensure their offerings address unique customer needs. Economic downturn affects customers in a variety of ways; banks that anticipate those problems can proactively address them before they turn into financial difficulties.

Insights from brand advocates can be especially helpful. For instance, a mommy blogger’s high referral rate may suggest that marketing should focus on millennials with kids. If affiliate links from the short video platform TikTok are a leading source of new customers, marketing teams should ramp up campaigns to reach Gen Z. Below are examples of how banks can act on insights about their unique customer cohorts.

Address Gen Z’s fear of making incorrect financial decisions: According to a Deloitte study, Gen Z fears committing to purchases and losing out on more competitive options. Bank marketers can encourage their influencer partners to create objective product comparison video content about their products.

Offer realistic home-buying advice to millennials: Millennials that were previously held back by student debt may be at the point in their lives where their greatest barrier to home ownership is easing. Banks can address their prospects for being approved for a mortgage, and how the federal interest rate hikes intersect with loan eligibility as well.

Engage Gen X and baby boomer customers about nest eggs:
Talks of recession may reignite fears from the financial crisis of 2007, where many saw their primary nest eggs – their homes — collapse in value. Banks can run campaigns to address these concerns and provide financial advice that protects these customers.

Banks executives watching for signs of a recession must not forget how the economic downturn impacts customer confidence. To minimize attrition, they should proactively focus on building up their brand integrity and leveraging advocacy from satisfied customers to grow customer confidence in their offerings.

7 Ways Banks Can Benefit From Data Analytics

A version of this article originally appeared on the KlariVis blog.

There is a pervasive data conundrum throughout the financial services industry: Banks have an inordinate amount of data, but antiquated and siloed solutions are suppressing incredible, untapped opportunities to use it.

Data analytics offer banks seven distinct and tangible benefits; it’s essential that they invest adequate time and resources into finding the right solution.

1. Save Valuable Time
Time is money. Investing in data analytics can streamline operations and saves employees time. The right solution organizes data, eliminates spreadsheets, freeing up the gray space in any organization. Employees can quickly locate what they’re looking for, allowing them to focus on the tasks that are most meaningful to the institution. Instead of organizing and sifting through data, they can spend more time analyzing the information, making strategic decisions and communicating with customers.

2. Secure Compliance, Risk Management Features
Data analytics improves overall bank security. The regulatory environment for financial institutions is complex, and regulatory non-compliance can lead to major fines or enforcement actions for banks. Data analytics incorporates technology into the compliance and risk management processes, improving bank security by reducing the likelihood of human error and quickly detecting potential cases of fraud.

3. Increase Visibility
Data silos in banks are often a result of outdated data solutions. Additionally, granting only a few people or departments access to the full set of data can lead to miscommunication or misinformation. Data analytics solutions, such as enterprise dashboards, give financial institutions the ability to see their full institution clearly. Everyone having access to the same information — whether it be individual branch performance or loan reports —improves customer service, internal communication and overall efficiency.

4. Cut Down on Costs
There is a high cost of bad data. Bad data can be inaccurate, duplicative, incomplete, inaccessible or unusable. Banks that aren’t storing or managing collected data appropriately could be wasting valuable company resources. They could also incur bad data costs through inconclusive, expensive marketing campaigns, increased operational costs that distract employees from important initiatives or customer attrition. By comparison, an updated enterprise data solution keeps employees up-to-date and can reveal new growth opportunities.

5. Create Detailed Customer Profiles
All financial institutions want to know their customers better. Data analytics help generate detailed profiles that reveal valuable information, such as spending habits and channel preferences. Banks can create highly specific segments with these profiles and pinpoint timely cross-selling opportunities. The right data solution makes it easier to gather actionable insights that improve customer experience and increase profitability.

6. Empower Employees and Customer Experience
Empowered employees improve the customer experience; happier customers contribute to empowering employees. A powerful part of this cycle is data analytics. Data analytics produce actionable insights that save employees’ time so they can focus on what’s important. Banks can send timely, data-based relevant messaging, based on customer-expressed preferences and interests.

7. Improve Performance
More time spent connecting with customers allows employees to build a deeper understanding of their financial needs and ultimately improve the bank’s performance. The right data analytics solution leads to a more productive and profitable financial institution. In this increasingly competitive financial landscape, employee and customer experience are vital to every financial institution. Customers expect seamless communication and digital experiences that are secure and intuitive; employees appreciate work environments where their work contributes to its overall success. Using data to its fullest potential allows banks to make better strategic decisions, identify and act upon growth opportunities, and focus on their customers.

How to Keep Existing Customers Happy

Many consumers already have an established relationship with a trusted bank that provides familiarity and a sense of reliability. If they find value in the bank’s financial support, they tend to stick around.

That makes existing customers essential to a bank’s future growth. However, in today’s landscape, many financial institutions focus on acquiring new customers, rather than satisfying the needs of their existing customer base. Data shows that although existing customers make up 65% of a company’s business, 44% of companies focus on customer acquisition, while only 16% focus on retention.

While acquiring new customers is vital to the growth of a financial institution, it is crucial that the existing customers are not left behind. Nurturing these relationships can produce significant benefits for an organization; but those who struggle to manage what is in house already will only compound the issues when adding new customers.

While acquiring customers is important to growing portfolios, loyal customers generate more revenue every year they stay at a bank. New customers might be more cautious about purchasing new products until they are comfortable with the financial institution. Existing clients who are already familiar with the bank, and trust and value their products, tend to buy more over time. This plays out in other sectors as well: Existing customers are 50% more likely to try new products and spend 31% more, on average, compared to new customers, according to research cited by Forbes.

Existing customers are also less costly as they require less marketing efforts, which frees up resources, time, and costs. New customer acquisition costs have increased by almost 50% in the past five years, which means the cost of acquiring a new customer is about seven times that of maintaining an existing relationship.

Additionally, loyal customers act as mini marketers, referring others to their trusted institution and increasing profit margins without the bank having to advertise. According to data, 77% of customers would recommend a brand to a friend after a single positive experience. This word-of-mouth communication supplements bank marketing efforts, freeing up resources for the customer acquisition process.

So how can banks improve their customer retention rate?

Be proactive. Banks have more than enough data they can use to anticipate the needs of existing customers. Those that see this data as an opportunity can gain a more holistic view into their existing client base and unlock opportunities that boost retention rates. For instance, lenders can use data like relative active credit lines, income, spending patterns and life stages to cultivate a premium user experience through personalized offers that are guaranteed and readily available. A proactive approach eliminates the potential of an existing customer being rejected for a loan — which happens 21% of the time — and allows them to shop with confidence.

Promote financial wellness. Having this insight into customers also allows banks to boost retention rates through financial wellness programs that help equip them with opportunities to enjoy financial competency and stability. Did they move to a new state? Did they have a baby? Do they have a child going off to college? Banks can acknowledge these milestones in their customers’ financial lives and tailor communication and relevant recommendations that show their support, create long-lasting and trusting relationships, and help the bank become top of wallet when the customer purchases a product or service.

Put the customer in the driver’s seat. Banks can present existing customers with a menu of products and services immediately after they log onto their online banking portal. Customers can weigh a range of attractive capabilities and select what they want, rather than receive a single product that was offered to tens of thousands of prospects with hopes they are in the market. This removes the fear of rejection and confusion that can occur when applying through a traditional lending solution.

Be a true lending center. If banks want to distinguish their online and mobile banking platform as more than a place to make transfers and check balances, they must provide branch and call center staff with the tools to evolve into a true lending center for customers. Existing customers should be able to find support and guidance inside their online banking accounts, apply for and receive appropriate products, make deposits, and so much more from the palm of their hand.

To remain a standard in their communities, banks must recognize the true value behind customer retention. This can help banks not only secure a prime spot in its customers’ financial lives but grow loan portfolio, boost engagement and gain or retain a strong competitive edge.

How Embedded Compliance Plays the Game to Win, Not Break Even

Imagine a game where your team can’t score points and there’s no such thing as winning. All you can do is meticulously follow the rules; if you follow them well enough, then your team doesn’t lose. Most banks approach compliance with this survival mindset and it shows.

According to the Federal Reserve Bank of St. Louis, compliance expenses account for 7% of banks’ non-interest expenses. The majority of that spend is typically directed at headcount distributed across siloed operational functions — using equally siloed technology — to get the job done during the last leg of a transaction. The best that can be said for this approach is that it achieves baseline compliance. The worst? It prevents institutions from investing in transaction data management strategies that deliver compliance while simultaneously driving efficiencies and business growth that show up on the bottom line. This scenario becomes more untenable with each passing year: Increasing compliance complexity drives up costs, and that diversion of investment erodes a bank’s ability to compete.

Banks can choose to play the game differently, by viewing compliance as an integrated part of the data management process. Solutions that leverage application programming interfaces, or APIs, provide a mechanism for technology components to communicate with each other and exchange data payloads. Outside of this approach, transaction data resides in bifurcated systems and requires extra handling, either by software or human intervention, to complete a transaction and book the right data to the core. Having the same data in multiple systems and rekeying data dramatically increase an institution’s risk profile. Why make it harder to “not lose” the game when banks can leverage API-first solutions to ensure that data is only collected once and passes through to the touchpoints where it’s needed? The key to unlocking this efficiency is a compliance architecture that separates the tech stack from the compliance stack. Otherwise, banks are obliged to wait for code changes every time compliance updates are pushed.

Mobile enablement is now as critical for a bank’s success as any product it offers. The customers that banks are trying to reach have no practical limit to their financial services options and are increasingly comfortable with contact-free experiences. According to studies from J.D. Power & Associates released this year, 67% of U.S. bank retail customers have used their bank’s mobile app and 41% of bank customers are digital-only customers. Given historical trends, those numbers are expected to only increase.

Compliance represents an opportunity to remove friction from the mobile banking experience, whether offered through an app or a website. Traditional PDF documents are designed for in-branch delivery and are a clumsy fit for the mobile world. Responsive design applies to compliance content no less than it applies to mobile apps; content needs to adjust smoothly to fit the size of the viewing screen. The concept of “document package” is evolving to the point where a “compliance package” should be constructed on responsive design principles and require minimal user clicks to view and acknowledge the content.

An embedded compliance solution should treat optimized mobile channels as table stakes. To survive and thrive in this environment, institutions need to be where their customers are, when they are there. Traditional banker’s hours have officially gone the way of the dodo.

Embedded compliance can also enhance bank data security in the event of a breach. It is difficult to overstate the reputational damage that results from a data breach. Embedded compliance offers critical safeguards for sensitive customer information, bolstering an institution’s overall security profile. Legacy compliance or document-prep solutions often require duplicate data entry and expose customer personal identifiable information to the inherent data breach risks that come with multiple databases scattered across technology platforms. Look for solutions that do not store PII data, and instead offer bi-directional integrations with your platform.

Increasing demand for digital engagement provides banks with opportunities to rethink their technology stacks. Management should evaluate each component for its potential to address a myriad of business needs. Compliance solutions can sharpen or dull a bank’s competitive edge and should be considered part of a strategic plan to grow business. Who knows, maybe someday compliance will actually become “cool”? A dreamer can dream.

The Corporate Banking Conundrum and the Massive Digitization Opportunity

Corporate banking makes up nearly a third of the average bank’s total lending operations. So, it is surprising that institutions don’t consider it among their core banking activities, especially given the need to digitize their front and back-end processes.

Corporate banking encompasses a large portfolio of services, including cash management, trade finance, risk management, transaction services and corporate finance services. At some banks, nearly 20% of their underlying book value is dedicated entirely to corporate banking activities. There are many moving pieces, which can make it difficult to optimize and digitize, especially for banks with a large number of corporate clients.

Corporate onboarding is an important and highly complicated process, with unique complexities for each bank. From the corporate customer perspective, the time needed to onboard, resolution turnaround time and customer experience are the most valuable areas — and require the most improvement. According to a recent Fenergo survey, 81% of bank C-suite executives believe poor data management lengthens onboarding and negatively affects customer experience. Improving how banks onboard corporate clients has a variety of benefits.

  • Reduce Time-to-revenue: Banks are keen to onboard new customers quickly to maximize income and profit. A faster setup means greater potential for revenue generation through various lending products.
  • Improve Customer Experience and Loyalty: An efficient customer onboarding process is crucial to secure loyal, lifelong relationships with corporate clients.
  • Streamline and Standardize Compliance: Anti-money laundering, Know Your Customer and other regulatory compliance obligations can be effectively automated internally and cross-country.

From a bank’s perspective, getting the right information, accounting for risk, and managing customer lifecycles is not only important – it is a differentiator. But we still find, right from the start of the customer journey, that tasks are excessively manual and turnaround time is alarmingly long: lacking even the most basic digital optimization, it can take between 90 and 120 days for corporate customer onboarding.

In corporate banking, a key area of concern is time. The traditional model of account onboarding and relationship management is far too labor intensive: collecting documents and navigating through tedious elements of their bank’s internal process flows, among other tasks. This time could be used for  meaningful and insightful interactions with the clients and enabling transaction for the customer.

Digitizing onboarding processes allows RMs more time to interact with clients. Digital channels can provide additional ways to connect with and closely serve clients. Applying artificial intelligence (AI) and machine learning (ML) to administrative and analytical tasks not only improve RM productivity, but provide a new perspective on customer service.

Digitized information leads to digitalization of the entire corporate onboarding process. Relationship portfolio management is the glue that holds it all together.

How to Attack the Corporate Banking Behemoth

Step 1: Adopt a digital technology framework to deliver end-to-end digitalization across customer lifecycle. This allows the bank to capture information from unstructured and structured sources using optical character recognition software (OCR), among other software solutions. As a result, making this information available digitally across stakeholders.

Step 2: Remove the friction between bank data sources, then automate the process flow with lean principles. This helps ease data enrichment by addressing any adverse or inadequate information upfront.

Step 3: Be proactive and manage risk.

Risk management has changed substantially over the past decade. Regulations that emerged from the global financial crisis and levied fines triggered a wave of change in risk functions. These included more detailed and demanding capital, leverage, liquidity, and funding requirements, as well as higher standards for risk reporting.

For risk functions to thrive during this period of fundamental transformation, banks need to proactively rebuild them. To succeed, banks must start now with a portfolio of initiatives, such as digital underwriting, the incorporation of AI and machine learning techniques and interactive risk reporting, that align short-term business cases with the long-term target vision. These improvements should be complemented by a shift in recruiting toward more technology-savvy profiles or the introduction of data lakes.

Prioritize natively integrated systems and gain deep insight into the portfolio with real-time metrics reflecting transactions, positions and risk exposure data. Slash costs by simplifying legacy systems, taking SaaS beyond the cloud, and adopting robotics and AI. Build technological capabilities that force the bank to be more intelligent around customers’ needs. Look for more advanced analytic tools with best-in-class road mapping and reporting functionality.

Banks are scrambling to catch up to the emerging demands of consumers in this digitally driven and rapidly evolving ecosystem. The commercial banking space has been buzzing around advancements in digitizing and automating processes, with clear benefits to boast. It’s time corporate banking joined them.

By 2025, risk functions in banks will need to be fundamentally different than today. The next decade in risk management may be subject to more transformation than the last one. Unless banks act now and prepare for these longer-term changes, they will continue to find themselves overwhelmed by new requirements and emerging demands.

The Role Analytics Play in Today’s Digital Environment

Banks have an increasing opportunity to employ and leverage analytics as customers continue to seek increased digital engagement. Combining data, analytics, and decision management tools together enriches executive insights, quantifies risk and opportunity, and makes decision‑making repeatable and consistently executed.

Analytics, and the broad, umbrella phrase automated intelligence can be confusing; there are many different subfields of the phrases. AI is the ability of a computer to do tasks that are regularly performed by humans. This includes expert models that take domain knowledge and automate decisions to replicate the decisions the expert would have made, but without human intervention. Machine learning models extract hidden patterns and rules from large datasets, making decisions based purely on the information reflected in the data.

Financial institutions can use this technology to better understand their data, get more value out of the information they already have and make predictions about consumer behaviors based on the data.

For example, having identified the needs of two consumers, digital marketing analytics can identify the consumer with the greater propensity-to-purchase or which consumer has the more-complex needs to determine resources allocation. These consumers may present equal opportunity, or they may vary by a factor or two. It’s also important to employ analytic tools that extend beyond determining probability to recommending actions based on results. For example, a customer could submit necessary credit information that is sufficient for a lender to receive an instant decision recommendation, increasing customer satisfaction by reducing wait time.

While there are countless ways banks can benefit from implementing analytics, there are eight specific areas where analytics has the most impact:

  • Measuring the degree of risk by evaluating credit, customer fraud and attrition;
  • Measuring the likelihood or probability of consumer behaviors and desires;
  • Improving customer engagement by increasing the relevance of engagement content as well as reaching out to customers earlier in the process;
  • Providing insight into the success or failure in the form of marketing, customer and operational key performance indicator;
  • Detecting and measuring opportunity in terms of customer acquisition, revenue expansion and resource/priority allocation;
  • Optimizing pricing;
  • Improving decisions based on credit, campaign, alerts or routing escalation; and
  • Determining intervention or corrective next action to reduce abandonment.

Each of these capabilities has numerous applications. In a digital economy, the entire customer journey and sales cycle becomes digitally concentrated. This includes using personal financial goal planning, market segmentation, customer relationship management data and website digital sensory to detect opportunities based on consumer intent, fulfillment, obtaining customer self‑reported feedback, attrition monitoring and numerous engagement methods like education or offers. Using analytics adds considerable value to each of these processes — it drives some of them completely. Actionable analytics are key. They drive outcomes based on expert models and data analysis, to scale, to a large set of consumers without increasing the need for additional employees.

Looking at actual business cases will underline the benefits of analytics in relation to propensity‑to‑purchase (PTP), email campaigns and website issue detection. When two different customers visit a bank’s website, the bank can use analytics to detect and measure each user’s navigation for probable interest and intent for new products based on time on page, depth of navigation and frequency signals within a given timeframe. If one person visits a general product page and only stays for 15 seconds, that person has a lower PTP than the other visitor who navigates to specific product and pricing information and remains there for 40 seconds.

The bank can route probable leads to either human‑based or automated engagement plans, based on aggregated data, segmentation, product intent, and in the case of an existing customer, current products owned.

A recent college graduate may be interested in debt management solutions, whereas a more-established empty nester may be in the market for wealth management and retirement planning. Based on user preferences and opportunity cost, these customers can be properly engaged with offers, education and helpful tools through email campaigns, texts, third‑party marketing or branch or contact center personnel.

In today’s banking environment, financial institutions must find new ways to increase efficiency, improve business processes and scale to consumer volume. Analytics support financial institutions in forecasting, risk management and sales by providing data points that help them increase performance, predict outcomes and better solve business issues.

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.

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.

Focus On This One Area To Position Your Bank For Success


data-12-27-18.pngWhether it’s compliance with forthcoming regulations or simply giving your customers an enjoyable experience, banks are realizing that one thing is central to achieving successful results across their operations.

Data management and governance has become a central element for banks positioning themselves for the future in a digital-first world and as new credit reporting requirements, like the current expected credit loss (CECL) provisions, are put into effect.

Banks that embrace and establish a robust data governance process will be better positioned to accomplish its strategic initiatives, whether they be in customer acquisition or relationship management, or with efficiently meeting the new accounting standards.


customer-12-27-18-tb.pngHow Banks Can Make Use of Data-Driven Customer Insight
Most are familiar with the algorithms and machine learning employed by big tech companies like Google, Netflix and Amazon. Banks are beginning to employ similar strategies as the competition for new customers and new deposits remains high.

data-12-17-18-tb.pngFrom CRM to CECL: Why Improved Data Governance Is Imperative for Your Bank
Banks know they have mountains of data about their customers that can help deliver attractive experiences on a variety of platforms. But data governance is not only about controlling large volumes of data, it’s about creating trust in the quality of data.

Poor data governance practices can lead to poor decision making by bank management, which is a risk no institution can afford.

No matter what lies ahead for your bank, how your institution manages and utilizes data will be an essential piece to its strategic initiatives and goals.

Now Is The Time to Use Data The Right Way


data-6-29-18.pngMost bankers are aware of the changes that are forthcoming in accounting standards and financial reporting for institutions of all sizes, but few are fully prepared for the complete implementation of all of the details in the new current expected credit loss (CECL) models that will take effect over the next few years.

Banks that act now to effectively and strategically collect, manage and utilize data for the benefit of the institution will be better positioned to handle the new accounting requirements under CECL and evolving regulations with state and federal agencies.

Here are three articles that cover key areas where your board should focus its attention before the rules take effect.


credit-data-6-29-18.pngCredit Data Management
Under Dodd-Frank, the law passed in the wake of the financial crisis, banks of all sizes and those especially in the midsize range of $10 billion to $50 billion in assets were required to do additional reporting and stress testing. Those laws have recently been changed, but many institutions in that asset category are opting to continue some form of stress testing as a measure of sound governance. Managing credit data is a key component of those processes.

management-6-29-18.pngCentralizing Your Data
Bank operations are known to be siloed in many cases as a matter of habit, but your data management can be done in a much more centralized manner. Doing so can benefit your institution, and ease its compliance with regulations.

CECL-6-29-18.pngGet Ready for CECL Now
The upcoming implementation of new CECL standards has many banks in a flurry to determine how those calculations will be developed and reported. Few are fully ready, but it is understood that current and historical loan level data attributes will be integral to those calculations.