Steps for Managing and Leveraging Data

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Does your institution rely on manual processes to handle data?

Institutions today generate vast amounts of data that come in different forms: transactional data such as deposit activity or loan disbursements, and non-transactional data such as web activity or file maintenance logs. When employees handle data manually, through mouse and keyboard, it puts your institution at risk for inefficient reporting, security threats and, perhaps most importantly, becoming obsolete to your customers.

Take a look at how data is moved through your organization. Exploring targeted improvements can result in actionable, timely insights and enhanced strategic decision-making.

First, focus on areas that may have the biggest impact, such as a process that consumes outsized amounts of time, staff and other resources.

What manual processes exist in your institution’s day-to-day business operations? Can board reporting be streamlined? Do directors and executives have access to meaningful, current data? Or should the institution explore a process that makes new opportunities possible, like improving data analytics to learn more about customer engagement?

Build Your Data Strategy
Crawl — The first step toward effectively managing data is to take stock of what your bank currently has. Most institutions depend on their core and ancillary systems to handle the same information. Various inputs go into moving identical data, like customer or payment information, from one system to another — a process that often involves spreadsheets. The issue of siloed information grows more prominent as institutions expand their footprint or product offering and adopt new software applications.

It’s helpful for directors and executives to ask themselves the following questions to take stock:

  • What is our current data strategy?
  • Does our data strategy align with our broader institution strategy?
  • Have we identified pain points or areas of opportunity for automation?
  • Where does our data reside?
  • What is missing?
  • In a perfect world, what systems and processes would we have?

Depending on complexity, it is likely a portion of the bank’s strategy will look at how to integrate disparate systems. While integration is an excellent start, it is only a means to an end in executing your bank’s broader digital strategy.

Prioritize ROI Efforts and Execute
Walk — Now that the bank has developed a plan to increase its return on investment, it is time to execute. What does that look like? Executives should think through things like:

  • If I could improve only one aspect of my data, what would that be?
  • What technical skills are my team lacking to execute the strategy?
  • Where should I start: build in-house or work with a third party?
  • Are there specific dashboards or reports that would be transformational for day-to-day business operations and strategic planning?
  • What digital solutions do our customers want and need?

Enable Self-Service Reporting
Run — The end goal of any bank’s data strategy is to help decision-makers make informed choices backed by evidence and objectivity, rather than guesswork and bias.

Innovative institutions have tools that make reporting accessible to all decision makers. In addition to being able to interact with data from multiple systems, those tools provide employees with dashboards that highlight key metrics and update in real time, generating the pulse of organizational performance.

With the combination of self-service reporting and data-driven dashboards, leaders have the means to answer tough questions, solve intractable challenges and understand their institution in new ways. It’s a transformative capability — and the end goal of any effort to better manage data.

The information contained herein is general in nature and is not intended, and should not be construed, as legal, accounting, investment, or tax advice or opinion provided by CliftonLarsonAllen LLP (CliftonLarsonAllen) to the reader.

CECL Model Validation Benefits Beyond Compliance

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The current expected credit loss (CECL) adoption deadline of Jan. 1, 2023 has many financial institutions evaluating various models and assumptions. Many financial institutions haven’t had sufficient time to evaluate their CECL model performance under various stress scenarios that could provide a more forward-looking view, taking the model beyond just a compliance or accounting exercise.

One critical element of CECL adoption is model validation. The process of validating a model is not only an expectation of bank regulators as part of the CECL process — it can also yield advantages for institutions by providing crucial insights into how their credit risk profile would be impacted by uncertain conditions.

In the current economic environment, financial institutions need to thoroughly understand what an economic downturn, no matter how mild or severe, could do to their organization. While these outcomes really depend on what assumptions they are using, modeling out different scenarios using more severe assumptions will help these institutions see how prepared they may or may not be.

Often vendors have hundreds of clients and use general economic assumptions on them. Validation gives management a deeper dive into assumptions specific to their institution, creating an opportunity to assess their relevance to their facts and circumstances. When doing a validation, there are three main pillars: data and assumptions, modeling and stress testing.

Data and assumptions: Using your own clean and correct data is a fundamental part of CECL. Bank-specific data is key, as opposed to using industry data that might not be applicable to your bank. Validation allows for back-testing of what assumptions the bank is using for its specific data in order to confirm that those assumptions are accurate or identify other data fields or sources that may be better applied.

Modeling (black box): When you put data into a model, it does some evaluating and gives you an answer. That evaluation period is often referred to as the “black box.” Data and assumptions go into the model and returns a CECL estimate as the output. These models are becoming more sophisticated and complex, requiring many years of historical data and future economic projections to determine the CECL estimate. As a result of these complexities, we believe that financial institutions should perform a full replication of their CECL model. Leveraging this best practice when conducting a validation will assure the management team and the board that the model the bank has chosen is estimating its CECL estimate accurately and also providing further insight into its credit risk profile. By stripping the model and its assumptions down and rebuilding them, we can uncover potential risks and model limitations that may otherwise be unknown to the user.

Validations should give financial institutions confidence in how their model works and what is happening. Being familiar with the annual validation process for CECL compliance will better prepare an institution to answer all types of questions from regulators, auditors and other parties. Furthermore, it’s a valuable tool for management to be able to predict future information that will help them plan for how their institution will react to stressful situations, while also aiding them in future capital and budgeting discussions.

Stress testing: In the current climate of huge capital market swings, dislocations and interest rate increases, stress testing is vital. No one knows exactly where the economy is going. Once the model has been validated, the next step is for banks to understand how the model will behave in a worst-case scenario. It is important to run a severe stress test to uncover where the institution will be affected by those assumptions most. Management can use the information from this exercise to see the connections between changes and the expected impact to the bank, and how the bank could react. From here, management can gain a clearer picture of how changes in the major assumptions impact its CECL estimate, so there are no surprises in the future.

Understanding Customers’ Finances Strengthens Relationships

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As the current economy shifts and evolves in response to inflationary pressures, and consumer debt increases, banks may encounter an influx of customers who are accruing late charges, overdue accounts and delinquencies for the first time in nearly a decade.

Banks have not been accustomed to seeing this level of volume in their collections and recovery departments since the Great Recession and have not worked with so many customers in financial stress. To weather these economic conditions, banks should consider automated systems that help manage their collections and recovery departments, as well as guide and advise customers on how to improve their financial health and wellbeing. Technology powered with data insights and automation positions banks to successfully identify potential weakness early and efficiently reduce loan losses, increase revenue, minimize costs and have the data insights needed to help guide customers on their financial journey.

Consumer debt increased $52.4 billion in March, up from the increase of nearly $40 billion the previous month. Financial stress and money concerns are top of mind for many households nationwide. According to a recent survey, 77% of American adults describe themselves as anxious about their financial situation. The cause of the anxiety vary and stem from a wide range of sources, including savings and retirement to affording a house or child’s education, everyday bills and expenses, paying off debt, healthcare costs and more.

While banks traditionally haven’t always played a role in the financial wellness of their customers, they are able to see patterns based on customer data and transactional history. This viewpoint enables them to serve as advisors and help their customers before they encounter a problem or accounts go into delinquency. Banks that help their customers reduce financial stress wind up strengthening the relationship, which can entice those customers into using additional banking services.

Using Data to Understand Customer’s Financial Health
By utilizing data insights, banks can easily identify transaction and deposit patterns, as well as overall expenses. This allows banks to assess their customer risk more efficiently or act on collections based on an individual’s level of risk and ability to pay; it also shows them the true financial health of the customer.

For example, banks can identify consumers in financial distress by analyzing deposit account balance trends, identifying automated deposits that have been reduced or stopped and identify deposit accounts that are closed. Banks can better understand a consumer’s financial health by collecting, analyzing and understanding patterns hidden in the data.

When banks identify potentially stressed customers in advance, it can proactively take steps to assist customers before loans go delinquent and accounts accrue late fees. Some strategies to accommodate customers facing delinquency include offering free credit counseling, short-term or long-term loan term modifications, and restructuring or providing loan payment skip offers. This type of assistance not only benefits the financial institution — it shows customers they are valued, even during tough economic times.

Data enables banks to identify these trends. But they can better understand and utilize the data when they integrate it into the workflow and apply automation, ultimately reducing costs associated with the management of delinquencies, loss mitigation and recoveries and customer relationship management. A number of banks may find that their outdated, manual systems lack the scalability and effectiveness they’ll need to remain competitive or provide the advice and counsel to strengthen customer relationships.

Banks are uniquely positioned to help consumers on their journey to improve their financial situation: They have consumer information, transaction data and trust. Banks should aim to provide encouragement and guidance through financial hardships, regardless of their customers’ situation. Augmenting data analysis with predictive technology and automated workflows better positions banks to not only save money but ensure their customers’ satisfaction.

How to Attract Consumers in the Face of a Recession

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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

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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

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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

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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

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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

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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

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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.