Transforming, Optimizing Bank Finance Functions

Written by


Banks can optimize their finance functions to go beyond compliance and drive performance and results. Creating a layer of functionality on top of the general ledger allows executives to apply behavior and risk data with an eye toward improving profitability and forecasting without replacing their core. Will Newcomer, vice president of business development and strategy at Wolters Kluwer, and Bill Collette, managing director of financial services solutions at Wolters Kluwer, share what kind of applications and analytics executives could use to drive measurement, accuracy and accountability. Topics include:

  • Trends in Transformation
  • Uses of Finance Analytics
  • Best Practices for Transformation

Banks can improve measurement, accuracy and accountability by leveraging their existing core and finance functions.

Data is the Secret Weapon for Successful M&A

Written by

The topic of data and analytics at financial institutions typically focuses on how data can be used to enhance the consumer experience. As the volume of M&A in the banking industry intensifies to 180 deals this year, first-party data is a critical asset that can be leveraged to model and optimize M&A decisions.

There are more than 10,000 financial institutions in the U.S., split in half between banks and credit unions. That’s a lot of targets for potential acquirers to sift through, and it can be difficult to determine the right potential targets. That’s where a bank’s own first-party data can come in handy. Sean Ryan, principal content manager for banking and specialty finance at FactSet, notes that “calculating overlap among branch networks is simple, but calculating overlap among customer bases is more valuable — though it requires much more data and analysis.” Here are two examples of how that data can be used to model and select the right targets:

  • Geographic footprint. There are two primary camps for considering footprint from an M&A perspective: grabbing new territory or doubling down on existing serving areas. Banks can use customer data to help determine the optimal targets for both of these objectives, like using spend data to understand where consumers work and shop to indicate where they should locate new branches and ATMs.
  • Customer segmentation. Banks often look to capturing market share from consumer segments they are not currently serving, or acquire more consumers similar to their existing base. They should use data to help drive decision-making, whether their focus is on finding competitive or synergistic customer bases. Analyzing first-party transaction data from a core processor can indicate the volume of consumers making payments or transfers to a competitor bank, providing insights into which might be the best targets for acquisition. If the strategy is to gain market share by going after direct competitors, a competitive insight report can provide the details on exactly how many payments are being made to a competitor and who is making them.

The work isn’t done when a bank identifies the right M&A target and signs a deal. “When companies merge, they embark on seemingly minor changes that can make a big difference to customers, causing even the most loyal to reevaluate their relationship with the company,” writes Laura Miles and Ted Rouse of Bain & Co. With the right data, it is possible that the newly merged institution minimizes those challenges and creates a path to success. Some examples include:

  • Product rationalization. After a bank completes a merger, executives should analyze specific product utilization at an individual consumer or household level, but understanding consumer behavior at a more granular level will provide even greater insights. For example, knowing that a certain threshold of consumers are making competitive mortgage payments could determine which mortgage products the bank should offer and which it should sunset. Understanding which business customers are using Square for merchant processing can identify how the bank can make merchant solutions more competitive and which to retain post-merger. Additionally, modeling the take rate, product profitability and potential adoption of the examples above can provide executives with the final details to help them make the right product decisions.
  • Customer retention. Merger analysis often indicates that customer communication and retention was either not enough of a focus or was not properly managed, resulting in significant attrition for the proforma bank. FactSet’s Ryan points out that “too frequently, banks have been so focused on hitting their cost save targets that they took actions that drove up customer attrition, so that in the end, while the buyer hit the mark on cost reductions, they missed on actual earnings.” Executives must understand the demographic profiles of their consumers, like the home improver or an outdoor enthusiast, along with the life events they are experiencing, like a new baby, kids headed off to college or in the market for a loan, to drive communications. The focus must be on retaining accountholders. Banks can use predictive attrition models to identify customers at greatest risk of leaving and deploy cross-sell models for relationships that could benefit from additional products and services.

M&A can be risky business in the best of circumstances — too often, a transaction results in the loss of customers, damaged reputations and a failure to deliver shareholder value. Using first-party data effectively to help drive better outcomes can ensure a win-win for all parties and customers being served.

How a Data-Driven Sales Methodology Can Help Banks Grow

Written by

Two issues are challenging banks to capitalize on any sales momentum and risk sales inertia. Without data and analytics, banks will struggle to scale sales methodology and grow revenue — even if they have an effective sales methodology and highly trained team in place.

Current market dynamics are creating a new set of obstacles for financial institutions to meet commercial revenue targets in the face of economic uncertainty and deteriorating industries and sub-segments. In addition, frontline sales resources at banks have become consumed by servicing and monitoring activities as institutions refocused relationship managers to portfolio management during the coronavirus pandemic

While banks might experience short-term growth, they will struggle to find long-term success given the absence of a focused, cost-effective and scalable process aimed at the ideal, targeted customer. That’s because the traditional, historical methods for selling are largely ineffective in today’s environment. Commercial banking sales have been rooted in selling through relationships and networking with “centers of influence,” such as accountants or attorneys. This is challenging approach in today’s climate because of a lack of a methodical plan to expand, repopulate, curate and filter the network on an ongoing basis to insure ample and effective referrals. The financial results prove this out with historically low win rates, sporadic cross-sale success and — in many cases — heightened levels of sales personnel attrition.

Without a standardized methodology, banks are generally unable to unlock the magnitude of their organization. Sales efforts are not repeatable and must be reinvented with each new sale, proving both costly and ineffective in business development. Without scalability, as banks grow inorganically, these challenges compounded and complicate further growth.

Banks have historically failed to leverage their disparate data sources to drive the methodology and optimize execution of sales plans. It is nearly impossible for bankers to identify and prioritize relationships in a meaningful way, given how data is typically stored in disparate data houses across multiple non-integrated systems.

The lack of coordination around data means that banks typically fail to effectively, easily and accurately align product revenue, whether interest or fee income, to an individual borrower or relationship. Executives face a challenge in planning and segmenting holistic, high-opportunity sales calls through proper segmentation and targeted sales activities without a clear understanding of the 360-degree profitability view.

Why is this important? Now more than ever, banks require a new scalable method to effectively identify, pursue, and sell to targeted existing and new prospective clients who offer new opportunities within optimal, performing industry segments.

A data-driven sales model is the key to scalability. Scalability is a sought-after state of operations, and provides the foundation for rapid, cost-effective growth. At the core of scalability is repeatability — the ease with which results can be reproduced even as bank operations change and adapt to varying conditions. Scalability is often made possible through technology enablement while leveraging automation.

Banks have explored scalability through technology and tools such as loan origination systems, base level CRM systems, and integrated third-party tools to automate the credit underwriting and scoring processes. But this alone does not create or generate scalability. Scalability is constructed by standardized, streamlined policies and procedures, and is evidenced by its repeatability and simplicity.

Scalable organizations benefit from economies of scale, processing greater volumes with fewer resources. The direct result of top line revenue growth is increased net profits and reduced operating expenses. A bank’s DNA must be central to the intersection of sales methodology and technology to drive support and insight, leading to greater scalability and accelerated growth.

In our view, banks should operate with a delivery model that leverages data and analytics, provides scalability and identifies the following: advanced customer segmentation, early-stage opportunity identification, early detection of significant cross sell opportunities and pre-defined sales targets supported by actionable and tactical workplans. With the right tool, banks also unify the sales management process and drive user adoption and experience through customized automated dashboards and reporting, accelerating success and driving sales accountability and transparency. With this approach, banks can manage relationship managers’ sales activity in ways that create scalable, sustainable sales success and ultimately achieve higher growth rates.

Marketing Campaigns Go High Tech

Written by

For years, community banks had to sit on the sidelines while the biggest banks rolled out sophisticated marketing and revenue-generating programs using artificial intelligence.

That’s no longer the case. There are now plenty of financial technology companies offering turnkey platforms tailored for community banks who can’t afford to hire a team of data analysts or software programmers.

“It’s amazing how far the industry has come in just five years in terms of products, regulatory structure and what banking means to customers,” says Kevin Tweddle, senior executive vice president for the Independent Community Bankers of America. Banks and regulators have gotten quite comfortable doing business with fintechs, choosing from a grocery cart full of options, he says.

One of the best examples of this is Huntsville, Alabama-based DeepTarget, which topped the operations category in Bank Director’s 2021 Best of FinXTech Awards. The category rewards solutions that boost efficiencies and growth.

The finalists and winners recognized in the annual awards are put through their paces in a rigorous process that examines the results generated by the growing technology provider space. For more on the methodology, click here.

DeepTarget’s 3D StoryTeller product delivers customized marketing content using 3D graphics that can be produced by a small bank or credit union without an in-house graphic design staff. Marketing messages resemble the video-rich stories on Instagram, Facebook and Snapchat, allowing the smallest financial institutions to compete with the biggest companies’ marketing campaigns.

The Ohio Valley Bank Co., the $1 billion bank unit of Ohio Valley Banc Corp. in Gallipolis, Ohio, has been using DeepTarget’s 3D StoryTeller software since October 2020, says Bryna Butler, senior vice president of corporate communications.

The bank used 3D StoryTeller to market an online portal where people could shop for cars and then apply for an auto loan through Ohio Valley Bank. From January to September of last year, that car-buying website generated just four loans. But after Ohio Valley Bank used DeepTarget’s 3D StoryTeller, the site saw a 1,289% increase in traffic. Using 3D StoryTeller translated into loans, too. Ohio Valley Bank generated 72 loans through the Auto Loan Center from October to December of 2020. Butler believes the response would have been even higher if the bank hadn’t been undercut by competitors with lower rates.

3D StoryTeller is a recent addition to DeepTarget’s line up; Ohio Valley Bank has been working with the company for about a decade. DeepTarget uses performance analytics among other options to recommend specific products and services that it believes will cater to each customer’s interests, similar to the way Facebook targets ads based on its knowledge of its users. “It’s not just scheduling ads,” Butler says. DeepTarget reports the return on investment for each campaign to the bank every month, including how many clicks translated into new account openings.

When the pandemic hit in March 2020 and the bank put its marketing plans on hold, the graphics program easily adjusted to feature messaging on how to use the bank’s digital banking or drive-thru customer service.

Although DeepTarget integrates with several cores, Butler says the software is also core-agnostic, in the sense that she can pull a CSV file on her customers and send that securely to DeepTarget.

Ohio Valley pays a small monthly fee for DeepTarget, but Butler says the software pays for itself every year. Other Best of FinXTech Awards finalists in the operations category include the marketing platform Fintel Connect, which tracks results and connects ad campaigns to social media influencers, and Derivative Path, a cloud-based solution that helps community banks manage derivative programs and foreign exchange transactions.

The Role Analytics Play in Today’s Digital Environment

Written by

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.

A Banker’s Perspective on LIBOR Transition to SOFR

Written by

The scandal associated with manipulation of the London Interbank Offered Rate (LIBOR) during the 2008 financial crisis caused a great deal of concern among banking and accounting regulators. In 2014, the Financial Stability Oversight Council recommended that U.S. regulators identify an alternative benchmark rate to LIBOR.  This recommendation was given an effective timeline in 2017 when the UK Financial Conduct Authority, as the regulator of LIBOR, announced the intent to discontinue the rate by year-end 2021. The Federal Reserve and the Alternative Reference Rates Committee (AARC) have since recommended the Secured Overnight Funding Rate (SOFR) as the recommended replacement rate for LIBOR.  Additionally, the AARC recommends that all LIBOR loan agreements cease using any LIBOR index rates by Sept. 30, 2021.

The transition to SOFR presents two distinct challenges for U.S. banks: term structure and fallback language.

Term structure: SOFR is an overnight rate, and not directly appropriate for term lending with monthly or quarterly resets. As such, several possibilities for using SOFR for term lending have emerged, with the main recommendation being Daily Simple SOFR plus a spread adjustment.  This spread adjustment is currently 12 basis points for 1-month LIBOR and 26 basis points for 3-month LIBOR, reflecting the difference between SOFR as a secured rate and LIBOR as an unsecured rate.  More importantly, Daily Simple SOFR is an arrears calculation, which is not particularly client-friendly for a standard commercial bank loan. Nevertheless, the AARC recommends that Daily Simple SOFR be used to replace LIBOR until a true term SOFR rate emerges.

SOFR vs 1-month LIBOR

Source: Federal Reserve Bank of New York

Banks are continuing to discuss options that would be easier for clients to understand on smaller bilateral loans, including prime or a historical average SOFR set at the beginning of an interest period (Figure 1). While not necessarily in-line with the cost-of-funds approximation of Daily Simple SOFR in arrears, the ability to set a rate at the beginning of an accrual period may be more appealing for client-friendly relationship banking.  Overall, the market still needs to settle on the best SOFR rate solutions for bilateral bank loans, and banks need to have a plan for using overnight SOFR until a true term SOFR rate is available.

Figure 1: Calculation Options for monthly payment

Fallback language: Most existing loan documentation is not expected to support SOFR without amendment. The AARC recommends adding “fallback language” to existing loan documents, with a very specific “hardwired” approach to using SOFR. This language defines a “waterfall” of options, depending upon what SOFR rates are available. However, many banks have also been working through a more general fallback language, to allow greater flexibility for different types of SOFR calculations as well as the use of other replacement rates. Whatever language is used, however, commercial banks are likely to have hundreds of thousands of floating-rate LIBOR loans that will need to be amended with new fallback language within the next 10 months.

In light of these issues, banks need to examine three key areas that will be affected by LIBOR replacement: documentation, systems and analytics.

Documentation: All existing LIBOR-based loans will need to be reviewed and potentially amended with appropriate fallback language before September 2021.  Amendments will require consent and signature from clients, opening the opportunity for negotiation of existing terms. Banks should have appropriate legal and banker teams working the review and amendment negotiation process with clients. And plenty of time should be allocated for these amendments to be executed and booked ahead of the fourth-quarter 2021 discontinuation of LIBOR.

Systems: All loan and trading systems that index to LIBOR will need to be re-coded to support SOFR. Most major loan system vendors have already created updates to support multiple SOFR calculations, which banks will need to install and test before re-booking amended LIBOR loans. Interfaces and downstream systems may also be impacted. Overall, a full enterprise examination of systems is required as loan systems are re-coded for the SOFR rate.

Analytics: All models — including those used for funds transfer pricing, risk adjusted return on capital and asset-liability management — will need to be rebuilt and pushed into production to support a new SOFR base rate.  Aligning the new floating rate index of SOFR with the models used internally to price funds and risk is essential to ensure that lending is evaluated appropriately.

The move from LIBOR to SOFR is now less than a year away. Bankers have generally embraced an approach to using SOFR; however, there is a great deal of work to be done on documentation, systems and models to be ready for the conversion in 2021.

What Banks Can Learn About Customers from 50,000 Chatbot Searches

Written by

Covid-19 has increased usage of digital banking services and tools, including live chat, video chat and chatbots.

While live chat and video chat offer a one-to-one conversation directly with your customers, chatbots provide 24-hour service, instant answers and the ability to scale without the need for human intervention. Relatively new channel to the banking world, the promise of chatbots seems endless: answering every question and automating related tasks, quickly and efficiently. How can banks best leverage the promise of this opportunities to better and more efficiently serve customers?

To truly answer that question, we need to understand how customers interact with chatbots, how that varies from known digital behavior, like search and navigation, and how can those insights be turned into reality.

So we decided to analyze more than 50,000 banking chatbot interactions. What we uncovered revealed some very interesting insights about customer behavior and what it will take to make that promise a reality.

It turns out that customers interactions with chatbots are very similar to human interactions:

  • They typically typed 11.24 words, on average, compared to with 1.4 words typed into a banking website search bar. Chatbot interactions are conversational. Customers ask questions like “Can I Have My Stimulus Debit Card Balance Deposited to My Account” or making statements like “I need to change my address.”
  • Almost 94% of questions asked were completely unique. While customers may ask the same type of question — “What is my routing number?” versus “What is your routing number?” versus “What is the routing number” —how they phrase the question is almost always unique.
  • A fifth of all interactions started with “I need,” “I want” or “I am” — another indication of the conversational approach that bank customers take with chatbots. Unlike a search function, where typically they would use shorter phrases like “refinance” or “refinance car,” they make statements or ask questions: “I am looking to refinance my auto loan” or “I want to refinance my auto loan.”
  • Fifteen percent of interactions included the word “how.” This is another indication that customers ask chatbots questions or looking for help completing tasks like “How do you use Zelle?” or “How does a home equity loan work?”
  • Fourteen percent of all interactions began with “Hi,” “Hey” or “Hello.” And who said that bots don’t have feelings?

Chatbot adoption and usage will only continue to grow. Like all newer channels, it will require fine-tuning along the way, using insights and analysis to effectively interpret what customers are looking for, and deliver back relevant responses that point them in the right direction.

This starts with analytics and data. As data sets grow with more usage, they will reveal insights on how customers interact with chatbots, what they are looking to do and how that changes over time. This will feed the data set used to power the chatbot’s AI — both natural language processing (the ability to interpret what the customer is asking or looking for) as well as the sentiment analysis (whether the customer is happy or frustrated). Analysis will be required to learn and understand the nuances of what customers are asking when presented with phrases like this actual query from our dataset: “Hi. What is the safest way to prove documents of account balance when applying to living in an apartment complex?” Banks and/or the chatbot vendors will need to monitor the training the chatbot, including recognizing customer frustration and offering up logical next steps — like “It looks like you’re frustrated, can we transfer you to an agent?” as needed.

The analytics and data will also provide the map of the information that needs to be developed and updated to deliver answers that customers need. Given that 93.8% of questions that customers ask are unique, having the right knowledge will be critical. Sometimes this might be a simple answer (“What is my routing number?”) and sometimes it might require decision trees that offer options (understanding if an auto loan is for a new or used vehicle to get the customer one step closer to conversion).

Banks have a great opportunity to make chatbots the 24/7 tool that improves customer experience, reduces support costs and drives digital adoption. But it will take a commitment to the analysis and ongoing optimization of knowledge to truly become a reality. 

Next time you start you interact with a chatbot, start with hello — I’ve heard they appreciate it!

Five Digital Banking Initiatives for Second Half of 2020

Written by

As the calendar nears the midpoint of 2020 and banks continue adjusting to a new normal, it’s more important than ever to keep pace with planned initiatives.

To get a better understanding of what financial institutions are focusing on, MX surveyed more than 400 financial institution clients for their top initiatives this year and beyond. We believe these priorities will gain even more importance across the industry.

1. Enabling Emerging Technologies, Continued Innovation
Nearly 20% of clients see digital and mobile as their top initiatives for the coming years. Digital and mobile initiatives can help banks limit the traffic into physical locations, as well as reduce volume to your call centers. Your employees can focus on more complex cases or on better alternatives for customers.

Data-led digital experiences allow you to promote attractive interest rates, keep customers informed about upcoming payments and empower them to budget and track expenses in simple and intuitive ways. 

2. Improving Analytics, Insights
Knowing how to leverage data to make smarter business decisions is a key focus for financial institutions; 22% of our clients say this is the top initiative for them this year. There are endless ways to leverage data to serve customers better and become a more strategic organization.

Data insights can indicate customers in industries that are at risk of job loss or layoffs or the concentration of customers who are already in financial crisis or will be if their income stops, using key income, spending and savings ratios. Foreseeing who might be at risk financially can help you be proactive in offering solutions to minimize the long-term impact for both your customers and your institution.

3. Increasing Customer Engagement
Improving and increasing customer engagement is a top priority for 14% of our clients. Financial institutions are well positioned to become advocates for their customers by helping them with the right tools and technologies.

Transaction analytics is one foundational tool for understanding customer behavior and patterns. The insights derived from transactions and customer data can show customers how they can reduce unnecessary spending through personal financial management and expert guidance.

But it’s crucial to offer a great user experience in all your customer-facing tools and technologies. Consumers have become savvier in the way they use and interact with digital channels and apps and expect that experience from your organization. Intuitive, simple, and functional applications could be the difference between your customers choosing your financial institution or switching to a different provider.

4. Leveraging Open Banking, API Partnerships
Open banking and application programming interfaces, or APIs, are fast becoming a new norm in financial services. The future of banking may very well depend on it. Our findings show that 15% of clients are considering these types of solutions as their main initiative this year. Third-party relationships can help financial institutions go to market faster with innovative technologies, can strengthen the customer experience and compete more effectively with big banks and challengers.

Financial institutions can leverage third parties for their agile approach and rapid innovation, allowing them to allocate resources more strategically, expand lines of business, and reduce errors in production. These new innovations will help your financial institution compete more effectively and gives customers better, smarter and more advanced tools to manage their financial lives.

But not all partnerships are created equally. The Office of the Comptroller of the Currency recently released changes surrounding third-party relationships, security and use of customers’ data, requiring financial institutions to provide third-party traffic reports of companies that scrape data. Right now, the vast majority of institutions only have scrape-based connections as the means for customers to give access to their data — another reason why financial institutions should be selective and strategic with third-party providers.

5. Strategically Growing Customer Acquisition, Accounts
As banking continues to transform, so will the need to adapt including the way we grow. Nearly 30% of our clients see this as a primary goal for 2020 and beyond. Growth is a foundational part of success for every organization. And financial institutions generally have relied on the same model for growth: customer acquisitions, increasing accounts and deposits and loan origination. However, the methods to accomplish these growth strategies are changing, and they’re changing fast.

Right now, we’re being faced with one of the hardest times in recent history. The pandemic has fundamentally changed how we do business, halting our day-to-day lives. As we continue to navigate this new environment, financial institutions should lean on strategic partnerships to help fill gaps to facilitate greater focus on their customers.

CECL Delay Opens Window for Risk Improvements

Written by

The delay in the current expected credit loss accounting model has created a window of opportunity for small banks.

The delay from the Financial Accounting Standards Board created two buckets of institutions. Most of the former “wave 1” institutions constitute the new bucket 1 group with a 2020 start. The second bucket, which now includes all former “wave 2 and 3” companies are pushed back to 2023 — giving these institutions the time required to optimize their approach to the regulation.

Industry concerns about CECL have focused on two of its six major steps: the requirement of a reasonable and supportable economic forecast and the expected credit loss calculation itself. It’s important to note that most core elements of the process are consistent with current industry best practices. However, they may take more time for banks to do it right than previously thought.

Auditors and examiners have long focused on the core of CECL’s six steps — data management and process governance, credit risk assessment, accounting, and disclosure and analytics. Financial institutions that choose to keep their pre-CECL process for these steps do so at their own peril, and risk falling behind competitors or heightened costs in a late rush to compliance. Strategically minded institutions, however, are forging ahead with these core aspects of CECL so they can fully vet all approaches, shore up any deficiencies and maintain business as usual before their effective date.

Discussions over the impact of the CECL standard continue, including the potential for changes as the impacts from CECL bucket 1 filings are analyzed. Unknown changes, coupled with a three-year deadline, could easily lead to procrastination. Acting now to build a framework designed to handle the inevitable accounting and regulatory changes will give your bank the opportunity to begin CECL compliance with confidence and create a competitive advantage over your lagging peers.

Centering CECL practices as the core of a larger management information system gives institutions a way to improve their risk assessment and mitigation strategies and grow business while balancing risk and return. More widely, institutions can align the execution across the organization, engaging both management and shareholders.

Institutions can use their CECL preparations to establish an end-to-end credit risk management framework within the organization and enjoy strategic, incremental improvements across a range of functions — improving decision making and setting the stage for future standards. This can yield benefits in several areas.

Data management and quality: Firms starting to build their data histories with credit risk factors now can improve their current Allowance for Loan and Lease Losses process to ensure the successful implementation of CECL. Financial institutions frequently underestimate the time and effort required to put the required data and data management structures in place, particularly with respect to granularity and quality. For higher quality data, start sourcing data now.

Integration of risk and financial analysis: This can strengthen the risk modeling and provisioning process, leading to an improved understanding and management of credit quality. It also results in more appropriate provisions under the standard and can give an early warning of the potential impact. Improved communication between the risk and finance functions can lead to shared terminologies, methods and approaches, thereby building governance and bridges between the functions.

Analytics and transparency: Firms can run what-if scenario analysis from a risk and finance perspective, and then slice and dice, filter or otherwise decompose the results to understand the drivers of changes in performance. This transparency can then be used to drive firms’ business scenario management processes.

Audit and governance: Firms can leverage their CECL preparations to adopt an end-to-end credit risk management architecture (enterprise class and cloud-enabled) capable not only of handling quantitative compliance to address qualitative concerns and empower institutions to better answer questions from auditors, management and regulators. This approach addresses weaknesses in current processes that have been discovered by audit and regulators.

Business scenario management: Financial institutions can leverage these steps to quantify the impact of CECL on their business before regulatory deadlines, giving them a competitive advantage as others catch up. Mapping risks to potential rewards allows firms to improve returns for the firm.

Firms can benefit from CECL best practices now, since they are equally applicable to the current incurred loss process. Implementing them allows firms to continue building on their integration of risk and finance, improving their ALLL processes as they do. At the same time, they can build a more granular and higher quality historical credit risk database for the transition to the new CECL standards, whatever the timeframe. This ensures a smoother transition to CECL and minimizes the risk of nasty surprises along the way.

Making Strategic Decisions With The Help of Data Analytics

Written by

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.