Six Things To Know About CECL Right Now


CECL-11-13-18.pngMany banks began the transition to CECL in earnest when the final version was issued in 2016. While banks are in various stages, some are already working through more nuanced aspects of the transition.

Many lessons have been learned from actual CECL implementations, and here are some tips to assist bank directors as they guide management through the transition.

1. The quantitative impact of CECL adoption may be less straightforward than initially expected. Even before the final CECL standard was issued, industry observers tried to predict just how much the allowance would increase upon adoption. In truth, it will be almost impossible to estimate the impact of the transition for an individual institution. The actual impact will depend upon many bank-specific factors, the estimation method, the length of the reasonable supportable forecast, the size of today’s qualitative adjustment, and management’s outlook, to name a few. Additionally, some banks with short-term portfolios have been surprised to discover the CECL estimate may be lower than the current allowance due to a shift from an estimate based on a loss emergence period to one that considers the next contractual maturity date.

2. CECL may result in a requirement to manage model risk for unsuspecting institutions. Similar to reserving practices today, banks are employing a variety of approaches. General trends include the largest institutions employing statistical software to build custom in-house models, while the smallest institutions favor a less complex approach that relies on adjusting historical averages. Many institutions who are not using models are relying on “correlations” to support their adjustments. However, this practice needs to be managed carefully, as per regulatory definition, any method that applies a statistical approach, economic, financial, or mathematical theory to derive a quantitative estimate is considered a “model.” Therefore, using a correlation – regardless of whether it is identified in a spreadsheet, vendor solution, or anywhere else – to quantify the impact of a factor is by definition a model, and subject to model risk management. Institutions taking this approach to CECL should carefully consider the scope of model risk management, and avoid accidentally creating or misusing models.

3. Qualitative adjustments will still be necessary. Regardless of the method used to estimate the impact of forecasted conditions, there will still be a need to apply expert judgment for factors not considered in the quantitative (modeled) estimate. Even the most sophisticated models used by the largest banks will not consider every factor. Further, many banks prefer the flexibility to exercise judgment in their reserving process. While it’s not yet clear which factors the industry will use or how to quantify the lifetime impact, as it relates to regulatory and auditor oversight, the level of scrutiny around qualitative adjustments will not decrease from existing practice. Again, accidentally creating models is particularly important given the scrutiny on management judgment and the overall impetus to quantify it.

4. Think beyond compliance. One of the overarching goals of CECL is to better align credit loss measurement with underwriting and risk management practices. The transition to CECL presents banks with an opportunity to have unprecedented insight into the credit portfolio. For example, a comparison between the CECL estimate and the interest margin can provide insight into underwriting practices. But this can only happen if banks take a holistic approach to the transition and make the necessary investment in systems and reporting.

5. Reporting and analytics will be more important than ever. Bank directors will be responsible for answering shareholder questions related to the CECL reserve, which will be sensitive to changes in forecasted conditions. As a key constituent of the disclosures and internal management reports, bank directors have a responsibility to ensure a proper reporting framework is in place – one that integrates the data inputs and quantifies the change in expected credit losses at the instrument level. Attribution reports, for example, will be especially helpful in explaining why the allowance changed because they isolate and quantify the impact of individual variables affecting the reserve.

6. Be prepared for an iterative process, even after adoption. Translating the conceptual to operational can reveal unintended consequences and further questions. The industry has continued to work through implementation concerns since the final version was issued in 2016, including several meetings of the CECL Transition Resource Group. Industry best practices will evolve well after initial adoption.

Four New Revenue Streams for Banks


revenue-10-10-18.pngCreating a healthy bottom line is the biggest goal for most financial institutions. If your bank can’t consistently turn a profit, you’ll quickly be out of business.

Maintaining a profitable bottom line requires a consistent flow of revenue. This can be difficult, especially for financial institutions that rely on both retail banking and enterprise customers to generate revenue.

Why is that? Because 40 to 60 percent of all retail banking customers are not profitable, according to a report by Zafin. Combined with the fact enterprise customers are consistently asking for a more robust product suite with high-tech payment options, turning a profit becomes difficult. Banks can alleviate the pressure by finding new ways of generating revenue that will improve the organization’s profitability.

Here are four ways you can create new revenue streams:

1. Reloadable Cards
If revenue has stagnated, it may be time to reinvigorate your product offerings. A good place to start for retail customers is reloadable cards. A report published by Allied Market Research, titled, “Prepaid Card Market – Global Opportunity Analysis and Industry Forecast, 2014 – 2022” predicts the global market for reloadable cards will reach $3.6 billion in 2022.

The benefits customers receive from reloadable cards are exceptional—fraud protection, no credit risk, and spending limits—and the profits financial institutions can reap are even better.

With reloadable cards, financial institutions can charge customers a variety of fees, including a fee to purchase and use the card, and a fee to withdraw funds for PIN-based transactions. Reloadable cards can also provide depository income.

2. White Labeling
White labeling can be a great way to generate new revenue streams by letting bank treasury departments resell funds disbursement platforms to their business customers. This makes payments more convenient for customers by speeding up and streamlining the process.

By reselling the right platform, banks can gain a competitive advantage by offering multiple emerging payment methods, such as virtual cards and real-time payments, to business customers. These high-tech payment methods are becoming more and more popular, helping financial institutions win new customers and retain established accounts.

3. Mobile Device Payments
The demand for mobile payment capability has been steadily growing since early 2000. Now, with digital natives like Gen Z entering the workforce, financial institutions have an opportunity to create mobile payment strategies that focus on customer satisfaction and retention.

This is a still an emerging space, but one that holds many possibilities for delivering products and services customers want and need. White labeling and reselling a funds disbursement platform, including mobile payment options, can help treasury clients in this area.

4. Improve Data Analytics
While not a revenue stream per se, analyzing data more effectively can help you identify new ways of increasing revenue unique to your business. For instance, if your analytics reveal many of your customers are small businesses struggling with treasury management, consider launching products and services that help.

The more you know about your consumers and the way they interact with your organization, the better equipped you’ll be to address their needs. Advanced customer data analytics will allow you to improve performance and add products in multiple areas of your financial institution, including:

  • Credit revolvers
  • Credit cards
  • Lending programs

Thoroughly analyzing customer data can also improve your ability to target new services and products to customers who want them.

Find New Products and Services that Appeal to Your Customers
Use your data and experiences with current customers to find areas where they’re struggling. Can you step in with a new offer that solves their problems? Options for improvement with existing customer accounts are the best new revenue streams for your financial institutions.

We’ve seen many banks succeed specifically by optimizing fee collections, delivering white-labeled products to improve customer convenience, and taking advantage of emerging payments technology. Use these revenue streams as a starting point, customizing them for what’s right for you and your customers.

Offline Versus Real-Time Analytics: Where Is the Industry Heading?


analytics-11-22-17.pngFinancial institutions are demanding real-time analytics at their point of customer interactions. Why? Sophisticated analytics applied in real time and at the point of customer contact can deliver better customer experience as well as increase the financial results of the institution. For example:

  • An insurance company can match different combinations of coverages and add-ons that can fit within a customer’s given constraints on price.
  • A banker receiving a phone call can see on screen the updated Life Time Value (LTV) of the customer and hold the discussion accordingly.

For years, we have been advising our clients to connect their front-end, customer- facing systems with real-time pricing analytical capabilities, or at least lay the foundations to enable this capability in the near future.

According to a September 2016 report from the research firm Gartner, “Between 2016 and 2019, spending on real-time analytics will grow three times faster than spending on non-real-time analytics.” Getting the right real-time analytics at the right time can deliver great value. Yet, from my company’s standpoint, most of the questions we get about real-time pricing engines are from vendors of front-end systems and other stakeholders. They are approaching us to enable the integration of their systems with their client’s back-end pricing structures. These are providers of insurance rating engines and underwriting solutions, as well as providers of core systems, revenue management and onboarding systems.

It seems that the driver for this vendor interest is explicit demand from the banks and insurance companies themselves. These institutions are increasingly investing in off-line pricing analytics to improve performance, software that can be used to optimize pricing and decision making.

Why Is This Happening Now?
The rush to utilize real-time analytics in customer-facing processes and decisions is not unique to pricing nor to the financial services industries. It has been growing for several years as part of the broader big data and advanced analytics trends.

Banks and insurers are now raising real-time pricing analytics as a requirement from suppliers of pricing systems, and have been defining such capabilities, or connectivity to such systems, as must have “add-ons” in requests for proposals for core and front-end systems. For example, banks and insurers are demanding real-time analytics for systems that offer customer relationship management, underwriting, onboarding, rating and pricing. Of course, the level of demand for such pre-integration differs between countries and sub-industries, and it is highly influenced by regulatory requirements, however, in most segments we have noticed the pull in this direction.

Moving From Off-Line Analytics to Real-Time Analytics
Today, it is even easier for financial organizations to get their budgets to include expenses of adopting real-time analytics. Replacement of core systems is accelerating as more resources are available to buy and implement these systems. This is enabling companies to re-evaluate all related processes, including pricing. Coupled with the surge in analytical know-how and advances in analytics technologies, including real-time capabilities and faster optimization, real-time analytics is becoming more widely feasible.

But the underlying benefits of real-time analytics is what is really driving the demand. Financial institutions realize that connecting their offline analytics to the customer facing process brings uplift not only in numbers but in the customer experience itself. According to a December 2016 report from the research firm Gartner, real-time analytics at firms is facilitating faster, more accurate decisions, especially for complex digital business initiatives such as online and mobile banking. Below are some of the benefits we have seen customers enjoying after migrating to real-time analytics:

  1. The ability to react quickly to aggressive competition, especially given the rise of direct channels and players.
  2. Improvement in the efficiency of price execution processes as well as a reduction in time-to-market of new pricing strategies.
  3. Improvement in customer-facing decisions. Once a company has a system in place to analyze real-time data, their ability to understand the customer significantly increases, translating into improvement in key performance indicators such as annual increases in pricing, as well as being able to anticipate and meet customer expectations.

Is Real-Time Analytics on Your Roadmap for 2018 or Beyond?
Regardless of what the reasons might be, we have been receiving more and stronger indications that real-time analytics is catching on in the insurance and banking markets in which we operate. Offline advanced analytics are already mainstream investments in financial organizations, and the focus seems to be progressing very practically to the next logical extension of real-time application of these analytics. Implementing real- time analytics that is connected to customer-facing systems requires forethought and planning. Even if this is something you are considering doing three years from now, the planning should start today.

To discuss how these topics impact your business, feel free to contact us at info@earnix.com.

How to Become a Data-Driven Bank


data-5-8-17.pngBanks collect lots of data on their customers, but they aren’t always adept at using it to grow their business. Community banks, in particular, are just beginning to realize the power of data analytics and business intelligence.

Client data and the tools to analyze it can transform how banks conduct their commercial lending business. Data-driven banks can leverage analytics to make better informed decisions, streamline operations, and improve customer service.

The following are three steps for boards to consider for successful adoption of better data analytics:

  1. Support investment in systems that organize and centralize data and standardize processes.
  2. Reinforce the systems investment with policy, training and change management initiatives.
  3. Champion the new systems and processes and how they contribute to the bank’s success.

Here are some practical recommendations for a community bank executive who wants to turn data analysis into bottom-line results.

Define the data universe. The data that community banks can use includes company financials, qualitative customer data, and borrower behavioral data, including payment and credit utilization history. Establishing a centralized system that captures this unstructured data consistently is the first step in this process.

Consider a partnership. Effective analytics strategies ensure that short- and long-term goals are aligned with the bank’s current business operations. Partnering with a vendor with the required analytics technology and implementation expertise could help the bank capture the right data and integrate it into their processes.

Data quality is key. The top tactical issues with this approach involve collecting, organizing, and protecting the quality of the data. Maintaining the integrity of analytics requires clean data that is accurate, comprehensive and continually updated. Data quality is key to realizing the value of business intelligence tools.

Communicate early and often. Educating the organization on the value of credit measures, whether back office risk managers or front office sales professionals, will equip all stakeholders with a solid understanding of the new analytic tools and how they support the overall goals of the bank.

Establish Success Metrics. Even data-driven banks should be wary of aligning internal data with external benchmarks and best practices, because the latter may not be applicable to a particular type of business, product focus, marketplace or strategy. Instead, banks can use internal data to define their own benchmarks and measure success against goals and past performance. Assessing actual performance by comparing historical trends to new profitability, default and recovery metrics (including internal ratings) serves as an indicator of improvement. In other words, how would the prior portfolio perform given new tools and measures versus its actual performance?

Leveraging advanced data analytics and business intelligence tools is an investment that, if properly implemented, should pay dividends in the form of higher quality loans, better customer service and increased operational efficiency.

To read the complete white paper, “How to Become a Data-Driven Bank,” click here.

Banks Can Improve Sales to Commercial Clients with Analytics


6-27-14-izale.pngCompetition is heating up in the world of business banking. New entrants such as Square, Wal-Mart and PayPal are serving notice to traditional banks—evolve or die—and the board has taken notice. Business banking sales is now a board-level issue.

In order to compete with non-banking competitors, banks need to be able to consistently recommend and sell products through any channel to truly understand potential sales and profitability.

American National Bank of Texas, Comerica Bank, Sovereign Bank (now known by its parent’s name, Santander), SunTrust Banks, Central Bank and Rockland Trust are just a few of the banks that are using Ignite to improve their sales process so that it is consistent and measurable.

Ignite’s solution consists of product recommendation guides and robust analytics. Recommendation guides personalizes a customer’s banking experience. It can be used directly by the customer on the website or via a mobile device or by banking personnel in the branch and call center as an interactive tool to provide a consultative, direct touch experience for the customer. The recommendation guide conducts a needs assessment by asking a series of questions to understand the prospect’s current situation. The guide uses the collected information, banking thresholds, and eligibility analytics to evaluate the responses to recommend product bundles to meet the specific needs of the customer. Recommendation guides help banks:

  • Shorten the sales cycle by automating selling, cross-sell and up-sell opportunities.
  • Create a consistent multichannel sales experience—whether online, mobile, at the branch, or in the call center.
  • Produce high quality leads by using demographics, bank eligibility and bank-defined thresholds, in real-time, at the point-of-sale.
  • Increase the average number of accounts opened.
  • Decrease user frustration by making banking products easier to find and select.
  • Increase customer loyalty to obtain a three-product loyalty threshold.
  • Easily integrate credit risk management solutions including Salesforce, FIS Customer Relationship Management, and Avidian Profit.
  • Increase customer profitability.

Data collected using Ignite’s recommendation guides, web traffic, product eligibility, bank threshold criteria, as well as from a unique and specialized banking database developed from over three million data points on purchasing behavior is used to produce customizable reports that are presented in secure, customizable, graphical, interactive, and easy-to-use dashboards. These reports can be run weekly, monthly or quarterly and help banks:

  • Determine lost profitability.
  • Understand profit gap. Ignites’ profit gap analysis will show you what was sold, what could have been sold based on the customer’s eligibility, potential profit, and the resulting profit gap per product.
  • Manage sales effectiveness in the branch, online, and within the customer service center.
  • Justify and target marketing spend.

Rockland Trust Finds Success with Analytics
Rockland Trust implemented Ignite’s solution both online and in their branches as part of their overall omni-channel strategy. Rockland had been looking for tools to help their branch sales staff sell more effectively to business clients. With Ignite’s solution, Rockland increased new business account openings significantly within the first three months.

Rockland has also learned that most business prospects qualify for up to seven income-generating products. Ignite’s analytics performs a real-time analysis, during the consultation, that matches Rockland’s products with business customer needs and eligibility. This can be done on a computer or a tablet and gives the branch sales staff the information they need immediately at their fingertips so that they can provide a higher level of service for their customers. As a result, Rockland has seen an increase in cross sales at the initial account opening when using the tool.

“At Rockland Trust we’ve been meeting the financial needs of the business community for more than 100 years and we’re constantly searching for ways to enhance our service,” said Jane Lundquist, executive vice president and director of retail banking, business banking and home equity lending at Rockland Trust. “We looked for a solution that would help our branch staff effectively identify all the services that could benefit business clients. Ignite’s recommendation guides and analytics proved to be the tool that works best.”

Rockland was able to see measurable results within 90 days of implementation.