What Banks Missed

It’s a classic case of a couple of upstarts upending the business of banking.

Increasingly familiar names such as Affirm Holdings, Afterpay Ltd. and Klarna Bank, as well as few household names such as PayPal Holdings, are busy taking credit card business away from banks by offering interest-free, installment loans at the point of sale.

Almost overnight, this type of lending has grown into a national phenomenon, starting with online merchants and then spreading throughout the industry, as Bank Director Managing Editor Kiah Haslett wrote about earlier this year.

C + R Research reports that of 2,005 online consumers, nearly half are making payments on some kind of buy now, pay later loan. More than half say they prefer that type of lending to credit cards, citing ease of payments, flexibility and lower interest rates as their top reasons why they prefer to buy, now pay later.

The amount of money flowing into the space is substantial. In August, Square announced that it would purchase Afterpay for $29 billion. Mastercard is trying to get into the game as well, announcing a deal in September to partner with multiple banks such as Barclays US, Fifth Third Bancorp and Huntington Bancshares to bring buy now, pay later to merchants.

Whatever your skepticism of the phenomenon may be, or your lack of interest in consumer lending, it’s clear that financial technology companies are chipping away at bank business models. This phenomenon begs the question: Why are fintech companies having such success when banks could have taken the opportunity but did not?

Banks have the data. They “know their customer” — both in the regulatory and relationship sense. Yet, they didn’t anticipate consumers’ interest or demand because they already had a product, and that product is called a credit or debit card.

Few companies cannibalize their business models by offering products that directly compete with existing products. But increasingly, I believe they should. Banks that don’t acknowledge the realities of today’s pressures are vulnerable to tomorrow’s innovation.

When we think about the business of banking today, I think about a glass half empty. It doesn’t mean we can’t put a little bit more water into it. But it does require an honest assessment of gaps in your current strategy and an assessment of the team you’d need — not necessarily the team you employ.

As I head into Bank Director’s Audit & Risk Committees Conference in Chicago this Monday through Wednesday, these are some of the themes on my mind. In some ways, having a glass half empty is sometimes the best thing for you.

It gives you the chance to do something positive to change it.

How a Data-Driven Sales Methodology Can Help Banks Grow

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.

Smart Ways to Find Loan Growth

In a long career focused on credit risk, I’ve never found myself saying that the industry’s biggest lending challenge is finding loans to make.

But no one can ignore the lackluster and even declining demand for new loans pervading most of the industry, a phenomenon recently confirmed by the QwickAnalytics® National Performance Report, a quarterly report of performance metrics and trends based on the QwickAnalytics Community Bank Index.

For its second quarter 2021 report, QwickAnalytics computed call report data from commercial banks $10 billion in assets and below. The analysis put the banks’ average 12-month loan growth at negative -0.43 basis points nationally, with many states showing declines of more than 100 basis points. If not reversed soon, this situation will bring more troubling implications to already thin net interest margins and stressed growth strategies.

The question is: How will banks put their pandemic-induced liquidity to work in the typical, most optimal way — which, of course, is making loans?

Before we look for solutions, let’s take an inventory of some unique and numerous challenges to what we typically regard as opportunities for loan growth.

  • Due to the massive government largess and 2020’s regulatory relief, the coronavirus pandemic has given the industry a complacent sense of comfort regarding credit quality. Most bankers agree with regulators that there is pervasive uncertainty surrounding the pandemic’s ultimate effects on credit. Covid-19’s impact on the economy is not over yet.
  • We may be experiencing the greatest economic churn since the advent of the internet itself. The pandemic heavily exacerbated issues including the e-commerce effect, the office space paradigm, struggles of nonprofits (already punished by the tax code’s charitable-giving disincentives), plus the setbacks of every company in the in-person services and the hospitality sectors. As Riverside, California-based The Bank of Hemet CEO Kevin Farrenkopf asks his lenders, “Is it Amazonable?” If so, that’s a market hurdle bankers now must consider.
  • The commercial banking industry is approaching the tipping point where most of the U.S. economy’s credit needs are being met by nonbank lenders or other, much-less regulated entites, offering attractive alternative financing.

So how do banks grow their portfolios in this environment without taking on inordinate risk?

  • Let go of any reluctance to embrace government-guaranteed lending programs from agencies including the Small Business Administration or Farmers Home Administration. While lenders must adhere to their respective protocols, these programs ensure loan growth and fee generation. But perhaps most appealing? When properly documented and serviced, the guaranties offer credit mitigants to loan prospects who, because of Covid-19, are at approval levels below banks’ traditional standards.
  • Given ever-present perils of concentrations, choose a lending niche where your bank has both a firm grasp of the market and the talent and reserves required to manage the risks. Some banks develop these capabilities in disparate industries, ranging from hospitality venues to veterinarian practices. One of the growing challenges for community banks is the impulse to be all things to all prospective borrowers. Know your own bank’s strengths — and weaknesses.
  • Actively pursue purchased loan participations through resources such as correspondent bank networks for bankers, state trade groups and trusted peers.
  • Look for prospects that previously have been less traditional, such as creditworthy providers of services or products that cannot be obtained online.
  • Remember that as society and technology change, new products and services will emerge. Banks must embrace new lending opportunities that accompany these developments, even if they may have been perceived as rooted in alternative lifestyles.
  • In robust growth markets, shed the reluctance to provide — selectively and sanely — some construction lending to help right the out-of-balance supply and demand currently affecting 1 to 4 family housing. No one suggests repeating the excesses of a decade ago. However, limited supply and avoidance of any speculative lending in this segment have created a huge value inflation that is excluding bankers from legitimate lending opportunities at a time when these would be welcomed.

Bankers must remember the lesson from the last banking crisis: Chasing growth using loans made during a competitive environment of lower credit standards always leads to eventual problems when economic stress increases. This is the “lesson on vintages” truism. A July 2019 study from the Federal Deposit Insurance Corp. on failed banks during the Great Recession revealed that loans made under these circumstances were critical contributors to insolvency. Whatever strategies the industry uses to reverse declining loan demand must be matched by vigilant risk management techniques, utilizing the best technology to highlight early warnings within the new subsets of the loan portfolio, a more effective syncing of portfolio analytics, stress testing and even loan review.

2021 Technology Survey Results: Tracking Spending and Strategy at America’s Banks

JPMorgan Chase & Co. Chairman and CEO Jamie Dimon recognizes the enormous competitive pressures facing the banking industry, particularly from big technology companies and emerging startups.

“The landscape is changing dramatically,” Dimon said at a June 2021 conference, where he described the bank’s growth strategy as “three yards and a cloud of dust” —  a phrase that described football coach Woody Hayes’ penchant for calling running plays that gain just a few yards at a time. Adding technology, along with bankers and branches, will drive revenues at Chase — and also costs. The megabank spends around $11 billion a year on technology. Products recently launched include a digital investing app in 2019, and a buy now, pay later installment loan called “My Chase Plan” in November 2020. It’s also invested in more than 100 fintech companies.

“We think we have [a] huge competitive advantage,” Dimon said, “and huge competition … way beyond anything the banks have seen in the last 50 [to] 75 years.”

Community banks’ spending on technology won’t get within field-goal distance of JPMorgan Chase’s technology spend, but budgets are rising. More than three-quarters of the executives and board members responding to Bank Director’s 2021 Technology Survey, sponsored by CDW, say their technology budget for fiscal year 2021 increased from 2020, at a median of 10%. The survey, conducted in June and July, explores how banks with less than $100 billion in assets leverage their technology investment to respond to competitive threats, along with the adoption of specific technologies.

Those surveyed budgeted an overall median of almost $1.7 million in FY 2021 for technology, which works out to 1% of assets, according to respondents. A median 40% of that budget goes to core systems.

However, smaller banks with less than $500 million in assets are spending more, at a median of 3% of assets. Further, larger banks with more than $1 billion in assets spend more on expertise, in the form of internal staffing and managed services — indicating a widening expertise gap for community banks.

Key Findings

Competitive Concerns
Despite rising competition outside the traditional banking sphere — including digital payment providers such as Square, which launched a small business banking suite shortly after the survey closed in July — respondents say they consider local banks and credit unions (54%), and/or large and superregional banks (45%), to be the greatest competitive threats to their bank.

Digital Evolution Continues
Fifty-four percent of respondents believe their customers prefer to interact through digital channels, compared to 41% who believe their clients prefer face-to-face interactions. Banks continued to ramp up their digital capabilities in the third and fourth quarters of last year and into the first half of 2021, with 41% upgrading or implementing digital deposit account opening, and 30% already offering this capability. More than a third upgraded or implemented digital loan applications, and 27% already had this option in place.

Data Dilemma
One-third upgraded or implemented data analytics capabilities at their bank over the past four quarters, and another third say these capabilities were already in place. However, when asked about their bank’s internal technology expertise, more than half say they’re concerned the bank isn’t effectively using and/or aggregating its data. Less than 20% have a chief data officer on staff, and just 13% employ data scientists.

Cryptocurrency
More than 40% say their bank’s leadership team has discussed cryptocurrency and are weighing the potential opportunities and risks. A quarter don’t expect cryptocurrency to affect their bank; a third haven’t discussed it.

Behind the Times
Thirty-six percent of respondents worry that bank leaders have an inadequate understanding of how emerging technologies could impact their institution. Further, 31% express concern about their reliance on outdated technology.

Serving Digital Natives
Are banks ready to serve younger generations? Just 43% believe their bank effectively serves millennial customers, who are between 25 and 40 years old. But most (57%) believe their banks are taking the right steps with the next generation — Gen Z, the oldest of whom are 24 years old. It’s important that financial institutions start getting this right: More than half of Americans are millennials or younger.

To view the full results of the survey, click here.

Taking Model Risk Management to the Next Level

A financial institution’s data is one of its most valuable resources. Banks constantly collect data on their loans, deposits and customer behaviors. This data should play a key role in how financial intuitions manage their risks.

Yet, developing a data strategy can be seen as too complex based on the sheer amount of data an institution may have, or as an unnecessary burden if the objective is solely to use the information to satisfy regulatory requirements. But a holistic data strategy can enhance value across all model risk management (MRM) platforms, both for regulatory and strategic purposes. On the flip side, being inconsistent or not updating data and inputs in a timely manner can lead to inaccurate or inconsistent results. Executives need to continually update and review information for consistency; if not, the information’s relevancy in assessing risk across various platforms will decrease.

Currently, the most common data strategy approach for banks is using individual tools to measure risk for regulatory purposes. For instance, financial institutions are required to calculate and monitor interest rate risk related to their balance sheet and potential movements in future interest rates. Typically, one team within the institution extracts data and transfers it to another team, which loads the data into an internal or external model to calculate the various interest rate profiles for management to analyze and make decisions. The institution repeats this process for its other models (credit, capital adequacy, liquidity, budgeting, etc.), adjusting the inputs and tools as needed. Often, banks view these models as individual silos — the teams responsible for them, and the inputs and processes, are separate from one another. However, the various models used to measure risk share many commonalities and, in many aspects, are interdependent.

Integrating model risk management processes require understanding a bank’s current data sources and aggregation processes across all of its current models. The first step for executives is to understand what data is currently used across these platforms, and how your organization can utilize it other beyond just checking the regulatory box. In order to enhance data quality, can one data extract be used for multiple platforms? For example, can the same loan-level data file be used for different models that use similar inputs such as asset liability management (ALM) and certain CECL models? While models may utilize some different or additional fields and inputs, there are many fields — such as contractual data or loan prepayment assumptions — that are consistent across models. Extracting the data once and using it for multiple platforms allows institutions to minimize the risk of inaccurate or faulty data.

From here, bank executives can develop a centralized assumption set that can be modeled across all platforms to ensure consistency and align results between models. For instance, are the credit assumptions that are developed for CECL purposes consistent with those used to calculate your ALM and liquidity profile under various scenarios? Are prepayment assumptions generated within the ALM model also incorporated into your CECL estimate? Synchronizing assumptions can provide more accurate and realistic results across all platforms. The MRM dashboard is a tool that can be configured to alert bank executives of emerging risks and ensure that data shared by different models is consistent.

One common method of gaining insights using MRM is through scenario and stress testing. Today’s environment is uncertain; executives should not make future decisions without in-depth analysis. They can develop scenarios for potential growth opportunities, modeling through the integrated platforms to calculate impacts to profitability and credit and interest rate risk. Similarly, they can expand deposit data and assumptions to assess high-risk scenarios or future liquidity issues apart from normal day-to-day operations. Whatever the strategy may be, assessing risk on an integrated basis allows management to gain a better understanding of all impacts of future strategies and make stronger business decisions.

Once institutions begin centralizing their data and model inputs and streamlining their monitoring processes using MRM dashboards, management can shift their focus to value-added opportunities that go beyond compliance and support the strategic vision of the institution.

The Key to Upgrading Digital Experiences

The pandemic has accelerated a number of trends and digital roadmaps, momentum that continues today.

Microsoft Corp. Chairman and CEO Satya Nadella put it best when he said “We’ve seen two years’ worth of digital transformation in two months.” In banking, 59% of consumers said the pandemic increased their expectations of their financial institutions’ digital capabilities. How can banks respond?  

A Non-Negotiable Experience
As customers, haven’t we all had an experience that left us confused? Many times it’s something obvious, like a marketing email urging us to download an app that we’ve had downloaded for years and use weekly. Customers expect that when they share their data, they get a better experience. A recent survey of Generation Z consumers reported that nearly 40% give a business only one chance to provide a satisfactory digital experience before moving onto a competitor.

Customers also expect their bank to be a strategic partner in money management, offering relevant services based on the data they have. These experiences can build loyalty by making customers feel taken care of by their financial institutions.

Common Challenges
When it comes to managing and optimizing their customers’ digital experiences, we see banks dealing with a few major issues:

  • Difficulty effectively cross-selling between products.
  • Disparate services where data lives in disconnected silos.
  • The scale of data, often exceeding legacy capabilities.

These challenges, along with many others, stem from the fact that customer data often live in numerous different systems. When data is scattered and siloed, it’s impossible to tie it together to understand customers or create personalized digital experiences that engender loyalty. This is why many banks are turning to customer data platforms (CDP).

Upgrading the Digital Experience
CDPs are powering some of the most cutting edge, customer-centric digital programs across leading financial institutions. An enterprise CDP makes data accessible and useful by bringing disparate data sources together, cleansing the data, and creating a singular view of the customer that can be used across the entire organization. It can become a bank’s single source of truth on customers. Marketing can connect to customers with personalized offers, analytics can explore data to find trends and areas of opportunity, customer service can access relevant information to assist customers, and finance can forecast with customer key performance indicators.

Should you consider a CDP?
Here are a few questions executives should ask to determine if their bank’s current setup is working:

  • Are customer data points and interactions centralized in one location?
  • How much time are analysts spending gathering customer data for reporting?
  • Is marketing able to easily use the same customer data to drive personalization?
  • How confident are teams in the data?
  • Is it easy to bring in a new data source?

If there is hesitation around any of the answers, looking at CDP options could be a really smart idea.

Capabilities to Look for

There are many companies using CDP terminology to describe products that aren’t exactly that. Banks should focus on a few key features when evaluating a CDP.

Speed to value. How long does it take to pull data together for a customer 360 degree view? When will data be ready to serve customers and power initiatives across the organization? The best way to accelerate these timelines is with a CDP that uses artificial intelligence to unify and organize records, which is much faster and more stable than rules-based data unification systems.

Enterprise functionality. A CDP should serve as the single source of truth for the entire organization, with a suite of tools that can accommodate the needs of different teams. Multiple views means teams are only presented with the data they need, with the methods that they prefer: robust SQL query engine for analysts, point-and-click segmentation for less technical users and dashboards for executive visibility.

Flexibility and interoperability. A CDP should work with your bank’s current technology investments, connecting easily to any tools or systems you add in the future. One sign of this is a CDP having many partnerships and easy integrations that can quickly allow you to take action.

You need to trust that a CDP can scale to the enterprise and compliance demands of a bank, accommodating vast stores of data that will only continue to grow.

A critical opportunity
There is unprecedented demand from banking leaders to stand up a CDP as a critical business driver. And no wonder. With so many customers using digital channels and generating more data, banks need to double down on increasing the lifetime value of existing customers while finding ways to attract new customers.

Can Banks Afford to Be Short-Sighted With Real-Time Payments?

The industry’s payments ecosystem is developing rapidly in response to increasing consumer demand for faster, smarter payments.

The need for real-time payments was accelerated by the global pandemic — but most banks are moving far too cautiously to respond to market demand, whether that is P2P, B2B, B2C or other segments. Currently, The Clearing House’s RTP® network is the only available real-time payments platform, while the Federal Reserve’s instant payments service, FedNow℠, is in a pilot phase with plans to launch in 2023. FedNow will equip financial institutions of all sizes with the ability to facilitate secure and efficient real-time payments round the clock.

For most banks, operating on core legacy technology has created a payments infrastructure that is heavy-handed, disjointed, costly and difficult to maintain, with no support for future innovation. Most banks, fearing the cost and effort of modernization, have settled for managing multiple payment networks that connect across disparate systems and require the support of numerous vendors. With the introduction of real-time payments, can these new payment rails afford to be a mere addendum to the already-byzantine payment architecture of banks?

Answering “yes” begets more questions. How resilient will the new offering be on the old infrastructure? Can banks afford to be myopic and treat real-time payments as a postscript? Are short-sighted payment transformations elastic enough to accommodate other innovations, like the Central Bank Digital Currency (CBDC) that are in the offing?

Preparation starts with an overhauling of payments infrastructure. If banks are to place themselves at a vantage point, with a commanding perspective into the future of payments, they should consider the following as part of the roadmap to payments modernization:

  1. From transactions to experience. Payments are no longer merely functional transactions; they are expected to provide qualitative attributes like experience, speed and intelligence. Retail and business customers increasingly demand frictionless and intuitive real-time payments, requiring banks to refurbish the payment experiences delivered to clients.
  2. The significance of payment data. The ISO20022 data standard for payments is heavier and richer compared to legacy payments data, and is expected to be the global norm for all payments by 2025. Banks are under increasing pressure to comply, with players like SWIFT already migrating to this format and more than 70 countries already using ISO20022. Payment solutions that can create intuitive insights from centralized data stored in ISO20022 format, while also being able to convert, enrich and validate legacy messaging into ISO20022, are essential. Banks can benefit from innovative services like B2B invoices and supply chain finance, as Request for Payment overlay services is a key messaging capability for customers of real-time payments.
  3. Interoperability of payment systems. The interoperability between payment systems will be an imperative, especially with the ecosystem of different payment rails that banks have to support. Interoperable payment rails call for intelligent routing, insulating the payer and payee from the “how” of payment orchestration, and paving the way for more operational efficiency. Operating costs account for more than 68% of bank payment revenues; centralizing the management of multiple payment networks through an interoperable payment hub allows bankers to minimize these costs and improve their bottom lines.
  4. Streamlining payment operations. Work stream silos lead to fragmented, inefficient and redundant payment operations, including duplicated fraud and compliance elements. This is where payment hubs can add value by streamlining payment operations through a single, consolidated operation for all payment types. Payment hubs are a great precursor for subsequent modernization: intelligent payment hubs can handle omnichannel payments, as well as different payment types like ACH, Fedwire, RTP and FedNow in the future. This takes care of the entire payment lifecycle: initiation, authorization, clearing, settlement and returns.
  5. Future-proofing payment systems. Following the path of trendsetters, banks have to equip themselves with future-proof solutions that can adapt to real-time domestic and cross-border payment systems processing multiple currencies. As open-banking trends gain traction, it is important to consider that the winds of change will eventually find payments, too. It is imperative that banks are cloud based and API driven, so they can innovate while being future-ready.

The opportunity cost of not offering real-time payments is becoming more evident for banks, as they wait for their core providers to enable real-time payments. Calls for banks to modernize their payments infrastructure are swelling to a roar; now is the time for banks to define their payments modernization strategy and begin to act.

How Banks Can Beat Big Tech at Its Own Game

For the last decade, headline after headline has predicted the demise of banks at the hands of the tech giants. Why? One word: data.

Finance is — and always has been — a data-dependent business. Providing a commercial loan relies on knowing how likely the would-be borrower is to default, a rudimentary data-processing task that is exactly the expertise that Big Tech has. These companies, which include Amazon.com, Apple, Facebook and Alphabet’s Google, can generate and leverage large-scale, granular and real-time data on their users, and have used this to build the largest and most valuable businesses in the world.

By comparison, many banks still don’t fully appreciate the value of their data, and have yet to generate meaningful financial returns from it. This is because most of their data is siloed and sprawled throughout the organization across customer onboarding, marketing, financial crime and fraud and credit risk. Instead of being regarded as a valuable commodity, data is seen as a potential temptation for hackers and a cost that needs to be managed.

If banks can learn to leverage their data like these technology giants — embracing digital transformation to lend faster, smarter and more to businesses — they can beat Big Tech at its own game. Unlike Big Tech, banks have long-worked in regulated environments that require all participants to follow the same rules. As a result, they figured out how to work collaboratively with regulators at speed and at scale, and established robust processes and governance around areas such as data ethics and privacy. This has helped build consumer trust and burnished relationships.

This proved to be a powerful combination over the last 15 months as Covid-19 forced states and businesses to temporarily shut down. The government turned to banks, not Big Tech, to help support businesses, via initiatives such as the Paycheck Protection Program and the Main Street Lending Program.

West Reading, Pennsylvania-based Customers Bancorp rose to this challenge and leveraged OakNorth’s ON Credit Intelligence Suite to identify which industries in its portfolio were more stressed and which metrics it should use to determine risk profiles. Data helps banks such as Customers identify overlooked market sectors and business types that are good credits, but could be difficult for lenders to take on without the data or analytics to make an informed decision. Embracing technology to leverage data effectively allowed Customers Bancorp to provide over 100,000 loans and become the sixth-most active PPP lender in the US – a substantial feat for a bank with $18.8 billion in assets.

Regulatory know-how, proprietary data sources and specialized services offer ways for banks to compete with — or indeed, collaborate — with Big Tech. This has been demonstrated through partnerships such as Goldman Sachs Group and Apple, and JPMorgan Chase & Co. and Amazon. Leveraging data and digital transformation will empower banks to discover gaps in the market and even attract borrowers which Big Tech is unable to. After all, not every business will be keen on the idea of borrowing from the same company that helps them share goofy photos with their friends.

Data Considerations for Successful Deal Integration

Bank M&A activity is heating up in 2021; already, a number of banks have announced deals this year. Is your bank considering a combination with another institution?

Banks initiate mergers because of synergies between institutions, and to achieve economies of scale along with anticipated cost savings. Acquiring institutions typically intend to leverage the newly acquired customer base, but this can be difficult to execute upon without a data strategy.

Whether your bank is considering are buying or selling, it has never been more important to evaluate whether your data house is in order. Unresolved acquisition data challenges can result in poor customer experiences, inaccurate reporting and significant inefficiency after the merger closes. What causes these types of data challenges?

  • Both institutions possess massive volumes of data and multiple systems, while disparate systems prevent a holistic view of the combined entity. In a merger, the acquirer does not have access to the target’s data until legal close, and data is not consolidated until the core conversion is completed.
  • Systems are often antiquated, and it is difficult to access high-value customer data. Data integrity is often an issue that impedes anticipated synergies that could promote revenue generation.
  • Absence of enterprise knowledge or insight into target’s customer portfolio. This makes it difficult to identify growth opportunities and plan the strategy for the combined institution. It also creates a barrier to pivoting in the event a key relationship manager leaves the institution.

Baltimore-based Howard Bancorp has conducted five successful acquisitions in the last eight years. Steven Poynot, Howard’s CIO, recommends looking internally first and getting your house in order prior to any merger. “If you don’t understand all of the pieces of your bank’s data and portfolio well, how are you going to overlay your information in combination with the other bank’s data for reporting?”

Five solutions to merger data challenges include:

  • Create a data governance strategy before a deal is in the works. Identify the source and location of all pertinent data. Evaluate whether customer data is clean and up to date. Stale customer information such as old land line phone numbers and inaccurate email addresses yield roadblocks for relationship managers attempting to use data effectively. If your bank does identify data issues, implement a clean-up project based on a data governance policy framework. This initiative will benefit all banks, not just those looking to merge.
  • Develop an M&A integration plan that sets expectations and goals. Involve the CIO quickly and identify tools needed for the integration. Make a strategic determination of what data fields need to be integrated for reporting purposes. Acquire tools to allow for enterprise reporting and to highlight sales opportunities. Partner with vendors who understand the specific challenges of the banking industry.
  • Unify Disparate Systems. Prioritize data integration with a seamless transition for customers as the top priority. Plan for mapping and consolidating data along with reporting for the combined institution. Take product and data mapping beyond what is needed for the system mapping required for core integration. Use the information gleaned from the data to support product analytics, risk assessment, business development and cross selling strategies. The goal is to combine and integrate systems quickly to leverage the data as an asset.
  • Discourage Data Silos. Make data available and easily accessible to all who need it to do their jobs. Banking is a relationship business, and relationship managers need current customer relationship information readily available to them.
  • Analyze. Once the data has been consolidated, analyze and leverage it to identify opportunities that will drive revenue.

In a merger, the sooner that data is combined, the earlier decisions can be made from the information. As data silos are removed and data becomes easily accessible across the organization, data becomes an enterprise-wide asset that can be used effectively in the bank’s strategy.

Unlocking the Value of Customers’ Data

A customer data platform is at the heart of the most cutting edge, customer-centric digital programs at leading financial institutions. This platform should clean, connect and share customer data so the business lines that need it most can create distinctive and relevant experiences. Amperity’s Jill Meuzelaar details the four key features banks should look for in a customer data platform, as well as common issues they may encounter when evaluating a current or prospective system.

  • How to Connect Customer Data
  • Incorporating Flexibility for Maximum Functionality
  • Avoiding Common Pitfalls