Disrupting the All-or-Nothing Mindset in Banking

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Nine and a half times out of 10, you don’t eat the entire pie during dessert. Instead, you opt for a slice – maybe two.

It’s the same with financial institutions and their services.

Most banks don’t originate every type of loan or allow customers to open every type of account in the market. But when they are in need of a specific capability, such as banking as a service capabilities or acquisition, development and construction financing, it can be difficult to find a solution that does only that.

In certain cloud-hosted environments, however, banks can create the exact solution they need for their business and customers.

In this episode of Reinventing Banking, a special podcast series brought to you by Bank Director and Microsoft Corp., we speak to Robert Wint, senior product director at Temenos, a cloud-based banking technology solutions provider.

Wint describes how Temenos’ cloud banking focus is helping financial institutions spin out specific, individual technologies and launch them as stand-alone solutions. He brings to the table some impressive case studies, and introduces a potentially new term to our American audience: composable banking.

Temenos reports that its technology is used to bank over 1.2 billion people. Listen to find out how.

This episode, and all past episodes of Reinventing Banking, are available on FinXTech.com, Spotify and Apple Music.

Detect and Prevent Check Fraud in Real Time

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Financial institutions are engaged in a never-ending battle to stay a step ahead of fraudsters that are clever and nimble enough to continuously exploit their organization or system weaknesses.

Many banks focus on combating digital fraud given the rapid digital developments in the financial services industry. However, fraudsters continue to leverage digital and physical channels to commit check fraud. In fact, checks serve as the payment method most impacted by fraud activity; 66% of payments professionals reported check fraud activity in 2020, according to the 2021 Association for Financial Professionals Payments Fraud and Control Survey.

Banks encounter check fraud in many ways, including counterfeits, forgeries, alterations, serial numbers, stop payments and check kiting. Technological advances have made it easier for fraudsters to create realistic counterfeit and fictitious checks, as well as false identification that can be used to defraud financial institutions nationwide.

Digital banking provides customers with many conveniences but also leaves banks vulnerable to risk. Perpetrators are no longer required to show their faces at physical branches or ATMs to deposit fake checks. However, 49% of fraud occurs in over-the-counter transactions, according to a recent ABA Deposit Account Fraud Survey Report.

Fortunately for financial institutions, there are three key tools they can use to combat check fraud.

The first is leveraging transaction analysis, which is the process of examining bank transactions to look for unusual and suspicious activity or other issues. This key component scrutinizes debits and credits contained in deposits and withdrawals to identify suspicious items, such as duplicate check numbers and out-of-range check numbers and amounts. It also applies tests at the account and entity level, measuring things such as account velocity, account volume and deposits or withdrawals of unusual amounts.

The second tool is check stock validation, which analyzes presented check images against historical reference check images to authenticate the check stock. This can help institutions identify counterfeit in-clearing and over-the-counter checks quicker and more effectively. accurately and reliably than visual inspections. Check stock verification leverages technology to spot aberrations that the human eye cannot detect. It also reduces the number of manual verifications and decreases false positives through digital check image analysis. This improves the check fraud detection process and alleviates the burden on in-house anti-fraud teams.

A third tool is signature verification, which uses machine learning algorithms and sophisticated decision trees to provide a detailed analysis of check signatures. This results in efficient evaluation of suspect in-clearing and over-the-counter checks and increased confidence levels for acceptance and return decisions.

Banks can improve on their ability to detect fraud by combining software innovations such as decision tree/multiple variable analysis, image analysis and machine learning predictive analytics. Data topology, which is a way to classify and manage real-world data scenarios, will increase over time, which allows banks to include contextual information and negative historical analytics. In turn, these outcomes detect transactional fraud and suspicious activity, reducing false negatives and enabling a financial institution to make better and faster fraud-related decisions.

Automation software performs fraud risk scoring on deposits and withdrawals, using specific detection algorithms for each type of check such as on-us, transit, treasury checks and local government checks. The software applies transaction and image analysis on each item in the deposit, along with a configurable scorecard that calculates the risk for the parties involved in the transaction. Today, software can analyze more than 60 parameters covering the conductor, beneficiary, issuing account, and items to produce a single fraud score. This calculated fraud score provides the bank with an appropriate interdiction message — including a hold recommendation that gives the bank the option to accept the deposit, covering the fraud and other collectability risks by holding the fund.

Fraudsters become more innovative every year, targeting vulnerable victims to execute their plans and schemes. Even though check use is increasingly uncommon, fraudsters still utilize checks as a convenient medium to exploit banks and their customers. But banks can mitigate risk and reduce fraud loss efficiently. Used together, tools like transaction analysis, check stock validation and signature verification enable banks to prevent check fraud. Providing a safer banking experience protects the financial institution from fraudulent risks, strengthens the customer experience and earns trust.

Getting Faster, Simpler, Cheaper and More Secure

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In June 2020, Coastal Financial Corp. began onboarding financial technology clients to ramp up its banking as a service (BaaS) business.

The $1.8 billion community banking company in Everett, Washington, would lend its bank charter, compliance program and payment rails to nonbanks for a fee. Nine out of 10 of those clients are unregulated by any financial regulator; one out of 10 might be a regulated entity such as a broker-dealer. This arrangement means the bank must monitor its nonbank customers for compliance with anti-money laundering, foreign sanctions and Bank Secrecy Act (BSA) laws.

Andrew Stines, the chief risk officer of Coastal Financial, and his staff of BSA experts keep track of a fluctuating amount of flagged transactions per month, about 3,000 to 4,000, on everything from ACH and loan payments to debit and credit card transactions. It’s a lot. From the bank regulators’ point of view, “I’m the one who really owns that risk,” Stines says.

The company previously had manually pulled flagged transactions for further investigation  with Excel spreadsheets. But that didn’t work anymore, given the workload. So Coastal turned to Hummingbird, the winner of Bank Director’s 2021 Best of FinXTech Award for compliance & risk.

Hummingbird automatically pulls flagged transactions from the bank’s core, Neocova, and automates compliance reporting. It sends suspicious activity reports (SARs) to regulators after Coastal Financial conducts investigations. Hummingbird also creates an auditable trail of each case.

The bank is not alone in trying to ramp up its fraud and compliance monitoring and reporting using new software. Financial institutions are under increasing pressure to update their fraud technologies with machine learning, robotic process automation and other tools to combat increasingly sophisticated criminals and higher use of digital services, according to a February 2021 report from the research firm Celent.

Celent Head of Risk Neil Katkov projects that North American financial institutions — which are the greatest targets for global fraud — will spend $3.1 billion on fraud technology in 2021, or 16.1% more than the year before. Spending on fraud operations will amount to another $4.55 billion, he wrote.

The marketplace for fraud and compliance software has become crowded, which benefits banks, says Kevin Tweddle, the senior executive vice president for community bank solutions at the Independent Community Bankers of America.

“People ask me what’s a fintech,” he says. “It makes [banking] faster, simpler, cheaper and more secure.” An especially active group right now are cybersecurity companies, all vying to monitor threats for financial institutions and to help with compliance and reporting requirements.

Finalists in the compliance and risk category for the Best of FinXTech Awards included IT compliance company Adlumin, which uses machine learning to detect threats, malfunctions and operations failures in real time, and the cybersecurity provider DefenseStorm, which is a cybersecurity compliance platform built for banks and credit unions. For more on how Bank Director chose winners, click here.

But Hummingbird was clearly a stand-out for Coastal Financial. The software program was cost competitive, although Stines declines to name the price. Using the software clearly pays for itself, he says. But he admits the company might not need Hummingbird if not for its BaaS business, which adds to the company’s reporting requirements. Stines estimates he’d have to hire four to five additional full-time employees without it.

The drawback is that Hummingbird’s software doesn’t include every tool the banking company needs. But there’s a roadmap to adding functionality, and Hummingbird sticks to its promised dates, Stines says. The real selling factor was the user interface and the fact that Hummingbird seems eager to make changes as needed, and understands Coastal Financial’s technology clients. “They are more forward-thinking and more in tune with digital and fintech services than traditional players in the space,” he says.

This may just be the beginning. For Tweddle, banks and credit unions are enjoying an early to middle development period for fintech. “There’s a lot more interesting things to come,” he says.

Build Versus Buy Considerations for Data Analytics Projects

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It is the age-old question: buy versus build? How do you know which is the best approach for your institution?

For years, bankers have known their data is a significant untapped asset, but lacked the resources or guidance to solve their data challenges. The coronavirus crisis has made it increasingly apparent that outdated methods of distributing reports and information do not work well in a remote work environment.

As a former banker who has made the recent transition to a “software as a service” company, my answer today differs greatly from the one I would have provided five years ago. I’ve grown in my understanding of the benefits, challenges, roadblocks and costs associated with building a data analytics solution.

How will you solve the data conundrum? Some bank leaders are looking to their IT department while other executives are seeking fintech for a solution. If data analytics is on your strategic roadmap, here are some insights that could aid in your decision-making. A good place to start this decision journey is with a business case analysis that considers:

  • What does the bank want to achieve or solve?
  • Who are the users of the information?
  • Who is currently creating reports, charts and graphs in the institution today? Is this a siloed activity?
  • What is the timeline for the project?
  • How much will this initiative cost?
  • How unique are the bank’s needs and issues to solve?

Assessing how much time is spent creating meaningful reports and whether that is the best use of a specific employee’s time is critical to the evaluation. In many cases, highly compensated individuals spend hours creating reports and dashboards, leaving them with little time for analyzing the information and acting on the conclusions from the reports. In institutions where this reporting is done in silos across multiple departments and business units, a single source of truth is often a primary motivator for expanding data capabilities.

Prebuilt tools typically offer banks a faster deployment time, yielding a quicker readiness for use in the bank’s data strategy, along with a lower upfront cost compared to hiring developers. Vendors often employ specialized technical resources, minimizing ongoing system administration and eliminating internal turnover risk that can plague “in house” development. Many of these providers use secure cloud technology that is faster and cheaper, and takes responsibility for integration issues.  

Purchased software is updated regularly with ongoing maintenance, functionality and new features to remain competitive, using feedback and experiences gained from working with institutions of varying size and complexity. Engaging a vendor can also free up the internal team’s resources so they can focus on the data use strategy and analyzing data following implementation. Purchased solutions typically promotes accessibility throughout the institution, allowing for broad usage.

But selecting the criteria is a critical and potentially time-consuming endeavor. Vendors may also offer limited customization options and pose potential for integration issues. Additionally, time-based subscriptions and licenses may experience cost growth over time; pricing based on users could make adoption across the institution more costly, lessening the overall effectiveness.

Building a data analytics tool offers the ability to customize and prioritize development efforts based on a bank’s specific needs; controllable data security, depending on what tools the bank uses for the build and warehousing; and a more readily modifiable budget.

But software development is not your bank’s core business. Building a solution could incur significant upfront and ongoing cost to develop; purchased tools appear to have a large price tag, but building a tool incurs often-overlooked costs like the cost of internal subject matter experts to guide development efforts, ongoing maintenance costs and the unknowns associated with software development. These project may require business intelligence and software development expertise, which can carry turnover risk if institutional knowledge leaves the bank.

Projects of this magnitude require continuous engagement from management subject matter experts. Bankers needed to provide the vision and banking content for the product — diverting management’s focus from other responsibilities. This can have a negative impact on company productivity.

Additionally, “in-house” created tools tend to continue to operate in data silos whereby the tool is accessible only to data team. Ongoing development and releases may be difficult for an internal team to manage, given their limited time and resources along with changing business priorities and staff turnover.

The question remains: Do you have the bandwidth and talent at your bank to take on a build project? These projects typically take longer than expected, experience budget overruns and often do not result in the desired business result. Your bank will need to make the choice that is best for your institution.