How One Bank Flattened Fraud

Argo.pngProtecting the bank and its customers — through cybersecurity measures, identity verification, fraud detection and the like — is vital in ensuring a financial institution’s safety and soundness, as well as its reputation in the marketplace. These investments typically represent significant cost centers, but fraud prevention tools can be an exception to the rule if they’re able to pay for themselves by preventing losses.

The idea is, when you put in a fraud system — and this is where some folks lose it — you want to make sure to catch more fraud than the system costs,” says Ronald Zimmerman, vice president in the operations department at $32.2 billion IBERIABANK Corp., based in Lafayette, Louisiana. “You always have to make sure that the cost doesn’t supersede your savings.”

Zimmerman implemented ARGO OASIS about a year ago. OASIS, which stands for Optimized Assessment of Suspicious Items, uses neural networks and image analytics to detect and prevent fraud. Modeled after the human brain, neural networks are a form of artificial intelligence designed to recognize patterns, making it well suited to identify check alterations, forgeries and other forms of transaction fraud. The solution then provides bank employees with detailed information to enable them to further investigate the activity.

Bank Director’s 2020 Risk Survey found that just 8% of executives and directors report that their bank uses AI technology to improve compliance. One-third are exploring these types of solutions.

IBERIA brought in OASIS to identify fraud in its “two-signature accounts” — customer accounts that require two signatures on a high-dollar check. “We have a queue set up in OASIS to monitor these checks as they come in through clearing. If a signature is missing or is in question, OASIS flags it for review,” Zimmerman says.

One thing about the technology that sets it apart is its check stock validation tool. “You have an overlay button where you can place a questioned check on top of a good check, and you have a little slide bar [so you] can see the small differences,” he says.

That tool alone has helped the bank stop roughly $300,000 in check fraud over the first eight months of use — meaning ARGO has already paid for itself. “We’ve caught a ton of fraud through this product,” says Zimmerman.

And $300,000 is a conservative estimate of the bank’s savings, Zimmerman says, because fraudsters have learned not to target his bank. “Check fraud flattened out, because the fraudsters have probably moved on, knowing that we’ve covered up a hole that was there before.”

ARGO OASIS was recognized as the Best Solution for Protecting the Bank at the 2020 Best of FinXTech Awards in May. ALTR, a blockchain-based security solution, and IDology, which uses big data for identity verification and fraud detection, were also finalists in the category.

Importantly, ARGO helps IBERIA stop fraud efficiently. A task that used to occupy three full-time employees’ time now takes two employees just a couple of hours.

IBERIA will soon merge with Memphis, Tennessee-based First Horizon National Corp. to form a $75 billion company. The deal was driven in part by the pursuit of scale.

Generating efficiencies is essential to better compete with big banks, said First Horizon CEO Bryan Jordan in a 2017 presentation. “We’ve got to be invested in technologies in such a way that we’re at or above table stakes,” he said. “The trick for us will be to … create efficiency in other parts of the business to create money that we can invest in leading-edge technologies and processes that really allow us to be competitive.”

Leveraging AI to reduce compliance busywork is a great place to start.

A Small Bank’s Big Bet on AI

Brex.pngBuilding a board with an appetite for innovation can be difficult, but the small group that oversees C3bank is decidedly different.

The institution was originally founded as a quiet community bank serving the Inland Empire region of southern California in 1981.

That same year, Evert “Chooch” Alsenz and Paul Becker, now board members at C3bank, formed an engineering partnership that would go on to fund the development of the world’s first quartz-based solid-state gyroscope, a patented technology used in brake systems for millions of automobiles. Subsequent ventures from the duo produced military communications antennas, lightning diversion strips and surge protection equipment for aircrafts.

Alsenz and Becker are no strangers to invention, a background they brought with them when they joined commercial real estate expert Michael Persall to buy C3bank in a deal that closed in 2014.

Alsenz and Becker’s shared history helps one understand how a four-branch, $356 million institution has been able to remake itself as a tech-savvy commercial bank. From the moment they acquired it, Persall, Alsenz and Becker, who also serve as principals for investment company ABP Capital, worked to transform the bank into an entrepreneurial shop with a specialty in commercial real estate lending. In 2019, the group moved the bank’s headquarters to Encinitas, California, where ABP is based, and changed its name to C3bank.

Understanding the entrepreneurial owners at C3bank also helps explain how the group was able to ink a new partnership to develop an artificial intelligence-based commercial lending tool just a few years after the change in ownership.

To strengthen the bank’s CRE lending program, bank chairman Persall approached technologist Shayne Skaff to develop a custom platform for assessing and monitoring CRE loans. Initially, Skaff wasn’t sold on the idea. When he dug deeper, though, he discovered that commercial lending technology was years behind the solutions for residential loans. That lag presented an opportunity, so he started working with the teams at ABP Capital and C3bank in June 2018 to build a solution that would eventually become known as Blooma.

Skaff brought developers into the institutions to learn about their respective underwriting processes. The goal for the project was to streamline the commercial underwriting process in a way that made it more dependent on science, than on art. Science, the parties believed — in this case, AI —  would lead to thorough, well-researched deals.

Our board and ownership group continues to think AI can have a big impact on banking,” says A.J. Moyer, the CEO of C3bank. “[They] push that thought process and believe a lot of underwriting can be supplemented.”

Traditionally, lenders spend a lot of time manually gathering the data that factors into a potential deal. Blooma allows banks to outsource that process to its AI engines. It taps into third party databases to extract information about local real estate markets and scours the web for other relevant information, such as neighborhood crime statistics and negative news.

Blooma then scores CRE deals on a 100-point scale that measures the probability that it will fit within the bank’s risk profile and portfolio needs. Users can drill down into the score to see exactly what factors influenced the score. As more deals pass through the system, Blooma’s AI gradually learns from the bank’s process to prioritize new opportunities.

The result? The process of onboarding and assessing a potential deal can shrink from weeks to minutes.

“[Q]uick yet accurate decision-making can be a strategic advantage for your institution,” says Moyer. “If I have a toolset that, when a potential deal comes my way, I can quickly confirm what that asset’s worth, [then] I can sign that deal faster than anyone else.”

In addition to the underwriting assist, Blooma provides a digital hub for managing deal documents and workflows. “We’ve gotten out of a spreadsheet environment,” says Moyer. “The world we’re in is more dynamic. Everyone can go [to Blooma] to see what deals we’re working on and what’s mission critical.”

Blooma was a finalist in the Best Business Solution category of this year’s Best of FinXTech Awards. Shield Compliance, a Seattle-based fintech helping institutions bank cannabis-related businesses, was also a finalist. The winner in this category was Brex, which partnered with Bank of the West to launch a small business-focused credit card that’s grown the bank’s revenue by more than 50% from clients using the co-branded card. You can learn more about that partnership here: How Innovative Banks Cards to Grow Revenue, Earn Loyalty.

Practical AI Considerations for Community Banks

A common misconception among many community bankers is that it isn’t necessary to evaluate (or re-evaluate for some) their use of artificial intelligence – especially in the current market climate.

In reality, these technologies absolutely need a closer look. While the Covid-19 crisis and Paycheck Protection Program difficulties put a recent spotlight on outdated financial technology, slow technology adoption is a long-standing issue that is exacerbating many concerning industry trends.

Over the last decade, community banks have faced massive disruption and consolidation — a progression that is likely to continue. It’s imperative that bank executives take a clear-eyed look at how advanced technologies such as AI can support their business objectives and make them more competitive, while gaining a better understanding of the requirements and risks at play.

Incorporating AI to Elevate Existing Business Processes
This may seem like a contrarian view, but banks do not need a specific, stand-alone AI strategy. The value of AI is its ability to improve upon existing structures and processes. Leadership teams need to be involved in the development process to identify opportunities where AI can tangibly drive business objectives, and manage expectations around the resources necessary to get the project up and running.

For example, community banks should review how AI can automate efficiencies into their existing compliance processes — particularly in the areas of anti-money laundering and Bank Secrecy Act compliance. This application of AI can free up manpower, reduces error rates and help banks make informed decisions while better serving their customers.

It’s necessary to have a strong link between a bank’s digital transformation program and AI program. When properly incorporated, AI helps community financial institutions better meet rising customer expectations and close the gap with large financial institutions that have heavily invested in their digital experiences.

Practical Steps for Incorporating AI
Once a bank decides the best path forward for implementing AI, there are a few technical and organizational steps to keep in mind:

Minimizing Technical Debt and “Dirty Data”: AI requires vast amounts of data to function. “Dirty data,” or information containing errors, is a real possibility. Additionally, developers regularly make trade-offs between speed and quality to keep projects moving, which can result in greater vulnerability to crashes. Managing these deficiencies, “or technical debt,” is crucial to the success of any AI solution. One way to minimize technical debt is to ensure that both the quantity and quality of data taken in by an AI system are carefully monitored. Organizations should also be highly intentional about the data they collect.More isn’t always better.

Minimizing technical debt and dirty data is also key to a smooth digital transformation process. Engineers can add value through new and competitive features rather than spending time and energy addressing errors — or worse, scrapping the existing infrastructure altogether.

Security & Risk Management: Security and risk management needs to be top-of-mind for community bankers any time they are looking to deploy new technologies, including leveraging AI. Most AI technologies are built by third-party vendors rather than in-house. Integrations can and likely will create vulnerabilities. To ensure security and risk management are built into your bank’s operating processes and remain of the highest priority, chief security officers should report directly to the CEO.

Managing risks that arise within AI systems is also crucial to avoid any interruptions. Effective risk management ties back to knowing exactly how and why changes affect the bank’s system. One common challenge is the accidental misuse of sensitive data or data being mistakenly revealed. Access to data should be tightly controlled by your organization.

Ongoing communication with employees is important since they are the front line when it comes to spotting potential issues. The root cause of any errors detected should be clearly tracked and understood so banks can make adjustments to the model and retrain the team as needed.

Resource Management: An O’Reilly Media survey from 2018 found that company culture was the leading impediment to AI adoption in the financial services sector. To address this, leaders should listen to and educate employees within each department as the company explores new applications. Having a robust change management program — not just for AI but for any digital transformation journey — is absolutely critical to success. Ongoing education around AI efforts will help garner support for future initiatives and empower employees to take a proactive role in the success of current projects.

At a glance, implementing AI technologies may seem daunting, but adopting a wait-and-see approach could prove detrimental — particularly for community banks. Smaller banks need to use every tool in their toolkit to survive in a consolidating market. AI poses a huge opportunity for community banks to become more innovative, competitive and prosperous.

AI: The Slingshot for Small Banks

Regional and community banks are struggling with growth and profitability in the face of competitive pressure from large national banks and fintech startups. Executives at these institutions are instructed to invest in technology, and to leverage data and artificial intelligence to compete more effectively.

While that sounds good, smaller banks are often constrained by a dependence on legacy core vendors that limits their IT potential, encounter difficulties in accessing their own data, lack skilled data scientists, and have no clear vision on where to start.

This conundrum came up during Bank Director’s 2020 Acquire or Be Acquired conference in Phoenix. I rubbed shoulders with fellow conference attendees over the course of three days, sharing ideas about the state of the banking sector and how community banks could leverage data and AI to drive business results. The talent gap was a frequent topic. Perhaps unsurprisingly, only a miniscule number of community banks have hired data scientists. The majority of banks have not prioritized data science capabilities; the few who are actively recruiting data scientists are struggling to attract the right talent.

But even if community banks could arm up with data scientists, what volume of data will they be working on to derive insights to fuel their business strategy? A $1 billion asset bank may have 50,000 to 75,000 customers — not a lot of data to start with. Furthermore, a number of bankers point to the difficulties they encounter in accessing their data in their legacy core systems.

As we were having these discussions, conversations were raging about the need for smaller banks to prepare for an existential threat. At the World Economic Forum in Davos, attendees were assessing comments from Bank of America Corp. Chairman and CEO Brian Moynihan that the bank could double its U.S. consumer market share. Back-of-envelope calculations indicate that if Bank of America manages to accomplish that growth, more than 1,000 community banks could be out of business. Can technology enable these banks to retain their core customer base, grow and avoid this fate? I think so.

One promising area of AI application for community banks is loan and deposit pricing. Community banks have little or no analytic tools beyond competitive rate surveys; most rely on anecdotal feedback from customers and front-line bankers. But price setting and execution on both assets and liabilities is one of the most important levers a bank can use to drive both growth and improve its net interest margin. Community banks should take advantage of new tools and data to level the playing field with the big banks, which are already well ahead of them in adopting price optimization technology.   

Small banks can gain the upper hand in this “David versus Goliath” scenario because accessible cloud-based technology works in their favor. True, big banks have worked with price optimization technology and leveraged large amounts of customer behavioral data for years. But community banks tend to have stronger customer relationships and often better pricing power than their larger competitors. Now is the time for community banks to take control of their destiny by adopting new technology and tools so they can better compete on price.

There are three reasons why cloud computing and the power of AI will be the slingshot of these ‘David’ banks:

  1. Scalable computing power, instantly on tap. Cloud-based computing and pre-configured pricing solutions are affordable and can be implemented in days, not months.
  2. Big data — as a service. Community banks can quickly leverage AI-based pricing models that have been trained on hundreds of millions of transactions. There is no need to build their own analytic models from a small customer footprint.
  3. Plug-and-play IT. It’s much easier today to integrate cloud-based platforms with a bank’s core system providers, which makes accessing their own data and implementing smarter pricing feasible.

Five years ago, it would have seemed crazy to think that in 2020, community banks would be applying AI to compete against the nation’s top banks. But the first wave of early adopters are already deploying AI for pricing. I predict we’ll see more institutions embracing AI and machine learning to improve their NIM and increase growth over the coming years.

Artificial Intelligence: Exploring What’s Possible

Banks are exploring artificial intelligence to bolster regulatory compliance processes and better understand customers. This technology promises to expand over the next several years, says Sultan Meghji, CEO of Neocova. As AI emerges, it’s vital that bank leaders explore its possibilities. He shares how banks should consider and move forward with these solutions. 

  • Common Uses of AI Today
  • Near-Term Perspective
  • Evaluating & Implementing Solutions

 

Using Intelligent Automation to Bank Smarter, Not Harder


technology-5-4-19.pngBy this point in 2019, most consumers and companies are somewhat familiar with the concept of artificial intelligence. Executives and consultants have discussed its application in financial services for years; lately, the conversations have been brisk and some organizations are doing more than just talking. Many tangible AI use cases have emerged at financial institutions of all sizes over the last 12 months, and intelligent technology is beginning to make an impact on banks’ productivity and bottom lines.

Still, AI remains a largely abstract concept for many institutions. Some of the biggest challenges these banks face in preparing and executing an AI strategy starts with having a too-narrow definition of these technologies.

Technically, AI is the ability of machines to use complex algorithms to learn to do tasks that are traditionally performed by humans. It is often misrepresented or misunderstood in broader explanations as a wider range of automation technologies — technologies that would be more appropriately characterized as robotics or voice recognition, for example.

Banks interested in using intelligent automation, which includes AI, robotic process automation, and other smart technologies, should target areas that could benefit the most through operational efficiencies or speed up their digital transformation.

Banks are more likely to achieve their automation goals if executives shift their mindsets toward thinking about ways they can apply smart technologies throughout the institution. Intelligent automation leverages multiple technologies to achieve efficiency. Some examples include:

  • Using imaging technology to extract data from electronic images. For example, banks can use optical character recognition, or OCR, technology to extract information from invoices or loan applications, shortening the completion time and minimizing errors.
  • Robotic process automation, or RPA, to handle high-volume, repeatable manual tasks. Many institutions, including community banks with $180 million in assets up to the largest institutions in the world have leveraged RPA to reduce merger costs, bundle loans for sale and close inactive credit and debit cards.
  • Machine learning or AI to simulate human cognition and expedite problem solving. These applications can be used in areas ranging from customer service interactions to sophisticated back-office processes. Some industry reports estimate that financial institutions can save $1 trillion within the next few years through AI optimization. Several large banks have debuted their own virtual assistants or chatbots; other financial institutions are following suit by making it easier and more convenient for customers to transact on the go.

What are next steps for banks interested in using AI? Banks first need to identify the right use cases for their organization, evaluating and prioritizing them by feasibility and business need. It’s more effective to start with small projects and learn from them. Conduct due diligence to fully assess each project’s complexity, and plan to build interactively. Start moving away from thinking about robots replacing employees, and start considering how banking smarter – not harder – can play out in phases.

Drafting a Data Strategy


data-4-29-19.pngBanks need to be aware of trends in data analytics that are driving decision-making and customer experience so they can draft an effective data plan. Doing so will allow them to compete with the biggest banks and non-bank technology competitors that are already using internal customer data to predict behavior and prescribe actions to grow those relationships. These approaches leverage concepts like machine learning and artificial intelligence — buzzwords that may seem intimidating but are processes and approaches that can leverage existing information to grow and deepen customer relationship and profitability.


analytics-4-29-19-tb.png10 Data and Analytics Trends Banks Should Consider
Current trends in analytics include focusing on the customer’s experience, using artificial intelligence and machine learning in analysis, and storing and organizing information in ways that reduce risk. Banks also need to know about threats like cybersecurity, long-term developments like leveraging blockchain, and how to build a governance program around the process. Knowing the trends can help companies make educated choices when implementing a data strategy.

datat-trends-4-29-19-tb.pngHow Banks Can Make Use of Data-Driven Customer Insight
Banks can use machine learning and artificial intelligence to gain insights into customer behavior and inform their decisions. These data-driven approaches can efficiently analyze the likeliness of future events, as well as suggest actions that would increase or decrease that likeliness. Many institutions recognize the need for new technical capabilities to improve their customer insight, but a significant percentage struggle to embrace or prioritize the technology among other priorities at their bank. These institutions have an opportunity to establish a data strategy, map out their internal information and establish appropriate governance that surrounds the process.

How Analytics and Automation Can Improve Shareholder Value


automation-2-8-19.pngAdvanced data science technologies like artificial intelligence (AI), machine learning and robotic process automation are delivering significant benefits to many banks.

As part of their mandate to protect shareholder value and improve financial performance, bank directors can play an important role in the adoption of these promising new technologies.

Technology’s expanding influence
With fintech companies generating new competitive pressures, most traditional banks have recognized the need to adopt some new techniques to meet changing customer habits and expectations. Declines in branch traffic and increased online and mobile banking are the most obvious of these trends.

Yet, as important as service delivery methods are, they are in a sense only the top layer of bigger changes that technology is bringing to the industry. New data-intensive tools such as AI, machine learning and robotic process automation can bring benefits to nearly all areas of a bank, from operations to sales and marketing to risk and compliance.

Advanced data analytics can also empower banks to develop deeper insights and make better, more informed strategic decisions about their customers, products and service offerings.

The power of advanced analytics
Historically, business data systems simply recorded and reported what happened regarding a customer, an account, or certain business metrics. The goal was to help managers understand what had happened and develop strategies for improving performance.

Today’s business intelligence systems advance this to predictive analysis – suggesting what is likely to happen in the future based on what has been observed so far. The most advanced systems go even further to prescriptive analysis – recommending or implementing actions that increase or decrease the likelihood of something happening.

For example, AI systems can be programmed to identify certain customer characteristics or transaction patterns, which can be used for customer segmentation. Based on these patterns, a bank can then build predictive models about those customer segments’ likely actions or behaviors – such as closing an account or paying off a loan early.

Machine learning employs algorithms to predict the significance of these customer patterns and prescribe an appropriate response. With accurate segmentation models, a bank can tailor marketing, sales, cross-selling and customer retention strategies more precisely aligned to each customer.

Automating these identification, prediction, and prescription functions frees up humans to perform other tasks. Moreover, today’s advanced analytics speed up the process and can recognize patterns and relationships that would go undetected by a human observer.

Industry leaders are using these tools to achieve benefits in a range of bank functions, such as improving the effectiveness of marketing and compliance functions. Many large banks already use predictive modeling to simplify stress testing and capital planning forecasts. AI and machine learning technology also can enhance branch operations, improve loan processing speeds and approval rates and other analytical functions.

Getting the data house in order
While most banks today are relatively mature in terms of their IT infrastructures and new software applications, the same levels of scrutiny and control often are not applied to data itself. This is where data governance becomes crucially important – and where bank directors can play an important role.

Data governance is not just an IT problem. Rather, it is an organization-wide issue – and the essential foundation for any advanced analytics capabilities. As they work to protect and build shareholder value, directors should stay current on data governance standards and best practices, and make sure effective data governance processes, systems and controls are in place.

AI, machine learning, and robotic process automation are no panacea, and banks must guard against potential pitfalls when implementing new technology. Nevertheless, the biggest risk most banks face today is not the risk of moving too quickly – it’s the risk of inertia. Getting started can seem overwhelming, but the first step toward automation can go a long way toward taking advantage of powerful competitive advantages this technology can deliver.

What Bankers Should Know About Conversational AI in 2019


omnichannel-12-21-18.pngWe’ve come a long way since filmgoers watched nervously as the computer “Hal” struck out on his own with the bland yet threatening response, “I’m sorry Dave, I’m afraid I can’t do that,” in Stanley Kubrick’s “2001: A Space Odyssey.”

Today, humans are comfortable interacting with machines. Twenty-five percent of customer service and support operations will integrate virtual customer assistant (VCA) or chatbot technology by 2020, up from less than 2 percent in 2017, according to Gartner, Inc. And in some cases, consumers seem to prefer machines to humans. Therapy bots like Woebot are successful in part because users don’t experience the fear of judgment that may exist speaking with another human.

The technology that enables machine-to-human interactions is known as conversational AI. It powers virtual assistants across apps, websites, messaging and smart speakers. In 2018, we saw virtual assistants take off in banking – finding their way into the apps and websites of the world’s largest banks. Pilots turned into production, and virtual assistants started engaging with real consumers at scale.

This technology is a growth engine for banks by servicing customers more efficiently, engaging customers to boost brand loyalty and acquiring customers to increase their lifetime value. But all conversational AI solutions are not the same.

Here are three key trends for banks implementing conversational AI in 2019.

Think omnichannel, not multichannel
Consumers’ expectations for banking are evolving from siloed multichannel experiences to deeply personalized omnichannel experiences. They expect the experience with their bank to be consistent and informed, no matter which channel they interact on, and they expect to move smoothly between channels. Banks implementing conversational AI should support “channel traveling” and never lose sight of who the customer is – not just their unique ID, but their preferences, history and more.

Make sure your solution supports sophisticated customer journeys and hand-offs between channels. Your customer should be able to start a conversation with your virtual assistant on Amazon Alexa, and the virtual assistant should be smart enough to follow up with more related details in the mobile app. The virtual assistant knows the optimal interaction model for each channel and generates the right response for the channel of choice.

Conversations that explain “why”
By now, consumers are accustomed to automated assistants that respond to them. A virtual assistant that answers questions has become table stakes. In 2019 and beyond, we’ll see consumers gravitate toward services that can give them answers to questions and explain their finances. People will come to expect answers to “why” in addition to “what.”

For example, customers will want to know their balance, but also why it is lower than expected. Or, they may ask if they can afford a vacation now, and if they could still afford it in six months. They’ll want to know their FICO score, and why it is lower than last year.

Banking customers already know chatbots can give their balance and move their money. In 2019, their expectation will be that conversational AI will do more to help manage their money with context and insights.

The era of available data is here
After years of waiting for banking data to be available, the future is finally here. Inspired by regulations such as PSD2, or the Payment Services Directive, in the European Union, large banks around the world are adopting open banking standards and launching modern developer portals that enable a new world of banking services. This is good for conversational AI, because its real value comes from personalized, actionable experiences—experiences that require data and services. With financial institutions such as Wells Fargo, Citi, Mastercard and Standard Chartered streamlining access to APIs, building meaningful conversational experiences and integrating them with the banks’ other services will be much easier and faster.

In 2018, we’ve seen conversational AI is here to stay, and in 2019, we need to make virtual assistants do more than respond to FAQs and complete simple tasks. Banks implementing conversational AI should remember consumer expectations are growing every year. To meet those expectations, leverage the abundance of available data via APIs to create omnichannel customer journeys that can understand your customers and explain the context to them.

Driving Efficiency Through Automation



Automation makes it possible for banks to gain efficiencies and help their employees be more effective. But how can bank leaders ensure they’re getting the most out of these solutions? Richard Milam, the CEO of the software company Enablesoft, explains that people—not technology—will drive success in these efforts, and culture plays an important role.

  • How Banks Use Automation Today
  • Successfully Deploying Automation
  • Advice for Bank Leaders