A Lending Platform Prepared for Pandemic Pitfalls

Managing a loan portfolio requires meticulous review, careful documentation and multiple levels of signoff.

That can often mean tedious duplication and other labor-intensive tasks that tie up credit administration staffers. So, when Michael Bucher, chief credit officer at Lawton, Oklahoma-based Liberty National Bank, came across a demonstration of Teslar Software’s portfolio management system, he couldn’t believe it. The system effortlessly combined the most labor-intensive and duplicative processes of loan management, stored documents, tracked exceptions and generated reports that allowed loan and credit officers to chart trends across borrowers. The $738 million bank signed a contract at the end of 2019 and began implementation in February 2020.

That was fortuitous timing.

Teslar Software’s partnership with institutions like Liberty National, along with its efforts to assist banks and borrowers with applications for the Small Business Administration’s Paycheck Protection Program, earned it the top spot in the lending category in Bank Director’s 2021 Best of FinXTech Awards. Finalists included Numerated — a business loan platform that was another outperformer during the PPP rollout — and SavvyMoney, which helps banks and credit unions offer pre-qualified loans through their digital channels. You can read more about Bank Director’s awards methodology and judging panel here.

Prior to implementing Teslar Software, Liberty National used a standalone platform to track every time a loan didn’t meet the bank’s requirements. It was an adequate way to keep track of loan exceptions when the bank was smaller, but it left him wondering if it would serve the bank’s needs as it continued to grow. The old platform didn’t communicate with the bank’s Fiserv Premier core, which meant that when the bank booked a new loan, a staffer would need to manually input that information into the system. The bank employed one person full-time to keep the loan tracking system up-to-date, reconcile it with the core and upload any newly cleared exceptions on various loans.

Bucher says it was immediately apparent that Teslar Software offered efficiency gains. Its system can integrate with several major cores and is refreshed daily. It collects documentation that different areas within the bank, like commercial loan officers and credit administration staff, can access, allows the bank to set loan exceptions, clears them and finalizes the documentation so it can be imaged and stored in the correct location. Staffers that devoted an entire day to cumbersome reconciliation tasks now spend a few hours reviewing documentation.

Bucher was also impressed by the fintech’s approach to implementation and post-launch partnership. The bank is close enough to Teslar Software’s headquarters in Springdale, Arkansas, that founder and CEO Joe Ehrhardt participated in the bank’s implementation kickoff. Teslar Software’s team is comprised of former bankers who leveraged that familiarity in designing the user’s experience. Between February and June of 2020, the earliest months of the coronavirus pandemic, Teslar Software built the loan performance reports that Liberty National needed, and made sure the core and platform communicated correctly. Weekly calls ensured that implementation was on track and the reports populated the correct data.

Teslar Software’s platform went live at Liberty National in June — missing the bulk of the bank’s first-round PPP loan issuance. But Teslar Software partnered with Jill Castilla, CEO of Citizens Bank of Edmond, and tech entrepreneur and NBA Dallas Mavericks owner Mark Cuban to power a separate website called PPP.bank, a free, secure resource for multiple banks to serve PPP borrowers.

“Teslar Software came to the rescue when they provided their Paycheck Protection Program application tool to all community banks during a period of extreme uncertainty for small businesses due to the Covid-19 pandemic,” Castilla says in a statement to Bank Director. “The partnership we forged with them and Mark Cuban was a game changer for so many that were in distress.”

And Liberty National was able to use Teslar Software’s platform to create and process forgiveness applications for the 500 first-round PPP loans it made. Bucher says the forgiveness application platform is similar to the tax preparation software TurboTax — it breaks the complex application down into digestible sections and prompts borrowers to submit required documents to a secure portal. The bank needs only one employee to review these applications.

“We had such a good experience with the forgiveness side that for PPP in 2021, we partnered with them to handle the front end and the back end of PPP [application],” he says. “It’s now all centralized within Teslar so that when we move on to forgiveness, everything is going to be there. I’m expecting the next round of forgiveness to go a lot smoother than the previous round.”

Outside of PPP, Teslar Software has allowed Liberty National’s credit administration team to manage its current workload, even as staffing decreased from 10 people to six. Instead of taking a full day to review and verify loan exceptions, it takes only a few hours. Bucher says the bank is exploring an expanded relationship with the fintech to add additional workflow modules that would reduce duplication and eliminate the use of email to share documents.

Banks Risk Missing This Competitive Advantage

Artificial intelligence is undergoing an evolution in the financial services space: from completely innovative “hype” to standard operating technology. Banks not currently exploring its many applications risk being left behind.

For now, artificial intelligence remains a competitive advantage at many institutions. But AI’s increasing adoption and deployment means institutions that are not currently investing and exploring its capabilities will eventually find themselves at a disadvantage when it comes to customer satisfaction, cost saves and productivity. For banks, AI is not an “if” — it’s a “when.”

AI has proven use cases within the bank and credit union space, offering a number of productivity and efficiency gains financial institutions  are searching for in this low-return, low-growth environment. The leading drivers behind AI adoption today are improvements in customer experience and employee productivity, according to a 2020 report from International Data Corporation. At Microsoft, we’ve found several bank-specific applications where AI technology can make a meaningful impact.

One is a front-office applications that create personalized insights for customers by analyzing their transaction data to generate insights that improve their experience, like a charge from an airline triggering a prompt to create a travel notification or analyzing monthly spend to create an automated savings plan. Personalized prompts on a bank’s mobile or online platform can increase engagement by 40% and customer satisfaction by 37%; this can translate to a 15% increase in deposits. Additionally, digital assistants and chatbots can divert call center and web traffic while creating a better experience for customers. In some cases, digital assistants can also serve as an extension of a company’s brand, like a chatbot with the personality of “Flo” that auto insurer Progressive created to interact with customers on platforms like Facebook, chat and mobile.

Middle-office fraud and compliance monitoring are other areas that can benefit from AI applications. These applications and capabilities come at a crucial time, given the increased fraud activity around account takeovers and openings, along with synthetic identity forgery. AI applications can identify fraudsters by their initial interaction while reducing enrollment friction by 95% and false positives by 30%. In fact, IDC found that just four use cases — automated customer service agents, sales process recommendation and automation, automated threat intelligence and prevention and IT automation — made up almost a third of all AI spending in 2020.

There are several steps executives should focus on after deciding to implement AI technology. The first is on data quality: eliminating data silos helps to ensure a unified single view of the customer and drives highly relevant decisions and insights. Next, its critical to assemble a diverse, cross-functional team from multiple areas of the bank like technology, legal, lending and security, to explore AI’s potential to create a plan or framework for the bank. Teams need to be empowered to plan and communicate how to best leverage data and new technologies to drive the bank’s operations and products.

Once infrastructure is in place, banks can then focus on incorporating the insights AI generates into strategy and decision-making. Using the data to understand how customers are interacting, which products they’re using most, and which channels can be leveraged to further engage — unlocking an entire new capability to deliver business and productivity results

In all this, bank leadership and governance have an important role to play when adopting and implementing technology like AI. Incorporating AI is a cultural shift; executives should approach it with constant communication around AI’s usage, expectations, guiderails and expected outcomes. They must establish a clear set of governance guiderails for when, and if, AI is appropriate to perform certain functions.

One reason why individual banks may have held off exploring AI’s potential is concern about how it will impact current bank staff — maybe even replace them. Executives should “demystify AI” for staff by offering a clear, basic understanding of AI and practical uses within an employee’s work that will boost their productivity or decrease repetitive aspects of their jobs. Providing training that focuses on the transformational impact of the applications, and proactively creating new career paths for individuals whose roles may be negatively impacted by AI show commitment to employees, customers, and the financial institution.

It is critical that executives and managers are aligned in this mission: AI is not an “if” for banks, it is “when.” Banks that are committed to making their employees’ and customers’ lives better should seriously consider investing in AI capabilities and applications.

 

Build Versus Buy Considerations for Data Analytics Projects

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.

Realign Your Bank’s Operating Model Before It’s Too Late


core-6-19-18.pngThe banking industry and its underlying operating model is facing pressure from multiple angles. The advent of new technologies including blockchain and artificial intelligence have started and will continue to impact the business models of banks.

Meanwhile, new market entrants with disruptive business models including fintech startups and large tech companies have put pressure on incumbent banks and their strategies. A loss of trust from customers has also left traditional banks vulnerable, creating an environment focused on the retention and acquisition of new clients.

In response to looming industry challenges, banks have begun to review and adapt their business models. Many banks have already adjusted to the influence of technology, or are in the process of doing so. Unfortunately, corresponding changes to the underlying operating models often lag behind technology changes, creating a strong need to re-align this part of the bank’s core functions.

So what does “re-align” mean from an IT architecture point of view?

Impact on System
In order to keep up with the fast-paced digital innovation, investments have largely focused on end-user applications. This helped banks to be seen as innovative and more digital friendly. However, in many cases these actions led to operational inefficiencies and there are several reasons why we see this.

One is a lack of integration between applications, resulting in siloed data flow. More often, though, the reason is the legacy core, which does not allow seamless integration of tools from front to back of an organization. Further, M&A activity has led many banks to have several core legacy systems, and often these systems don’t integrate well or exist with multiple back-end systems that cater to a specific set of products. This complicates the creation of a holistic view of information for both the client and financial advisor.

There are two ways of addressing the above-mentioned challenges to remain successful in the long-run:

  1. Microservice driven architecture
  2. Core Banking System modernization

Microservice-driven architecture
Establishing an ecosystem of software partners is important to be able to excel amid rapid innovation. Banks can’t do all the application development in house as in the past. Therefore, a microservice-driven architecture or a set of independent, yet cohesive applications that perform singular business functions for the bank.

The innovation cycles of core banking systems are less frequent than innovation cycles for client- and advisor-facing applications. To guarantee seamless integration of the two, build up your architecture so it fully supports APIs, or application programming interfaces. The API concept is nothing new; however, to fully support APIs, the use of standardized interfaces will enable seamless integration and save both time and money. This can be done through a layer that accommodates new solutions and complies with recent market directives such as PSD2 in Europe.

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Core Banking System Modernization
Banks are spending a significant amount of their IT budget on running the existing IT systems, and this allows only specific parts go into modernization.

A simple upgrade of your core banking system version most likely won’t have the desired impact in truly digitizing processes from front to back. Thus, banks should consider replacing their legacy core banking system(s) to build the base layer of future innovation. This can offer new opportunities to consolidate multiple legacy systems, which can reduce operational expenditures while mitigating operational risks. In addition, a core banking replacement allows for the business to scale much easier as it grows.

A modern core banking system is designed and built in a modular way, allowing flexiblity to decide whether a specific module will be part of the existing core or if external solutions will be interfaced instead, resulting in a hybrid model with best-of-breed applications in an all-in-one core banking system.

Investing In Your Core Can Save You
Core banking system modernization and adoption of the microservice-driven architecture are major investments in re-aligning a bank’s operating model. However, given the rapid technological innovation cycles, investments will pay off in improved operational efficiency and lower costs.

Most importantly, re-aligning the operating model will increase the innovation capabilities, ultimately resulting in a positive influence on the top line through better client experiences.

IoT: Is Your Bank Ready?


internet-of-things-11-1-17.pngWhat if your fridge could sense the absence of a milk container and automatically reorder the milk for delivery? What if your car could sense the deflation of a tire, alert the driver and order roadside assistance service? IoT, or the internet of things, is a sensor-based technology that connects objects with sensors embedded in them for data transmission and monitoring over the internet.

IoT is making a lot of this possible. Bank boards should get ready for a future where many more devices are connected through the internet, which will increase exponentially the amount of transactions going through banks. Many of the security questions raised by the IoT-connected world have not been answered yet.

These sensors send and receive signals and carry interactions to and from other IoT devices or systems enabled with IoT technology. So, important implications of this technology are very large and continuous volumes of data flowing from IoT devices and impacting banking systems.

Some examples of impacts to banking systems include:

  • Banks will be improving features and capabilities to support more sophisticated consumer-based transaction processing, including IoT-based transactions.
  • With new banking technology integration and infrastructure investment, consumers will have increased access to detailed information regarding our most important IoT-based transactions and more options to manage finances surrounding these transactions.
  • Consumers will see new transaction reporting for IoT in our banking consoles.

Also, since IoT is an integrated form of data and information transmission, many new types of devices beyond common types such as cell phones, tablets and other kinds of mobile devices have the potential to tap into banking infrastructure.

Newer devices like refrigerator consoles or onboard computer systems in vehicles have the capability to transmit transactions for purchases that impact today’s banking architecture.

By one estimate, the market for IoT platforms, software, applications and services will grow from $170.57 billion in 2017 to $561.04 billion by 2022, a compound annual growth rate of 26.9 percent.

So, because of this, customers will need additional services on the banking side of IoT transaction processing to understand what types of transactions (and from which devices) are included in their bank accounts. Many of today’s customers are used to real-time bank account information and portal login for easy viewing of transactions. So, it is very likely that this new IoT capability for banking would be expected to come in at the same level for all forms of consumer banking.

Understanding how banking computer systems and infrastructure will be adjusted and upgraded to accommodate the influx of IoT-enabled transactions will play a crucial role in supporting customers and clients globally. Consumers will be most impacted by changes in retail and consumer markets. However, business use of IoT for financial transaction flows is also a growing factor. So, the combined business and consumer IoT sensor-driven transaction flows is an exciting area of banking and computing convergence that holds great potential for new and emerging global markets.

The People Who Plan to Change Financial Services


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This article originally published inside The FinTech Issue of Bank Director digital magazine.

The world is filled with technology companies hoping to transform the financial industry. Of course, very few of them will. Not all ideas can overcome the substantial hurdles to become major commercial successes. We are not proposing here at Bank Director digital magazine to tell you who will be a success and who won’t be. But we do want to introduce you to some of the entrepreneurs who are proposing to reshape the world as we know it. These are people whose ideas are re-envisioning platforms and processes, people who are simplifying, unifying and upsetting conventional practices. These entrepreneurs really are shaking up traditional boundaries to help us all think about banking a little differently.

Christian Ruppe and Jared Kopelman

They are creating the driverless car of banking.

Using machine learning, this duo, who met as students at the College of Charleston, have built a platform for banks and credit unions to help millennials save without even thinking about it. Frustrated that fellow college students would get on a budget and then abandon it a few weeks later, 22-year-old Ruppe thought he could make the attainment of financial stability easier. Achieving financial health takes discipline and focus, like weight loss. But Ruppe reasoned that technology could help with financial health so it wasn’t so dependent on discipline and focus. If he could come up with a way to automate savings, debt payments and investments, many more people could realize the benefits of compounding over time to create wealth. “We are the self-driving car of banking,’’ Ruppe says.

There are several other automated savings applications on the market that use machine learning, such as Digit and Qapital, but most of those are sold directly to consumers, rather than through a financial institution. Monotto’s private label approach means the customer doesn’t pay for the product and never knows the platform doesn’t come from the bank. Monotto, a play on the words “money” and “auto,” can be integrated into mobile banking or online applications, sending well timed messages about refinancing the mortgage or buying a house, for example. Bear State Financial in Little Rock, Arkansas, a $2.2 billion asset bank, already has agreed to pilot the program. When customers sign up, the algorithm learns from their spending patterns and automatically pulls differing amounts from their checking accounts into their savings account using the bank’s core banking software, taking into consideration each customer’s transaction history. Individuals can set savings goals, such as buying a house or a car, and the platform will automatically save for them. For now, Monotto has received funding from friends and family, as well as an FIS-funded accelerator program. Eventually, the founders envision a platform that will also help you invest and pay down debt.

“You have someone who is solving a problem [for society] but figuring out how to solve it for the bank, as well,” says Patrick Rivenbark, the vice president of strategic partnerships at Let’s Talk Payments, a research and news site.

Zander Rafael

This student lender calculates the school’s ROI to determine eligibility for a loan.

With the rising cost of tuition, students who take out loans end up with an average of $30,000 in debt after college, leading to rising rates of delinquency. But what’s holding the schools accountable?

Alexander “Zander” Rafael, 32, and his team created Climb Credit in 2014 to service student loans based on the returns the college provides its graduates. This places Climb among a menagerie of fintech startups, like SoFi, LendEDU and CampusLogic, all trying to serve the student loan market.

Climb, which funds its loans through investors, stands out because it only works with schools that have a record of landing students jobs that “pay them enough to [cover the] cost of tuition,” says Rafael. In addition to evaluating the student, Climb also assesses the schools. If the institution passes Climb’s graduation and return on investment analysis, then its students are eligible for Climb loans and the school takes on some of the risk of the loan, receiving more money if more students pay them back.

Climb has grown by focusing on more non-traditional learning environments, like coding boot camps, where students invest $10,000 for a yearlong program to learn web development. According to Climb’s analysis, many of these students land jobs that pay up to $70,000. “The return was very strong,” says Rafael. Climb now works with 70 schools, including some two and four-year university programs.

Schools benefit because they can accept students that lack cosigners and who otherwise may have struggled to find a private loan elsewhere. Climb charges an average of 9 percent APR for the loans, but it can range from 7.59 percent to 23.41 percent.

With a $400 million lending capacity, Climb has raised a Series-A funding round of $2 million. But the idea has shown early promise, as Rafael adds that profitability is “within line of sight.”

Ashish Gadnis

Could this man be the Henry Ford of identity?

What if you could unlock trillions of dollars of wealth that could be associated with individuals around the globe? What sort of opportunities would be there for banks and businesses of all sorts? BanQu cofounder Ashish Gadnis saw first hand the problem facing billions of people worldwide who don’t have a bank account when he tried to help one woman farmer in the Democratic Republic of Congo. “The banker said, —We won’t bank her, but we’ll bank you, Mr. Gadnis,’” a native of India who grew up in poverty himself. “They wouldn’t recognize her identity,’’ he says, despite the fact that she owned a farm and had income every year from her harvest. Gadnis and cofounders Hamse Warfe and Jeff Keiser say this is a problem that confronts 2.7 billion people around the world who don’t have access to bank accounts or credit because they don’t have a verifiable identity. Gadnis, who wore a giant cross in lieu of a tie to a recent conference, promises to change all that by providing a way for people to create their own digital transaction-based identity through an open ledger system, or blockchain. Others in their network can verify transactions such as the buying and selling of a harvest, or the granting of a job. He estimates that approximately 5,000 people, some of them living in refugee camps in the Middle East, are using the technology to create a digital identity for themselves that could open up future opportunities to obtain credit and enter the global economy.

It’s not a nonprofit company, as you might think. BanQu is in the middle of a Series A venture capital funding round, and envisions banks and other financial institutions paying for the platform so they can access potential customers. It’s free to users. Like other tech entrepreneurs, he is optimistic about the potential of his platform, perhaps wildly so. “The key to ending poverty is now within our reach,’’ he says. But he has quite a few admirers, including Jimmy Lenz, the head of predictive analytics for wealth and investment management at Wells Fargo & Co. Gadnis has credibility, Lenz says, as he sold a successful tech company called Forward Hindsight to McGladrey in 2012. “When I think about Ashish, I think about Henry Ford. We think about Henry Ford for the cars. But really, his greatest achievement was the assembly line, the process.”

Nathan Richardson, Gaspard De Dreuzy and Serge Kreiker

These entrepreneurs provide anywhere, anytime trading for brokerage houses and wealth management firms.

All three of these individuals have well established backgrounds in technology, including Richardson, who was formerly head of Yahoo! Finance. Now, they are using application programming interfaces, or APIs, to try to make it easier to trade no matter the platform or where you are. Instead of logging into a brokerage firm’s website, Trade It sits on any website and lets you trade your brokerage account inside the website of a publisher or other company, such as Bloomberg. Although many banks have yet to sign up to use the app, the company is licensing the software to brokerage houses and Citi Ventures, the venture capital arm of Citigroup, invested $4 million into the company in 2015. “The thing that impressed us is taking financial services to our customers in the environment they are in, rather than expecting them to come to us,’’ says Ramneek Gupta, the managing director and co-head of global venture investing for Citi Ventures.

Publishers like the app because it doesn’t take the customer outside of their site. Brokerages like it because they can reach their customers anywhere. “If you think about 70 percent of consumers under the age of 40 who trust Google and Facebook more than their financial institution, why wouldn’t you want to put your product there?” says Richardson.

Gupta thinks this speaks to the future of financial services. “You have already seen it elsewhere,’’ he says. “You can order Uber from inside Google Maps.”

How Advances in Machine Intelligence Will Benefit Banks


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Over the past two years, banks have enthusiastically embraced robotic process automation (RPA), and for good reason. Offloading mundane work to bots (apps that perform automated tasks) can help banks improve efficiency and allow employees to focus on high-value work requiring human judgement and skills, all of which can help increase productivity, profitability, and customer and employee satisfaction.

At the same time, scientists have been making tremendous progress in big data analytics and artificial intelligence (AI). It’s been an era of amazing innovation and research acceleration, with many technologies maturing in parallel. The stage is now set for the third strand of automation in banking: AI (also known as cognitive computing) and machine intelligence.

AI, Machine Intelligence and Your Bank
Broadly speaking, AI and machine intelligence are computer systems that mimic human intelligence. AI is used for performing human tasks, whereas machine intelligence is an umbrella term for a broader collection of cognitive tools that have evolved significantly in recent years: machine learning, deep learning, advanced cognitive analytics, robotics process automation and bots, to name a few. They have been around (and evolving) for decades but innovations and new capabilities are enabling banks to apply them to a rapidly expanding set of business problems.

For example, banks are improving customer service by using AI to learn from customer behavior and deliver more precisely on customer preferences, tailor the customer journey and streamline product and credit acquisition. Using AI on repetitive tasks is helping banks find new ways to increase productivity and reduce costs—carefully selecting the next best action or responding efficiently to customers via cognitive call centers, for example. And AI is helping banks lower the risk of human error by reducing human involvement in cyber, credit, fraud, compliance, internal audit and employee retention.

Bots and RPA have demonstrated their value and reliability on straightforward tasks, building confidence and interest in more sophisticated uses of AI machine intelligence such as:

  • Robots that answer complex financial questions posed in plain English.
  • Cloud-based software that can potentially answer more the 65 million questions by scanning drug approvals, economic reports, monetary policy changes and political events and their impact on nearly every financial asset on the planet.
  • AI to help organize customer data and create customized packages of personalized advice, delivered to bank customers via their mobile phone.
  • Linguistic analysis and trading compliance technology to help monitor and prevent trade malpractice.

We’re far from peak adoption, but banks are quickly moving past pilot stage as they discover powerful ways AI and machine intelligence can improve multiple areas of business.

Machine Intelligence is Not Just the Ability to Do, it’s the Ability to Learn
In addition to making some things easier, faster, more accurate and more reliable, AI and machine intelligence enable you to do things that simply weren’t possible before.

For example, say your bank needs to review every contract to confirm compliance with new regulations. An AI system can search all contracts and insert specific language if it’s missing, saving considerable time and resources.

AI also enables the use of intelligent systems where, for example, employees can check in with HR on payroll, vacation time, professional development credits and so on. The system translates queries from written text to actual meaning and returns results in plain narrative. If it can’t answer a question, an HR specialist can pick up the conversation from the transcript while the AI watches, listens and learns. Gradually, the AI becomes as effective as the trained HR professional, who can now focus on other, more complicated tasks.

And computers can analyze data in an unlimited number of dimensions—a much more powerful view than the human limitation of three or four dimensions. If your bank is trying to understand which clients are more likely to engage in fraud, you can use AI software to look for suspicious behavior, a far superior alternative to reliance on people and older types of technology. This is of incredible value: Banks are obligated to pursue alerts but the vast majority are irrelevant. AI can eliminate false positives, reducing the amount of unnecessary activity significantly.

AI is Ready for Banking, but is Your Bank Ready for AI?
Right now, AI bolts onto a robotics environment, providing capability that enables robots to interact with humans or computer systems with more intelligence. Building these systems requires people with experience in the different technologies, but they have become relatively easy to implement: Nothing is out of the box—yet—but the enabling components are in the marketplace. The systems are cost effective, in large part because of the basic benefits they provide—fewer errors, faster throughput and greater productivity (24×7, no vacations).

Bottom line: If your bank is ready for AI, AI is ready for your bank.

Contributed by: Sridhar Rajan, Principal, Deloitte Consulting LLP andDave Kuder, Senior Manager, Deloitte Consulting LLP