The Top Fintech Trends Driving the Next Decade

By Rob Morgan

Ten years ago, when Steve Jobs unveiled the first iPhone, few predicted that it could become the primary platform by which customers interact with their banks. New technologies are rapidly changing the way customers consume all of their products and services, and banking is no different. Customers’ expectations are being set by the experiences offered to them by tech companies. In a world where you can call a cab from your phone, customers expect their banking experience to be as intuitive.

America’s banks are innovating and partnering with startups to deliver the services that customers want. They are also looking forward to the next wave of technologies that could shape the future of the industry. Here is a glimpse at the technologies driving bank innovation today—as well as a look ahead to the technologies coming down the pipeline that will change the way banking is done over the next 10 years.

1. Digital Lending (here and now)

Digital lending is the technology that kickstarted the fintech movement and is still the most prominent example in the media. As consumers and businesses have moved more activities online, they have created an unprecedented amount of data. Online lenders have leveraged this data to make underwriting decisions, creating computer programs that can automate loan originations without the need for a customer to ever set foot in branch.

These programs can use traditional underwriting criteria—such as debt to income or cash flow analysis—or less traditional metrics such as the number of visitors to a company’s website. In practice, most platforms today use traditional underwriting criteria that would be familiar to banks across the country. It is the digitization of the process where these platforms add the most value—getting quicker and more consistent decisions for customers while taking less bank employee time to process. It also expands the ability of banks to lend outside of their physical footprints, allowing smaller banks to compete.

Unsecured consumer lending is the first market where digital lending has made an impact and is by far the most mature. Today, the two leading consumer lending platforms (Lending Club and Prosper) originate roughly $2.5 billion in loans quarterly. Small business lending has quickly followed and is rapidly digitizing. ABA has endorsed the digital commercial lending solutions offered by Akouba, providing banks of all sizes the use of these platforms to enhance customer service and significantly reduce underwriting costs.

Despite the difficulties of collateralized lending, digitization has a role to playhere as well. One area experiencing significant innovation is mortgage lending. While the mortgage process still has a long way to go before digital end-to-end origination is possible, technology can significantly simplify the process by digitizing forms, prepopulating known information and ensuring that all of the documents are in order before a customer sits down with a mortgage officer. ABA endorses MortgageBot from Finastra (formerly D+H) to provide many of these services.

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2. Biometrics (1-2 years)

As trusted custodians of their customers’ most sensitive data, banks are the gold standard in data security. A key component of this security is ensuring that someone logging in is actually the bank’s customer. Today, bank authentication is typically dual-factor based on “something you know,” such as a password and your mother’s maiden name. Biometrics present the opportunity to increase this level of security by authenticating based on “something you are,” such as a thumbprint, selfie or iris scan.

Passwords are only secure to the extent that they are kept private and cannot be guessed by a keen observer. The problem is that these passwords often rely on observable pieces of our life like our birth date, our children’s names, or our pets (my personal favorite), which are all readily available to criminals using social media and public databases. A 2015 study by TeleSign indicated that one in five people use passwords that are over 10 years old, with 73 percent of accounts being secured by the same password. Compounding this problem is the fact that we now have so many accounts that require a password, that it is impossible to keep them straight.

Biometric technology can help address all of these concerns. While thumbprint authentication was once strictly the territory of secure government facilities and James Bond villains, smartphone thumbprint scanners have made this technology accessible for daily consumer use.

The challenge this presents is that biometric authentication is probabilistic. Rather than determining a right or wrong input, a biometric system calculates the probability that a thumbprint or iris scan match the original. The output from smartphone hardware is typically a yes/no answer that gives little information about the level of confidence. A bank may be willing to let a customer view their balances with a 95 percent level of confidence, but might require a stricter test to move money out of the account.

As a result, banks are building new technologies that provide stronger levels of authentication and give them more control. One example of this is in call centers, which use vocal patterns for identification. A number of firms have developed technology that uses the forward-facing camera on a phone to authenticate with a selfie photo or an iris scan. A British bank has even rolled out a system that scans the veins in your fingers to authenticate users on large corporate accounts.

As these technologies develop, they will become even more seamless. There are companies working on “behavioral biometric” technologies that can authenticate users based on how they interact with bank websites or apps. It turns out that the way you hold your smartphone and how you swipe up or down may be as unique as your fingerprint.

Traditionally, a tradeoff existed between security and usability. In order to better secure a customer’s account, additional roadblocks and secondary levels of security were needed. Biometric technology gives banks the ability to put better security measures in place that can also make customers’ lives easier.

3. Customer Data (1-3 years)

In a digital era, data is one of the most valuable resources for a company. Companies like Google and Amazon are built on the customer insights gained through use of the platform. Banks house a tremendous amount of customer data that has the potential to drive real value for customers, by allowing banks to better understand their needs.

Today, customer data at banks is often unstructured—housed in systems that are inconsistent and may not talk to each other. A single customer may have multiple accounts with a bank that are all housed in different systems, with inconsistent identifiers. A number of banks, as well as core processors, are working to reconcile these systems. Some are working to build additional data warehouses that aggregate disparate customer data to create a unified view of customers.

Community banking is a relationship business and community banks are able to serve their customers so well because they know those customers. As these customers continue to interact with their banks digitally, a complete digital view of a customer can help a bank better understand and serve that customer.

Banks are also beginning to offer value-added services to customers that give them more information about those users. For example, personal financial management tools—such as those offered by Geezeo and endorsed by ABA—allow a bank to help users manage their money by categorizing their transactions and visualizing spending trends. These tools also allow customers to aggregate account data held at a bank as well as other financial data (such as investments, retirement funds or auto loans), giving them a comprehensive view of their finances. Geezeo, for example, aggregates data from an estimated 17,000 financial institutions.

PFM tools are a classic win-win for customers and banks alike. Customers gain insights that help them better manage their budgets and set and achieve financial goals. In turn banks get information that can better help them serve that customer. For example, a customer may be saving for a down payment on a house. The bank is able to see this and make a mortgage offer tailored to that customer’s needs.

4. Regtech (3-5 years)

Regulatory technology, or regtech, refers to the application of technology to help ease banks’ regulatory compliance burden. Just as fintech is being used to digitize customer-facing financial services, regtech promises to digitize back-office regulatory compliance, simplify regulatory reporting and empower staff to better assess risk and monitor regulatory compliance.

The application of regtech is still in its infancy. Some ideas require the buy-in of regulators and the maturation of technology, while others are as simple as providing better information to compliance officers—and are available today. For instance, IBM is training its artificial intelligence system, Watson, to help banking professionals manage their regulatory and fiduciary responsibilities.

Regulatory reporting is one area that seems ripe for digital disruption. Today, filing call reports is a quarterly activity that requires significant time. It would not be hard to imagine a software solution that was tied into a bank’s back-end systems and prepopulated all of the key reporting fields. Moreover, it would be possible for regulators to receive a steady feed of data from a bank that would give them an ongoing view into the bank and may reduce the frequency with which exams are necessary.

Another area that holds great potential is in know-your-customer procedures. Today, onboarding and verifying a customer’s identity is a manual, time-consuming task that relies on physical identification documents such as a driver’s license. In some cases it is even illegal to digitize these documents. A digital identity could help banks quickly and accurately get to know new customers and manage risks.

5. Artificial Intelligence (5-10 years)

Artificial intelligence has long been in the realm of science fiction movies, but we are seeing real-world examples of software that learns and adapts. AI as discussed today describes a process known as machine learning. Machine learning allows computers to learn, without specific programming instructing them how to operate. Unlike traditional computing—in which devices are given a specific input and in response take a specific pre-programmed action—machine learning allows computers to change their programming and respond differently as new information is made available. While the underlying technology is complex, it powers two key use cases that make technology more accessible:

Natural language processing. Tech companies have developed virtual assistants (like Apple’s Siri or Amazon’s Alexa) that allow users to interact conversationally with a computer program by asking questions or giving commands. Spoken language is not as straightforward as coding language, as there are often many ways to say something. AI allows these programs to understand complex voice commands and translate them into computer code.

Banks have begun building interfaces that work with these virtual assistants, giving customers access to bank account information and performing basic tasks such as checking a balance or paying a bill. While these applications may seem like a gimmick today, features on the first generation of smartphones were similarly limited. As voice recognition technology improves, virtual assistants will become increasingly useful.

Big data. Machine learning allows software programs to analyze large sets of unstructured data. Traditionally, data analysis required a well-organized and structured set of data with which a researcher could test specific hypotheses. Machine learning allows users to draw valuable insights from much larger sets of data than were previously accessible.

One way this could help bankers is by improving fraud detection. Traditional fraud monitoring systems rely on specific non-personal rules (like geography) to detect fraudulent transactions. Machine learning could be applied to analyze the transactions of each customer, flagging transactions that are out of their normal habits.

This improved analytical capability has the potential to give banks insights that could allow them to develop better credit models and more accurately identify risks. The power of big data is, however, highly dependent on the quality of the data, which is not always easily accessible. It is still a long time before banks will easily be able to get big-data insights from their existing data.

6. Internet of Things (8-10 years)

The internet of things, or IoT, is the digital networking of physical devices also referred to as “connected devices.” Almost anything with an on/off switch can now be connected to the internet. Today, the average home in the U.S. has seven devices connected to the internet. By 2020, Gartner estimates that there will be 25 billion internet-connected devices globally. These “smart devices” range from refrigerators that remind you when the milk has gone bad to industrial machinery that can signal when a part is worn out. This technology promises to have a huge impact on nearly every industry, and banking is no different.

For example, banks may be able to use internet-connected devices to make better loans and monitor collateral. Inventory or livestock for a small business can be monitored in real time. This would allow a bank to monitor a customer’s balance sheet on an ongoing basis, giving it the tools to make better decisions about lending or adjusting credit lines in real time.

The biggest long-term impact that IoT is likely to have is in payments. Connected devices are already able to talk to each other, but will also require the ability to make payments back and forth. Today, this may be as simple as using your smart watch to settle a bill, but could evolve to the point at which your refrigerator pays for groceries that are running low. A number of auto makers are experimenting with enabling cars to make payments.

There are still concerns—especially in cybersecurity—that may limit IoT’s usefulness in banking. Every additional internet-connected device is a new attack vector for hackers, and it can be hard to update IoT devices to the latest security specs. Despite these challenges, more of our everyday devices are being connected to the internet, and banks should consider the implications for their business.


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