Digital transformation has been the top buzzword for the past several years, though the digitization of processes has been happening for the past several decades. That said, digital transformation has swept through the fintech and payments sector like wildfire in recent history and continues into 2019. According to Russell Reynolds Associates, media, telecom, and consumer financial services experienced the most disruption in 2015. APM Digest suggested that banking was most likely to be negatively impacted by disruption in 2018.
Almost every party in the payments ecosystem has been touched by these waves of digital transformation—from card issuers and acquirers to processors and gateway providers—and the relentless emergence of new technologies has only sparked additional disruption. In some cases the entire payments value chain has transformed as payments move from a behind-the-scenes mechanism to a perfectly engineered digital experience.
As we look at what the future holds for digital transformation in the payments space, it can be helpful to look at the impetus and conditions behind this push toward digitalization. From there, we can explore what the future may hold for this constantly evolving space. The
The PSD2 mandates that banks provide third-party providers (TPPs) access to their customers’ accounts through open APIs. In turn, TPPs can build and sell financial services on top of the data and infrastructure provided by banks. It presents a real challenge and several threats to banks as they exist today.
First, it makes the financial services space highly competitive. Banks will now have to compete against other banks as well as fintechs, TPPs, and merchants. It disrupts the payments value chain, which makes sense when you consider that the ultimate goal is to encourage innovation, improve customer experience and security, and to increase the security of online payments and account data.
The directive introduces two new entities to the financial service ecosystem in the form of Payment Initiation Service Providers (PISPs) and Account Information Service Providers (AISPs). The former (PISPs) are entities that initiate a payment on behalf of the user, which may include P2P transfers and bill payments. The latter (AISPs) are able to access the account information of banks’ customers, which can then be used to analyze transaction history and spending behavior or to aggregate user account information across several banks and channels into one dashboard view.
These new players paired with a more level playing field means that banks must think strategically to withstand and effectively grow through this disruption. While threats to bank business are very real, there are also significant opportunities for banks to monetize this open API movement and to create new revenue streams. The first step is to become compliant. From there, banks can strategically position themselves (and the wealth of information to which they have access) to avoid disintermediation.
The increased level of connectivity – or the growth of the Internet of Things (IoT) – has led to the need for innovation in payments as well. The IoT has opened up the possibility of connecting just about any physical device to the internet and the global IoT market is set to grow at a compound annual growth rate of 28.5% to reach $457 billion next year. Consumers have become accustomed to extreme connectivity and convenience and payments is no exception.
Payment service providers have been leveraging IoT already, enabling payments through connected voice assistants, smart TVs, and even cars. As connectivity multiplies, the matching payment options to enable consumers to pay when, where, and how they want to will multiply, too.
Technology like artificial intelligence (AI), IoT, and machine learning are all finding their way into various industries in order to simplify and streamline what once were manual processes. In the payments space, where the ecosystem has relied heavily on manual or legacy systems to keep the machine running, these technologies have catapulted payments innovation into the next generation of solutions.
The two biggest areas where we stand to see major improvements due to enhanced technology is in customer service and fraud prevention across all channels and platforms. Machine learning can quickly and automatically identify patterns across large volumes of transactions to quickly decide which may be fraudulent—while also reducing false positives. Its ability to adapt over time and efficiently learn new fraud tactics makes it invaluable in the fight against fraud.
Artificial intelligence provides the ability to personalize the customer experience and improve customer service. Many organizations are already tapping this technology—along with natural language processing (NLP)—to enable customer service and payments via chatbots and voicebots.
In 2019, we have seen drastic changes to the payments space over the past few years. Yet, many financial service organizations—whether fintechs, payments companies, or banks—are still bogged down by legacy systems and a brick-and-mortar mindset. Consumers, on the other hand, have evolved as quickly as the technology and have come to demand a certain level of security, convenience, and speed.
There were roughly 327.16 million people making up the U.S. population in 2018, according to the US Census Bureau's population clock. About 77% of those people own a smartphone, according to Pew Research Center. Of these smartphone owners, the following percentages had used a mobile wallet to pay at some point in time before or during 2018:
While these percentages seem tame, this year may be a breakthrough year for mobile wallet adoption. Many merchants have not yet enabled the in-store technology to accept mobile payments but others are understanding that a little creativity and innovation are exactly what consumers are craving. Honda, for example, has released an in-dash entertainment system that enables users to access the mobile app to both find gas stations that accept mobile payments and to actually pay once the tank is full.
Both banks and payment service providers are looking to expand in the mobile app realm and smartphones are now ubiquitous and a highly preferred channel for consumers. With the PSD2 (Revised Payment Service Directive) being implemented in 2018 in the EU, and many companies in the U.S. and other countries following suite, there are more opportunities than ever to reach customers via third-party apps. As banks increasingly open up their customers’ account information via open APIs, we will see more third-party financial service and payment apps flood the market—all built on top of banks’ existing data and infrastructure.
The foray of payments into the mobile space spans just about every niche—from lending, investing, budgeting, financial management, P2P payments, and gift cards. The idea remains simple: meet consumers where they are and offer seamless ways to pay (and manage, invest, budget, etc.). The buzz around mobile apps will only increase as we move through this year; contactless payments are estimated to hit $90 million by 2020 while payments via apps will reach $318.8 billion next year as well.
AI leverages big data, machine learning, and neural nets modeled after a human neural network to analyze incoming data and make decisions. This ability to analyze data and assist in the decision-making process expands payments capabilities on a number of levels. While a lot has been written and said about the possibilities for AI in payments, research shows that its prevalence may be overstated. PYMNTS conducted a survey and found that only 5.5% of banks are using “true AI” for things like fighting money laundering or reducing false-positives. That said, the planned investments in these areas illustrate their perceived potency in the financial space. Almost three-quarters (73%) of banks with more than $100 billion currently budget at least $50 million dedicated to the maintenance of existing machine learning and AI systems. Additionally, eight out of 10 (82%) will bump up their investments in these types of technologies in the future.
AI’s ability to streamline operations, reduce manual processes and make payments more efficient become even more important as digital payments see a steep uptick. A recent report by Capgemini and BNP Paribas notes that online payments are expected to rise by 11% annually through 2020. The need for fraud prevention has become paramount as an increasing number of bad actors look to online transactions as a new revenue stream.
AI and machine learning are able to quickly identify patterns and detect potentially fraudulent transactions. With supervised learning, AI models are trained on large sets of transaction data tagged as either “fraudulent” or “not fraudulent”, through which the model can learn patterns that reflect each type of behavior. This is a more static application of machine learning, though unsupervised learning has opened the door to more robust fraud prevention.
With unsupervised learning, AI models can be developed through self-learning, where the transaction data may be thin. Rather than relying on a labeled training set, the model is updated as new patterns emerge—patterns that are largely invisible to other forms of analytics. This continuous retraining enables the AI to detect outliers and anomalies.
Fraud prevention is not the only application that can benefit payments. Advancements have enabled a broader application of this technology, automating the workforce at incredible levels. McKinsey Global Institute projects that 47% of the US workforce will be automated by 2030. The same momentum can be see for machine learning applications in the payments space. Machine learning has been used for transaction monitoring and authorization of payments, both of which utilize learning algorithms. The possible applications for machine learning in payments are nearly endless.
With the growth of big data, machine learning has become increasingly necessary to sift through large volumes of data and garner deeper insights from it all. Access to big data alone is not enough. Machine learning tools enable fintechs to quickly and accurately process this data to better meet customer need, decrease costs, and better allocate resources. These tools are especially applicable to large and changing data sets not unlike those tracking consumer behavior. Slight shifts in behavior and subsequently, in the data, can be hard to detect. Machine learning is quick and accurate at identifying such minute anomalies, making the data itself more powerful. This ability to detect subtle shifts can improve predictability.
There are several other ways in which machine learning stands to invoke increased digitization in payments and throughout related operations in organization: AI’s
Banks have long had a stronghold on the payments product and services market, but digital is disrupting this hierarchy. The proliferation of mobile apps, digital wallets, and other connected channels have upended the distribution model, opening up the payments ecosystem to application developers and technology companies, in addition to the traditional players. Banks are increasingly partnering with these third parties to create a more personalized, relevant, and digital customer experience through open APIs.
In Europe, the PSD2 has helped accelerate some of these changes by essentially ending the banks’ monopoly on customer data. As an expanded set of laws and regulations from the original Payments Services Directive, enacted in 2007, the PSD2 is geared toward securing digital payments and expanding the financial services ecosystem. It is applicable to almost all payment services providers and paves the way for increased transparency and fair competition.
As banks must compete with third party providers who enable direct payments from accounts via apps or other digital platforms, innovation will take a front seat and new services will emerge. Though the push for innovation is a key driver here, so is the imperative for better security within digital payments.
The PSD2 also calls for strong authentication measures that utilize at least two of the following three elements: something a user knows, something a user has, something a user is. In other words, the days of the static password are over. In
Cloud computing has been a popular tool within the grander scheme of digital transformation—a trend that shows no signs of slowing. It’s predicted that over half of all IT spend will be cloud-based by 2021. As more organizations embrace the adoption of cloud infrastructure, we see this should be no different within the fintech and payments spaces. The delivery of on-demand, pay-as-you-go computing resources enables other service providers to streamline their own offerings and increase efficiency across an entire organization.
This is particularly true for banks, who face significant challenges in adapting to innovations within payments. As regulations evolve and data privacy and security becomes more complex, cloud-based solutions show real promise for financial institutions (FIs). There are several benefits to FIs, who have long been bound by costly-to-replace legacy systems:
These changes in the payments landscape will only accelerate in the near- and long-term. Electronic payments will continue to evolve and the ecosystem will continue to be disrupted by new market players. To remain competitive in the space, traditional players and incumbent banks will need to embrace innovation and leverage many of the newer tools, channels, and technologies mentioned in this paper.
Shrewd strategies that embrace the disruption—and adopt the right mix of emerging technologies—will position incumbents and new entrants alike to survive and thrive in this era of digital transformation.