Not too long ago, CEOs and major banks firmly believed that physical bank branches were indispensable for serving their customers. However, in the past decade, we’ve witnessed the rise of Digital Banks. These banks operate without physical locations yet successfully expand their user base and offer additional services such as insurance, mortgages, and loans.
In the Payments industry, giants like Chase and First Data have held dominance for over four decades. Nevertheless, just as digitalization reshaped the banking landscape, the digitization of payments has allowed companies like WorldPay, Vantiv, and more recently, Stripe, PayPal/Braintree, and Adyen to capture a significant market share.
They achieved this not by concentrating on traditional businesses, but by catering to startups that have grown to overshadow or even outcompete traditional counterparts, much like Blockbuster versus Netflix, taxis versus Uber, or brick-and-mortar stores versus Amazon.
As more businesses recognize that digital has become the new norm, a recurring question arises: What lies ahead for the payments industry?
Similar to companies that embraced computers and databases in the 1970s and 1980s or understood the internet’s transformative potential in the 1990s, those that grasp the impact of algorithmization and actively invest in it are likely to thrive in the 2010s and 2020s.
Digitization of Processes
To comprehend algorithmization, we must first revisit traditional business processes. For centuries, these processes have relied on human labor to create products or deliver services, along with the accompanying business processes.
For instance, a doctor offering medical services used to have office hours where patients would visit or make appointments. Traditionally, tasks like patient registration, file updates, or prescription writing were all done manually on paper. With digitization, computers entered the doctor’s office, files became digital, appointments were managed through digital calendars, and prescriptions were sent via email.
Even today, businesses continue to enhance the digitization of processes by developing mobile apps and offering cloud-based solutions, improving data access and storage derived from core business operations.
Algorithmization
Algorithmization involves using digitized labeled data stored in a database and automated processes to generate analytics, from which users can derive valuable insights. While many companies have taken these initial steps, it’s only the beginning of the algorithmization journey. When a data set (N) can be leveraged to create analytics, the next step involves employing Machine Learning and Artificial Intelligence to develop a new process (N=N+1), thereby completing the cycle of algorithmization.
A Payment Service Provider Example
Consider a Payment Service Provider handling millions of daily transactions. Each transaction submitted by a merchant contains various transaction-related details, including PAN (Personal Account Number), CVC (Card Verification Code), Expiry Date, Customer Name, and Email Address. Through the browser, the PSP collects additional data like date and time, device fingerprint, browser type and version, IP address, and more (N).
As these transactions are processed, they are stored in a database. Most PSPs use this data to provide merchants with standard transaction reports, and some even aggregate the data to offer summaries. To go further, PSPs may research if historical data can help prevent fraud in incoming transactions. By employing Machine Learning and Artificial Intelligence, data scientists can create algorithms that assess numerous transaction variables to predict the likelihood of fraud in newly submitted transactions.
Card-based payments offer the advantage of allowing cardholders to dispute transactions within a specific time frame. When a fraudulent transaction is reported, the issuer initiates a chargeback process. The PSP can then use this information to refine the original algorithm using Artificial Intelligence, enhancing prediction accuracy by adjusting the weights assigned to various variables considered in the algorithm—effectively creating a new process (N=N+1).
How Will Algorithmization Impact Payments?
The example above highlights how Algorithmization can enhance existing processes, especially in combating fraud. However, the realm of Payments extends beyond fraud prevention. Factors like costs, conversion rates, connectivity, billing, and payouts provide opportunities for new PSPs to employ Algorithmization for differentiation.
As more companies adapt to the commoditization of PSP services, success in this space hinges on demonstrating how a PSP can generate more business. While cost reduction and fraud prevention are crucial, the key to sustainable growth lies in delivering value that drives revenue, not just improving profits.
Intelligent Acquiring Routing
Transactional data can also be leveraged to optimize acquiring routes based on performance, functionality, or pricing. Implementing algorithms like the multi-armed bandit algorithm, a more sophisticated version of A/B testing using machine learning, enables PSPs to dynamically allocate traffic to variations that perform well while reducing traffic to underperforming variations. By connecting multiple acquirers, PSPs can enhance merchant results by routing transactions to the acquirer that best suits the merchant’s needs in terms of performance, pricing, or functionality.
Dynamic 3D Secure
Despite additional two-step verification measures like Verified by Visa or Mastercard’s 3DSecure, PSPs often face high decline rates due to issuer-mandated two-step verifications. By employing Decision Tree Learning, PSPs can predict whether a transaction requires routing to a 3DSecure page for additional verification or if proceeding with the traditional flow would lead to a successful transaction.
Unleash Creativity Beyond Boundaries
Numerous possibilities exist for using transactional data to enhance merchant performance in payments. PSPs that can harness decades’ worth of data and employ modern tools to revamp processes will emerge as providers better suited to serve merchants and business models that haven’t even materialized yet.
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