As enterprise technologies continue to rapidly evolve, so do the challenges facing financial institutions on their modernization journeys. Firms responsible for payment processing must adapt to the constantly shifting security and threat landscape of their software while ensuring swift execution times. Leveraging artificial intelligence (AI) and data presents numerous opportunities for payment services providers to address these challenges and enhance protection for the enterprise and its customers.
The Need for Predictive Analytics
With the arrival of immediate payments and real-time settlements comes an increase in financial crime, as organized criminal groups and tech-savvy individuals become more adept at concealing their identities and evading detection. This fueled a record number of attacks targeting the financial sector last year.
Fraud and anti-money laundering (AML) teams must update their rules-based detection systems to ensure they identify questionable parties, suspicious networks, and anomalous activity faster and more accurately. By using predictive analytics and the vast amounts of existing data, they can reduce false positives and increase detection rates. Real-time payments not only require multiple transactions behind the scenes between merchants and sellers, but for a payment processor to execute a near instant tap of the card or Zelle transaction, they need predictive analytics.
AI/ML Transforms Payment Security and Efficiency
AI and machine learning (ML) continue to be useful tools in combating fraud and cybercrime. These intelligent systems can ingest vast amounts of data, build holistic profiles, and assist payment service providers with executing their AML and Know Your Customer (KYC) obligations at pace.
AI/ML based models can identify trends and patterns in fraud more effectively. By capitalizing on generative AI, for example, payment service providers can analyze their ledgers, look at the purchase, its purpose and the amount and make an association in near real-time to ascertain if it is a valid transaction. This helps bring efficiency into the payment lifecycle and reduce the overall risk of false positives and fraud. Additionally, machine learning can be used in conjunction with two-factor authentication (2FA) to assign a risk score to each transaction, learn a user’s patterns and run thousands of checks in milliseconds to uncover correlations to uncover fraud. This is moving beyond just the normal filtering that happens today towards giving payment service providers and firms richer information and details much quicker.
In order for firms and payment services providers to utilize these more advanced AI and ML technologies, a single, standardized platform that can run these tools anywhere is required—as is a secure environment that allows data encryption.
An open hybrid platform allows firms to build, train and run the algorithms that can detect linkages among different parties, accounts, events, and transactions that can burst into the public cloud is critical in getting agility. Just as important as functionality is, so too is knowing what the “black box is doing,” using tools such as MLOps and model monitoring, making sure the models are behaving as expected and giving full traceability to auditors and regulators.
When properly designed and implemented, AI/ML applications can dramatically improve an organization’s ability to safeguard and streamline every step in the entire payments lifecycle. A simple example is addressing verification: AI can do the research that would otherwise need to be done manually, including scouring geospatial data, Google maps, electronic phone records, utility bills and any other publicly available information. Generative AI can do this more quickly, at scale and with greater decision consistency than a human.
In addition, a well-designed, well-Implemented AI/ML application can also bring fairness to the entire payments lifecycle as clear and repeatable processes bring accountability and transparency.
Protect to Innovate
Payment service providers and firms alike are keen to protect their customers’ data. They know that a single breach could ruin their reputation, costing them money and, likely, a large portion of their customers.
At the same time, the market is demanding more speed, more transparency and lower costs in an operating environment with more new and different risks than before. This means firms need a platform with security designed in and must build resilience into the entire payment lifecycle—including within their organizations from a people and process point of view. To achieve this, payment services providers will continue to capitalize on AI/ML tools to harness larger, richer data sets. The opportunity of generative AI combined with automation and modern platforms, better intelligence and business insights are within their grasp.
