As enterprise technology continues to rapidly evolve, so too do the challenges that financial institutions face in their modernization journey. Businesses responsible for payment processing must adapt to the ever-changing software security and threat landscape while ensuring fast execution times. Leveraging artificial intelligence (AI) and data offers many opportunities for payment service providers to address these challenges and better protect businesses and their customers.
The need for predictive analytics
The advent of instant payments and real-time payments has increased financial crime as organized crime groups and tech-savvy individuals become more sophisticated at hiding their identities and evading detection. This led to a record number of attacks targeting the financial sector last year.
Anti-fraud and money laundering (AML) teams need to update their rules-based detection systems to more quickly and accurately identify suspicious parties, suspicious networks, and anomalous activity. By using predictive analytics and vast amounts of existing data, you can reduce false positives and increase detection rates. Real-time payments not only require multiple behind-the-scenes transactions between merchants and sellers, but also require predictive analytics for payment processors to execute card taps and Zelle transactions almost instantly.
AI/ML transforms payment security and efficiency
AI and machine learning (ML) continue to be useful tools to fight fraud and cybercrime. These intelligent systems ingest vast amounts of data, build comprehensive profiles, and help payment service providers quickly fulfill their AML and Know Your Customer (KYC) obligations.
AI/ML-based models can more effectively identify trends and patterns in fraud. For example, by leveraging generative AI, payment service providers can analyze the ledger, confirm the purchase, its purpose, amount, and make correlations in near real-time to confirm whether it is a valid transaction. . This increases efficiency in the payment lifecycle and reduces the overall risk of false positives and fraud. Additionally, using machine learning in conjunction with his two-factor authentication (2FA), it assigns a risk score to each transaction, learns user patterns, and performs thousands of checks in milliseconds to create correlations. Reveal and discover fraud. This goes beyond just the usual filtering that is currently taking place, towards providing payment service providers and businesses with richer information and details more quickly.
For enterprises and payment service providers to take advantage of these more advanced AI and ML technologies, they need a single, standardized platform to run these tools anywhere, and a secure environment that enables data encryption. is also required.
An open hybrid platform allows businesses to build, train, and run algorithms that can discover connections between different parties, accounts, events, and transactions that can burst into the public cloud. This is essential to achieving agility. Just as important as functionality is the ability to use tools like MLOps and model monitoring to know “what the black box is doing”, to ensure that the model is working as expected, and to help auditors and provide full traceability to regulators.
When properly designed and implemented, AI/ML applications can significantly improve an organization's ability to secure and streamline every step of the entire payments lifecycle. A simple example is responding to validation. AI can perform investigations that would otherwise be done manually, such as scrutinizing geospatial data, Google Maps, electronic phone records, utility bills, and other public information. Generative AI can make more consistent decisions faster and at scale than humans.
Additionally, well-designed and well-implemented AI/ML applications can also bring equity to the entire payments lifecycle, as clear and repeatable processes bring accountability and transparency.
protect and innovate
Payment service providers and businesses alike are keen to protect their customers' data. They know that a single breach can damage their reputation, cost them money, and perhaps cost them a large portion of their customers.
At the same time, the market demands greater speed, transparency and lower costs in a new and different business environment with new risks. This means businesses need platforms with built-in security, and they need to build resilience across the payments lifecycle, including within their organizations from a people and process perspective. To achieve this, payment service providers will continue to leverage AI/ML tools to leverage larger and richer datasets. Automation and generative AI opportunities combined with modern platforms, better intelligence and business insights are within their reach.
