Machine learning is already sending ripples through the financial services industry. Learn how this technology will impact the future of payments.
Some fintechs are already making strides using machine learning. Flywire, a payments company, recently announced its use of machine learning within its cross-border payment and receivables platform. The benefits include better security, faster payment-to-settlement time, and lower costs for both payers and receivers. Through automation, the company is able to streamline reconciliation across a wide range of payments in a variety of currencies originating from all over the world.
The biggest obstacle to the machine learning revolution in payments is the inertia of legacy payment systems, which rely on inflexible rules-based systems. These rigid systems slow reconciliation and are slow to adapt to new business models, different currencies, and more complex transactions like cross-border payments. These systems rely on manual efforts for transaction review and reconciliation.
Alternatively, machine learning can support neural networks to streamline the reconciliation process via automation. Reinforcement learning enables machine learning models to learn, improve, and become more accuracy. This requires minimal supervision, reducing the manual effort needed. The result is a system that can easily adapt to and learn new payment methods, detect anomalies, and support compliance and operations teams.
Undoubtedly, machine learning’s power lies in its ability to analyze and provide deep insights on large volumes of dynamic data. From transaction data to consumer behavior, the ability to quickly identify patterns and evolve business models accordingly will cause ripples throughout financial services. Machine learning’s ability to learn, improve, and detect subtleties in data will open up a world of possibilities for banks, fintechs, and other financial organizations that adopt the technology early. It will facilitate a broad array of payments applications while reducing risk and cost significantly.