Machine learning is already sending ripples through the financial services industry. Learn how this technology will impact the future of payments.
In the recent past, the financial services sector has witnessed a considerable uptake in the use of advanced AI technologies and machine learning. The payments industry, in particular, stands to benefit from easier navigation of complex and evolving regulations, as well as real-time payments. A report suggests that the global lending and payments market will rise from $7,833.28 billion in 2021 to $8,681.44 billion in 2022 at a CAGR of 10.8%. In the near and long term, artificial intelligence and machine learning will play critical roles in enabling faster payments, effective risk management, and managing KYC systems.
Improved security, quicker payment-to-settlement time, and reduced costs for both parties stand out as some of the primary advantages of leveraging machine learning in payments. Owing to this, payment providers are now able to standardize reconciliation and capitalize on multi-currency payment processing capabilities.
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 methods contribute to delays in reconciliation. That’s not all—they also result in the slow adoption of new business models and end up struggling with the traditional ways of making complex transactions like cross-border payments. These systems are heavily dependent on manual efforts for transaction review and reconciliation processes.
Alternatively, machine learning can assist neural networks in automating the reconciliation process. Machine learning models can learn, improve, and become more accurate thanks to reinforcement learning. This requires little supervision, reducing the amount of manual labor that is required. As a result, the system is capable of quickly adapting to and learning new payment methods, detecting anomalies, and assisting compliance and operations teams.
As per the OECD report on AI, ML, and Big Data in Finance, worldwide spending on AI is expected to double between 2020 and 2024, rising from $50 billion to more than $110 billion. In the United States, artificial intelligence has the potential to generate 58 million jobs and add $15.7 trillion in economic value by 2030. At the same time, it would eliminate mundane tasks and allow workers to be more creative, with the bulk (1.2 million) working in banking/lending. In fact, McKinsey estimated that AI could add $1 trillion to the banking industry’s value each year. According to a report, 72% of executives believe that artificial intelligence (AI) will be the most significant business advantage of the future, and it’s easy to see how machine learning and AI technologies will be a disruptive force in financial services.
The payments value chain is experiencing—and will continue to experience—disruption from machine learning. Some ways that it is already adding value to the payments value chain include:
Fraud detection and prevention: The real-time element of machine learning makes it a prime tool for processing large volumes of transaction data. Identifying patterns and anomalies makes for highly accurate decisions and the ability to precisely identify fraud and avoid false positives.
Personalization of products & services: The ability to analyze big data means machine learning can help banks and other organizations develop deeper insights about customers and more appropriately segment customers based on their needs. In turn, these organizations can offer personalized and better-suited products and services that customers want and need.
Retention: Machine learning can reduce churn by offering a much more proactive approach. Rather than addressing lost customers, financial organizations can identify customers at the risk of churning and take steps to retain them.
Customer service: Machine learning has already infiltrated customer service and support teams across industries via chat and voice bots. This will continue to bleed into the financial services sector, too. Machine learning and AI can augment customer support teams, increase capacity, improve the customer experience, and decrease wait times.
Compliance: As new technologies emerge at an accelerated pace, financial institutions and FinTechs will have to ensure compliance at every turn. Compliance requirements—and even financial reporting—currently require a significant investment of resources to process data. Machine learning and AI can aid in the restructuring of these operational procedures while simplifying regulatory and compliance requirements with increased automation and accuracy.
Undoubtedly, machine learning’s power lies in its ability to analyze and provide deep insights into 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, FinTech, and other financial organizations that adopt the technology early. It will facilitate a broad array of payment applications while reducing risk and costs significantly.
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