The Strategic Shift Toward Smarter Payment Infrastructure
The banking industry is going through a major change, driven by fast technological progress and shifting customer needs. Banks face pressure to upgrade old systems, cut costs, and offer highly personalized services. This is where Artificial Intelligence (AI) becomes important. AI is central to this change, providing tools to automate tasks, improve decision-making, and boost customer interaction. For example, Bank of America’s AI-based assistant, Erica, helps customers with tasks like checking balances and making payments, while HSBC uses AI to improve fraud detection by analyzing transaction data in real time.
However, within banking, the payments sector faces its own set of serious challenges. As digital commerce speeds up and real-time transactions become standard, traditional payment systems—often built on decades-old infrastructure—struggle to adapt. These systems are hindered by inefficiencies, high processing costs, limited scalability, and vulnerability to fraud.
According to McKinsey, the global payments industry is projected to generate $3.1 trillion in revenue by 2028. AI is proving especially transformative in the payments sector by eliminating long-standing inefficiencies and boosting operational agility. Real-world examples illustrate how AI is changing payment flows in banking by increasing efficiency and fostering innovation. For example, PayPal uses AI to optimize payment routing by analyzing multiple factors to lower transaction costs and improve speed and reliability. Similarly, Visa employs AI to verify payment details and identify duplicate transactions, ensuring accuracy, regulatory compliance, and fraud prevention.
How Banks Are Putting AI to Work
In the banking sector, AI offers numerous benefits, including enhanced operational efficiency, cost reduction, and personalized customer experiences. Key trends include:
- Chatbots & Virtual Assistants: Many banks use AI-powered chatbots and virtual assistants to provide 24/7 customer support, reduce human workload and improve response times.
- Fraud Detection: AI analyzes transaction patterns to detect and prevent fraud, helping safeguard customer accounts.
- Personalized Services: Banks leverage AI to tailor financial advice, product recommendations, and marketing based on user behavior.
- Credit Scoring & Risk Management: AI evaluates creditworthiness using diverse data sources, improving lending accuracy and reducing defaults.
- Process Automation: Tasks such as loan approvals and compliance checks are streamlined through AI, minimizing manual effort.
These innovations showcase how AI helps banks modernize operations and create new value for customers. For example, JPMorgan Chase’s COiN uses AI to extract data from legal documents, significantly cutting review time and errors. Likewise, Bank of America's AI assistant, Erica, assists customers with tasks like checking balances, making payments, and offering financial advice, all while maintaining account security.
Optimizing Payments with Intelligence
Like the banking industry, AI can also improve payment processes—reducing errors and processing times while enabling faster, more accurate transactions. For example, machine learning algorithms can analyze transaction data to identify patterns and predict potential issues before they happen. This proactive approach helps banks avoid delays and ensures smooth payment processes. Additionally, AI-powered systems can automate routine tasks, freeing up human resources for more strategic activities. Real-world examples of banks using AI to improve their payment systems include automated fraud detection, real-time transaction monitoring, and intelligent payment routing.
AI plays a vital role in optimizing payment flows by improving efficiency, accuracy, and security. Banks and payment service providers are using AI to streamline various aspects of payment processing, including validation, duplicate detection, dynamic routing, and more, where old, complex, and rigid legacy systems can be effectively replaced with intelligent solutions.
Here are some implementable AI use cases that can help optimize payment flows:
- Payment Processing: AI algorithms are used to automate and speed up payment processing, reducing the time required for transactions and minimizing errors. For example, AI algorithms can prioritize transactions based on various factors, such as category purpose (ISO 20022), urgency, and risk level. This includes real-time processing and settlement of payments.
- Validation: AI is employed to validate payment details, ensuring that transactions are accurate and comply with regulatory requirements, for example structured address details from unstructured sources etc. This helps in reducing the risk of errors and fraud.
- Duplicate Transaction Detection: AI systems can detect duplicate transactions, whether we are talking about incoming payments or outgoing payments triggered by diverse internal presenters, by analyzing patterns and identifying anomalies. This reduces manual intervention and resource lock-in, prevents duplicate payments, and ensures that transactions are processed accurately.
- Dynamic Routing: AI can optimize the routing of payments by selecting the most efficient path based on various factors and SLAs from correspondent banking agreements such as cost, volume, value, amount threshold, speed, and reliability. This helps in reducing transaction costs and improving the overall efficiency of payment networks.
- Reconciliation: AI can automate reconciliation processes, matching transactions with corresponding records to ensure accuracy. These improvements lead to a reduction in impairment of accounts and thus to a more efficient and reliable payment system.
- End of day (EoD) processing: When it comes to closing the business day, AI can be used to manage and coordinate dependencies among various sequential jobs, ensuring there are no delays, errors, or the need for human intervention.
- Payment postings: Bookings are some of the most complicated steps in the payments value chain. They involve multiple variables—such as category purpose, product code, customer code, and channel—and their combinations determine how and how much transactions should be booked. Because of the many possible combinations, many are not set up in the system, leading to frequent manual work. An AI solution could smartly identify and map booking scenarios, automate setups, and ensure accurate processing—allowing teams to focus on higher-value tasks.
- Monitoring payment jobs: Because of its complexity, payment processing involves many program jobs that run either independently or with dependencies at specific times—whether in real-time or as batch-scheduled tasks. Therefore, it is essential to monitor all jobs. However, in practice, not all jobs are tracked, as they need to be manually configured in monitoring tools, which can be time-consuming. An AI solution can solve this by intelligently scanning all processes and mapping them into a monitoring tool, ensuring full coverage and timely alerts.
- Payments’ exception: AI can pre-emptively reduce transactions sent to exception queues in payment processing by addressing incomplete data, such as missing IBANs in cross-border payments to the SEPA region. Wipro’s AI Genie uses entity resolution and predictive techniques to analyze account metadata, recognize patterns, and synthesize compliant IBANs. This enriched data is injected into payment messages, maintaining schema integrity and avoiding exception queues.
- Monetizing investments: AI helps banks unlock new revenue by converting payment data into credit insights. Analyzing AP and AR flows, AI assesses cash flow and identifies financing needs. This enables real-time, personalized working capital loans, lowers underwriting risk, and integrates lending into digital channels, transforming transactional data into a monetizable asset and enhancing client engagement.
Many of these AI applications—such as duplicate detection, validation, and payment routing—are already widely adopted across the industry. Meanwhile, others, like payment processing and monetizing investments, are still emerging but demonstrate promising growth potential. These real-world examples show how banks can leverage AI to transform payment processes, fostering innovation and efficiency. For example, PayPal uses AI to optimize payment routing and reduce transaction costs. Their algorithms analyze diverse factors to identify the best route for payments, making transactions faster and more dependable. Similarly, Visa relies on AI to verify payment details and detect duplicate transactions, helping to ensure accuracy and compliance with regulations, which in turn decreases errors and fraud risks.
Unlocking New Value in Payments Through AI
AI can help drive innovation in payment solutions, such as mobile payments, digital wallets, and blockchain technology. For instance, it can help banks monetize their payment services by identifying new revenue streams. By analyzing transaction data, AI can uncover opportunities for cross-selling and upselling, enabling banks to offer targeted promotions and personalized offers.
From a retail customer’s perspective, a personalized payment experience would be a game-changer. AI could analyze spending habits to recommend unnecessary subscriptions (or better ones), payment schedules, and customer-tailored discounts paired with loyalty programs. Additionally, for those who frequently travel, advanced algorithms could improve currency exchanges, lower transaction fees by enabling A2A payments (wherever possible), and provide add-on services such as specialized travel insurances, car rentals, or expense management.
When it comes to services, customers are a key area where AI can have a major impact. AI-powered chatbots and virtual assistants can handle routine questions, give personalized recommendations, and resolve issues quickly and efficiently, especially when traveling abroad. This not only boosts customer satisfaction but also eases the workload of human agents. For example, ING created a Gen AI chatbot that provides customers with real-time, personalized responses in a secure way. This chatbot improves customer interactions by offering instant support and customized recommendations based on individual needs.
Real-World Wins with AI in Payments
Wipro assisted a major U.S. bank in deploying machine learning-based anomaly detection for transaction data. The solution helped identify new transactional patterns, data anomalies, and potentially suspicious connections, leading to improved financial data processing and operational efficiency.
Another use case involves the digitization and automation of unstructured cash disbursement and wire transfer payment instructions. Wipro’s machine learning solution used optical character recognition (OCR) to digitize payment instruction documents and deployed multiple supervised ML models to extract and classify key data elements. The team also examined alternative model architectures, including Bidirectional Encoder Representations from Transformers (BERT), to improve performance.
Embracing the Future of Payments with AI
The move toward more intelligent payment infrastructure represents a significant milestone in banking's evolution. As AI advances, reshaping efficiency, accuracy, and personalization, financial institutions are well-placed to leverage its transformative power. AI is transforming payments from merely a back-end process into a strategic asset, by streamlining operations, enhancing fraud detection, enabling dynamic payment routing, and delivering personalized customer experiences.
As banks transition beyond legacy systems and adopt intelligent solutions, they unlock new value, enhance operational efficiency, and provide superior services to their customers.
Whether your bank or financial institution has already started its payments transformation or still uses legacy systems, Wipro can assist you in exploring how AI can be integrated into your current payments environment. This method provides immediate value while preparing you for future infrastructure updates.
