We spoke with Ravi Rajamiyer, CTO of Exact Payments, about how he foresees the use of artificial intelligence (AI) in the payments industry. Here’s what he had to say…
What use cases do you foresee using AI in your business?
Fundamentally, payments is a technology business, so at a company level, I see AI benefiting our internal processes across the board. As AI helps us enhance productivity across software architecture design, coding, testing, and deployment, we expect a tremendous increase in product delivery velocity, making cutting-edge features available much faster.
For the payments industry as a whole, risk assessment is one of the most important uses. In payments, the faster we can onboard new merchants, the better. However, accuracy is important in this process, and the more thorough we are with risk assessment, the longer it takes. AI can accelerate this process so we don’t have to compromise accuracy for speed.
We can also see AI playing a role in continuous merchant monitoring with AI-driven risk scoring for calculating reserves and holds in payouts. This helps payment companies make sure merchants are operating within the law.
Additionally, generative AI models are showing promise for faster and more efficient customer support with NLP-based (natural language processing) chatbots. For example, an interactive chat experience can offer suggestions and answer questions for our customers as they go through our integration process.
How has AI impacted fraud detection and prevention in payment processing?
Most AI-based fraud detection is algorithm-driven, relying on patterns to determine early on in the processing cycle if a specific payment could be fraudulent, and typically produces results with an acceptable level of accuracy. The vast amount of data routinely collected during the day-to-day operations of payment platforms provides an excellent source for building continuously improving algorithms and training predictive models in real time to flag fraudulent payments.
Another area of huge impact is the availability of AI-powered tools that help risk experts broaden the scope of transaction monitoring beyond human ability. For example, PayPal has a platform that rates an individual transaction as it comes in, and if it is likely fraud, it is easy and clear to turn down that transaction.
What are the key benefits of using AI to streamline payment processes for businesses?
Increased operational efficiency and improved cost reduction would be the key benefits of using AI in payment processes. AI can significantly reduce the time and money spent on risk, fraud, and compliance issues.
For example, chargeback disputes are currently handled manually using various pieces of documentation. Humans are limited by the number of staff members and their output capacity. Often, there are one to three risk operations professionals on a given team due to budget and talent pool limitations. Given that, as disputes scale, we will need AI to score and prioritize disputes so that the human team spends their time focusing on the right disputes.
Automating payment processes like chargeback management with AI can significantly reduce operational costs. It minimizes the need for manual intervention, reduces errors that could lead to costly disputes, and helps avoid fraud-related losses.
How does AI enhance customer experience in payment processing?
AI can analyze customer behavior and preferences to offer tailored payment options, rewards, and recommendations. This level of personalization can enhance the shopping experience, making customers feel valued and understood. AI also enhances customer experience in payment processing by offering faster and more accurate transaction approvals, as well as 24/7 customer support through chatbots—reducing wait times and increasing satisfaction.
What role does AI play in optimizing compliance with payment processing regulations?
AI plays a pivotal role in optimizing compliance with regulations in the payment processing industry, addressing various challenges that stem from the ever-evolving regulatory landscape and the increasing complexity of financial transactions.
For example, AI excels in identifying patterns and anomalies in transaction data that could indicate money laundering activities. By recognizing these patterns, AI helps implement effective anti-money laundering measures, report suspicious activities, and prevent transactions that could violate regulations.
With regulations like the General Data Protection Regulation (GDPR) emphasizing data privacy, AI can help ensure that customer data is handled, processed, and stored in compliance with these laws. AI can automate data privacy controls, detect unauthorized access attempts, and ensure data processing activities meet legal requirements. Overall, payment companies will take a proactive approach to compliance by using AI to mitigate risk and safeguard their reputation.
How can AI be leveraged to analyze payment data and trends for business intelligence and decision-making?
Data is the core of any technology-assisted business, and payments are no exception. Companies are often sitting on a treasure trove of data that they don’t know what to do with, or at the very least, are not harnessing its potential. The power of AI is to process vast amounts of unstructured data to glean useful insights that help businesses make better decisions and drive growth.
One example is utilizing AI to analyze payment data in the context of broader market trends to provide insights into how a business is performing relative to its competitors. This analysis can inform strategic decisions, such as pricing strategies, market-entry, and product development.
By analyzing patterns in payment data, AI can also identify customers at risk of churning. This enables businesses to proactively engage with these customers through personalized offers or targeted communication to retain them. The use cases for AI in business intelligence are seemingly unending, yet we have just begun to scratch the surface of its capabilities.
What are some of the challenges faced in implementing AI in payment processing, and how can they be overcome?
Potential challenges include privacy concerns as AI relies on analyzing vast amounts of personal transaction data. Overcoming this challenge means implementing robust data encryption, anonymization techniques, and secure data storage solutions.
Customers may be wary of AI-driven processes, especially if they perceive them as invasive or if they experience unfair outcomes. In this case, businesses can enhance transparency by explaining how AI is used in payment processing, implementing user-friendly interfaces, and providing options for human intervention when needed.
Additionally, there's the challenge of ensuring that AI systems do not inadvertently introduce bias or unfair practices in their decision-making processes. AI systems can inherit biases from their training data, potentially leading to unfair treatment of certain customer groups.
The solution for this challenge is using diverse and representative datasets to train AI models. Regularly audit and test AI systems for bias and implement corrective measures when biases are detected.
Can AI help in predicting future payment trends and consumer behavior?
AI can process and analyze vast amounts of data from various sources, including transaction records, market research, and economic indicators. This capability allows AI to identify trends and patterns that may not be visible through traditional analysis methods. By identifying these patterns, AI can forecast how these trends will likely evolve.
Using predictive analytics to forecast these future trends, it can then predict peaks in demand for certain payment services, anticipate shifts towards new payment technologies (like cryptocurrencies or mobile payments), and identify emerging markets or demographics.
AI can also analyze social media and other online platforms to gauge consumer sentiment towards payment methods, brands, and financial institutions. This sentiment analysis can indicate potential shifts in consumer behavior, allowing businesses to adapt their strategies proactively.
Lastly, AI can simulate various economic and market scenarios to see how they might affect payment trends and consumer behavior. This ability helps businesses and financial institutions prepare for different potential futures, ensuring they remain agile in a rapidly changing environment.
What are your thoughts on the future of AI in payments?
The future of AI in payments appears to be both transformational and expansive, driven by continuous advancements in technology and growing acceptance among businesses and consumers. AI is expected to become an integral part of all aspects of payment processing, from fraud detection and customer service to personalized marketing and risk management.
Many of us in the industry are excited for AI to enable hyper-personalization in payment services. AI can significantly enhance customer loyalty and engagement by offering tailored recommendations, dynamic pricing, and personalized financial advice based on individual spending habits and preferences.
AI also has the potential to increase financial inclusion by making payment services more accessible to underserved populations.
For example, AI can assess alternative data to provide credit scores for individuals without traditional credit histories, opening up access to financial services. These use cases and others described in this interview show that AI has the potential to transform the payments business by improving security, efficiency, and customer experience. As AI technology continues to evolve, its integration into the payments industry will likely deepen, creating new opportunities and reshaping the payments landscape.
Ravi Rajamiyer, CTO of Exact Payments
Ravi Rajamiyer brings more than 20 years of experience building and leading technology teams across industries ranging from global payments to cloud security, AI/machine learning, data center virtualization, and mobile application development. At PayPal, Ravi led the engineering team responsible for global payments platform development. His work focused on building exceptional customer experiences across the merchant lifecycle, including onboarding, risk/underwriting, servicing, reporting, and dispute management. He and his team led the execution of a cloud-native transformation of payments infrastructure covering a global footprint.
Ravi received his M. Tech. from the Indian Institute of Technology in Mumbai and completed his PhD from Washington University in St. Louis, MO.
