Generative artificial intelligence (GenAI) represents a significant technological advance, capable of generating realistic content such as text, images, and audio from training data. This technology is poised to transform many aspects of daily life, particularly in the payments industry. The potential applications for GenAI in payments range from enhancing consumer payment experiences to revolutionizing operational processes.
Transforming Consumer Payments
Evolution or Revolution?
Over the past 25 years, digital commerce has transformed shopping through its convenience and ubiquity. GenAI is expected to support the next wave of transformation by modernizing the online checkout experience and unlocking a new era of personalized conversational commerce. A simplified GenAI-enhanced shopping journey might look like this:
- Personalized Recommendations: Consumers interact with a chatbot that provides product recommendations based on their preferences.
- Enhanced Customer Support: The chatbot answers detailed questions about products and provides instant support.
- Embedded Checkout: When consumers are ready to buy, a payment plug-in linked to the chatbot enables a seamless checkout experience without leaving the chat interface.
Behind the Scenes Enhancements
Payments companies will support and enhance the shopping experience through several actions:
- Providing payment plug-ins to merchants and GenAI providers.
- Enabling merchants to tailor payment options based on consumer preferences.
- Implementing dynamic pricing and tailored rewards.
- Extending recommendations into areas such as financing.
While AI promises significant advancements in consumer payments, it will be part of an evolutionary process rather than a revolution. High regulatory and security protocols will remain essential, and merchants will continue to have know-your-customer obligations.
Optimizing Payments Operations
Efficiency and Productivity Gains
Most of GenAI's potential in payments lies on the operations side. Payments operations involve high-volume, high-frequency, data-rich activities that are ideal for GenAI applications. These include marketing, sales, product development, onboarding, customer support, risk and compliance, and support functions.
Marketing and Sales
- Customized Marketing: GenAI can improve productivity in market research, content creation, and campaign optimization, delivering an average productivity gain of 25% to 35%.
- Sales Effectiveness: GenAI enables assisted recommendations, tailored product offerings, and dynamic pricing, potentially improving productivity by 28% to 38%.
Customer Service
GenAI will transform customer service activities such as claims handling, onboarding support, incident resolution, and churn prevention. Smart agent assistants powered by GenAI could improve productivity by 35% to 45% and reduce call volumes significantly.
Risk and Compliance
GenAI will play a crucial role in detecting and preventing fraud, automating report generation, and improving trend prediction and anomaly detection. These advancements will lead to cost savings, error reduction, and better resource allocation.
Product Development
In product development, GenAI can enhance coding quality, reduce technical debt, and expedite the onboarding of junior developers. The potential productivity improvement in this area is 28% to 38%.
Transverse Activities
GenAI can automate daily tasks such as drafting emails and composing meeting summaries, increasing productivity by up to 15%. Although the impact is somewhat lower than other use cases due to the fragmented nature of these tasks, the overall efficiency gains are still significant.
Implementing GenAI
Strategic Implementation
To fully realize the benefits of GenAI, payments companies must approach it as a company-wide transformation opportunity. This involves setting clear ambitions, accelerating delivery, and driving scale. A robust, responsible GenAI framework that aligns with the company's overall purpose is essential.
Addressing Challenges
Implementing GenAI requires addressing several challenges, including developing responsible AI frameworks, ensuring data privacy, and managing security concerns. Integrating GenAI with existing legacy systems and handling unstructured data are additional hurdles.