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Visa Consulting & Analytics

January 2024


3 - 4 Minutes

Generative AI in payments

The rapid rise of ChatGPT propelled generative artificial intelligence (AI) from the niche to the mainstream. Suddenly everyone has an opinion on how generative AI could be leveraged, and leadership teams are now left wondering how, when, where, and why it should be applied in their own businesses.

This is true in the world of payments.

Payment businesses have some unique characteristics. The risk profile, the regulatory scrutiny, the competitive intensity, and the need to segment and engage closely with customers throughout the product lifecycle, all mean that generative AI could have profound implications with potential opportunities to seize, and new challenges to navigate. Below is a quick introduction to generative AI and some of the payment-specific opportunities to help financial institutions (FIs) acquire more customers, drive customer engagement, and enhance operational excellence for optimal growth.

Visa’s 30-Year Heritage in AI

In 1993, Visa became the first network to deploy AI-based technology for risk and fraud management, pioneering the use of AI models in payments. In the last 10 years alone, the company has invested more than $3 billion in AI and data infrastructure to enable the safer, smarter movement of money and to proactively identify and prevent fraud.1 With several AI and machine learning models in production, Visa’s AI and deep learning capabilities help to solve longstanding challenges and pain points for buyers, sellers, and financial institutions.

As AI transforms the payments industry, businesses across various sectors can anticipate new opportunities in customer management, improved customer experiences, and enhanced operational efficiency.

A quick introduction to generative AI

Generative AI is a broad label describing any type of AI that can be used to create new digital content, like text, images, video, audio, or computer code. It is typically powered by foundation models, which are expansive neural networks trained on vast quantities of unstructured, unlabelled data in a variety of formats, such as text and audio. Foundation models can be used for a wide range of tasks. This is very different from existing predictive AI models, which were usually designed to perform just one task. For example, an AI model that can predict customer attrition can only use data about customers and their behavior, and cannot do anything else, such as writing a review or recognizing a face.

“The generative AI revolution is not happening in just one or two select industries – it’s everywhere. The technology is already transforming business growth strategy, improving services and productivity in general,” says Sam Hamilton, SVP, Data and AI Platforms, Visa.

Today’s data, computational power, and sophisticated large language models (LLMs) put the payments sector on the cusp of a new era of AI. Generative AI has the potential to transform how we work, how we develop and build new products and services, and how we serve our clients.

Appreciating some of the upside opportunities

The commercial impact of generative AI holds significant potential to transform entire industries. And, given the unique characteristics of the payments business, the impact of generative AI could be even more profound. Below are some potential ideas for the strategic use of generative AI:

Acquiring more customers

  • Understanding the market and its hidden nuances: Generative AI could be used to effectively analyze vast datasets including market trends, competitor activities, and customer behavior – thereby generating actionable insights to identify emerging trends and drive strategic decisions.
  • Developing winning products and propositions: Generative AI could bring new ways to analyze customer behavior and social sentiment to discover unmet or under-met needs and guide new product and experience design.

Driving more engagement

  • Crafting compelling messaging and marketing: Generative AI could analyze customer spend behavior and preferences to generate more relevant and personalized marketing and outreach with a quicker ability to pivot and adjust based on performance.
  • Providing a more personal and personalized service: With generative AI, there is the potential to introduce a more conversational approach to everyday banking interactions. Routine customer interactions could be migrated from static interfaces with prepopulated FAQs, to chatbots that could dynamically and continuously improve customer interaction preferences.
  • Managing customer lifecycle: Using generative AI to combine various threads of structured and unstructured customer data can provide timely insights on a customer’s readiness for things like an upgraded product, or special financing terms.
  • Enhancing loyalty: Generative AI could help assess behavior patterns and identify engagement opportunities to enrich loyalty experiences.
  • Enhancing customer retention: Generative AI could be trained to bring more speed and accuracy to customer retention programs, identifying factors that indicate which customers are at risk of leaving, and enhancing loyalty and incentives.

Improving customer experience

  • Increasing the speed and accuracy of manual tasks: Generative AI could be used across the business to help humans increase their effectiveness and productivity in rote, manual tasks. It can provide quick “first drafts” of documents like meeting minutes, organizing routine tasks to focus on strategic priorities.
  • Improving authorization: Generative AI could potentially be applied to existing models scoring individual transactions to improve authorization rates.
  • Enabling more granular operational decision-making, such as fraud: Generative AI could potentially assess data connections and sequence data in much more granular ways, identifying connections and allowing for more surgical decision-making that can improve authorization decisions while also helping to combat fraud.
  • Delivering attentive, always-on personalized service: Generative AI could be used to develop more human-like chatbots, offering personalized 24/7 customer support – thereby enhancing customer satisfaction and reducing the workload on service agents by analyzing customer interactions before escalating to a human agent.

Pre-considerations for Generative AI implementation

With boundless innovation and opportunities there are just as many boundless limits, so implementation strategy is vital. Implementing generative AI too quickly and without proper infrastructural support could mean inadvertently exposing the organization to performance, operational, and reputational issues.

Given the nature of the payments business, and the appropriate risk-balancing nature of many companies that operate within it, this is an area that needs appropriate consideration before generative AI applications are implemented. Below are four of many considerations for adoption.

1. Transparency: Making generative AI usage clear and understandable
Vital to successful implementation, transparency around generative AI usage includes building the capability to educate and train users on outputs.

2. Fairness: Placing ethical safeguards in generative AI responses
Responsible generative AI use needs to be fair, so companies using generative AI should monitor and be able to remediate biases, if any, that show up in generated responses.

3. Security: Protecting users from cyberattacks
With any new technology, there may be opportunities for bad actors to commit even more sophisticated and rapid cyberattacks on unsuspecting users. Companies should be aware of AI-specific cyber risks and implement appropriate mitigating safeguards.

4. Reliability: Creating a reliable operating model
By design, generative AI models can produce many unique answers in response to one prompt. Users are then responsible for the reliability of generated outputs by vetting their accuracy.

Choosing the right AI tool is important

With the attention generative AI is capturing, it is important to understand that generative AI is a subset of a wider artificial intelligence/machine learning (AI/ML) toolset. Choosing the right tool for the right use case is critical to optimizing the value achieved in any AI implementation.

Generative AI based methods can use richer transactional data to identify unique patterns and merchant preferences. Transactional sequence signatures translated into high-dimensional vector embeddings would minimize information loss which occurs with data aggregation to directly power our next generation of models. Actions taken based on these models would be more relevant to customers with higher levels of accuracy as they are using more data. For example, with the greater computing and predictive powers of generative AI, more factors relevant to a model’s scoring and assessment of a transaction can be analyzed without tradeoffs, benefitting clients and creating more trust.

Six broad considerations for payments businesses

While generative AI is still in its early days, here are a few considerations for FIs intending to implement generative AI within their own organizations:

Recommendation #1: Get familiar with what generative AI is (and isn’t) and link to existing business objectives

  • Get familiar now, including experimenting with generative AI, even on a personal level with open tools, to understand how it works.
  • Identify a use case that is closely and clearly aligned with a pre-existing business objective so your organization is better equipped to assess success factors.
  • It could be about improving digital acquisition, enhancing loyalty, or streamlining operations. What is important is to pick a use-case that is relevant, has a strong strategic rationale, has well-structured success metrics, and helps to achieve a tangible business goal.

Recommendation #2: Build a strong foundation in data infrastructure, governance, and transparency

For any FI, data management is a critical consideration. Careful thought should be given to three factors:

  • Infrastructure – the systems and processes used to collect, store, and manage data need to be robust, while the data needs to be accurate, complete, and consistent. This is important because generative AI models are trained on data, and the quality of the data will directly impact the quality of the outcomes. Be sure technical infrastructure is supported with the right human capital to work alongside to provide the right oversight to the AI.
  • Governance – the quality and security of data need to be championed as a core goal. This is important to ensure that the data is used in a responsible and ethical way. It also helps to protect the data from unauthorized access or misuse.
  • Transparency – implement processes to trace the flow and use of data. This is important because it helps to ensure that the models are accountable, and the data is not being used for malicious purposes. It can also help to build trust with users and stakeholders.

Recommendation #3: Foster a culture of innovation and experimentation

  • The initial reaction of some FIs has been to block all access to generative AI programs to their employees. Having limited practical knowledge of generative AI carries certain risks of creating blind spots relative to how it is shaping the business landscape, both in strategic opportunities and potential risks to your business.
  • Instead, FIs should provide their team a safe and secure environment to familiarize themselves with generative AI so they can better understand what it is, how it works, its potential, and its limitations.
  • They should also actively encourage teams from across the business to explore the capabilities of generative AI, and focus on the likely implications for the business, the functions most likely to be impacted, the type of initiatives that could be piloted, the suppliers and vendors who specialize in generative AI, and how peer-group organizations are responding.

Recommendation #4: Prioritize ethical considerations

  • Develop guidelines and policies that empower ethical uses of AI, including fairness, transparency, and accountability.
  • Building trust around AI is crucial, so consider educating employees and customers on when, where and how AI is being used.

Recommendation #5: Invest in talent and expertise

  • Generative AI has only just burst onto the mainstream. Hence, it is a subject that a lot of people may know a bit about, but few people know a lot about.
  • FIs that are serious about deploying it in your business should invest in the requisite expertise, by providing formal training for your existing talent, recruiting more people with proven data science know-how, and enlisting trusted advisors.

Recommendation #6: Collaborate with external partners

  • The tech ecosystem is already home to a growing group of generative AI specialists offering solutions.
  • To accelerate implementation, and supplement skills gaps, FIs should prepare to draw on their expertise, resources, models and frameworks, but should also remain cognizant of data-management risks.
  • Ensure that data integrity and data privacy are prioritized in partner selection, and that they are prepared for the regulatory and reputational issues.

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Case studies, comparisons, statistics, research and recommendations ("Information”) are provided “AS IS” and intended for informational purposes only and should not be relied upon for operational, marketing, legal, technical, tax, financial or other advice. Visa Inc. neither makes any warranty or representation as to the completeness or accuracy of the information within this document, nor assumes any liability or responsibility that may result from reliance on such information. The Information contained herein is not intended as investment or legal advice, and readers are encouraged to seek the advice of a competent professional where such advice is required. When implementing any new strategy or practice, you should consult with your legal counsel to determine what laws and regulations may apply to your specific circumstances. The actual costs, savings and benefits of any recommendations, programs or “best practices” may vary based upon your specific business needs and program requirements. By their nature, recommendations are not guarantees of future performance or results and are subject to risks, uncertainties and assumptions that are difficult to predict or quantify. 
All brand names, logos and/or trademarks are the property of their respective owners, are used for identification purposes only, and do not necessarily imply product endorsement or affiliation with Visa.

1 “30 years of AI and counting”, Rajat Taneja,, Sep 14, 2023,

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