Agentic Commerce

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The emergence of AI agents capable of making autonomous purchasing decisions is reshaping the payments landscape. Agentic commerce—where artificial intelligence systems act on behalf of consumers to discover, negotiate, and complete transactions—presents both extraordinary opportunities and complex challenges for the payments ecosystem.

The Promise and the Problem
AI agents are poised to transform commercial transactions. Unlike traditional e-commerce where humans click through checkout flows, agentic commerce enables software to make purchasing decisions based on learned preferences and sophisticated algorithms. An agent might automatically reorder groceries, negotiate insurance rates, or book travel that optimizes for cost and convenience.
For payments providers, this opens remarkable opportunities. Transaction volumes could multiply exponentially as agents handle routine purchases. Micro-transactions become viable without human approval friction. Agents enable dynamic pricing and real-time optimization of payment methods based on rewards or exchange rates.
The fundamental challenge is determining when an AI agent has legitimate authority to spend money. How do we verify that a transaction genuinely reflects the owner’s intent rather than a hallucination or compromise? Current authentication frameworks were designed for human-initiated transactions, making Strong Customer Authentication and similar regulations problematic when software makes dozens of autonomous daily decisions.
Liability and Trust
When an AI agent makes erroneous purchases—ordering 50 pounds of bananas instead of 5—who bears responsibility? The consumer, agent provider, or merchant? Payment networks have chargeback systems for traditional commerce, but these assume human decision-making. New frameworks must distinguish between agent errors, consumer miscommunication, merchant misconduct, and system compromises while balancing consumer protection with practical adoption.
Effective AI agents require extensive data about preferences, habits, and financial constraints. Payment providers will have unprecedented visibility into purchasing patterns and AI decision-making logic, creating significant privacy risks. Questions about data portability and preventing manipulation of agent behavior will shape competitive dynamics.
Infrastructure Evolution
Payments infrastructure must evolve substantially for agentic commerce. Current latencies acceptable for humans become problematic for agents making rapid decisions. Real-time payment rails may become essential rather than premium features. Standardized APIs will be critical for agents to discover payment options and execute transactions across diverse merchants—without them, the ecosystem fragments.
Fraud detection systems currently identify anomalous human behavior patterns. When AI agents routinely make purchases across geographies or categories, these signals become unreliable. Payment providers need new approaches that distinguish legitimate agent behavior from compromised systems, creating a complex dynamic where machine learning monitors other machine learning.
Regulatory Challenges and Opportunities
Regulators are only beginning to address agentic commerce. Existing consumer protection laws assume humans make purchasing decisions with conscious intent. Anti-money laundering and Know Your Customer requirements were designed for people, not software agents. New frameworks must address agent transparency, consent, and accountability without stifling innovation or pushing commerce into unregulated spaces.
Despite these challenges, agentic commerce creates tremendous innovation opportunities. Companies solving authentication could enable new business models. Payment networks adapting fastest to agent-driven patterns could capture disproportionate value. AI could make payments more inclusive by helping consumers optimize choices and navigate complex fee structures.
Building the Future
The transition to agentic commerce will be gradual. Early adoption will focus on low-risk, high-frequency transactions like recurring subscriptions and routine household purchases. As trust builds, agents will handle higher-value and more complex transactions.
Success requires collaboration across payment networks, financial institutions, technology companies, merchants, and regulators to establish standards and build protective systems. The companies viewing agentic commerce as partnership opportunity rather than threat will thrive.
The convergence of AI and payments represents a fundamental shift in commercial infrastructure. While challenges around authentication, liability, privacy, and regulation are substantial, they’re not insurmountable. By approaching these issues thoughtfully and collaboratively, the industry can unlock agentic commerce’s potential while protecting consumers. The question isn’t whether AI agents will reshape payments, but whether we’ll build that future thoughtfully or haphazardly.

Machine Learning and Customer Experience

Photo by Sergey Katyshkin on Pexels.com

By Donna Bailey  Principle DB Innovators LLC

Payments today is an exciting industry to be involved in – it seems to be ever changing on a monthly basis-what excites me is data, technology and how to provide a best-in-class customer experience.

Innovation has always been part of my DNA so when Artificial Intelligence started gaining new momentum it made sense to research and understand how this area would and could impact payments, the business and the customer.

Satisfied customers = business growth

Machine Learning is the subset of Artificial Intelligence-this article focuses on how companies are using Machine learning to improve the customer experience.

Machine learning is based on classical statistics. Statistical inference does form an important foundation for the current implementations of artificial intelligence. It’s important to realize that AI has been around since the 1950’s but only gained significant ground due to the speed of computers and the vast amount of data sets out there over the past 10 years.

Machine learning is all about models, data and computers – supervised, unsupervised or reinforced learning.

According to Forbes- 75% of companies using ML in their customer experience initiatives are seeing a 10% increase is customer satisfaction*

(Satisfied customers=business growth)

Some critical levers that ML helps to move are :targeting, offer presentation, chatbots, virtual assistants. The more data you have about your customers the better services and products can be built to service them/their needs.

Leveraging customer past purchasing transactions, any experience with customer services, response to offers are all ideal data inputs to help the Machine learning model target the right customer segment or present the relevant online products or offers.

In the payments industry there are some well-known areas that Machine Learning has proven to help businesses and customers.

Chatbots/Customer Service

Erica a virtual financial assistant from Bank of America developed in 2017 really pioneered banking chatbots/virtual assistants. The more customers interact with Erica the more it learns about their situations. Erica integrates with customer service consoles and spending and budget tools. This is a great example of how a chatbot is helping customers manage their money and saving the bank service costs.

Fraud/Anti Money Laundering

One of the more prevalent examples of Machine learning being used in payments is to identify anti money laundering and fraud.

PayPal’s Braintree Auth payments uses PayPal’s consumer transaction data in conjunction with software developer Kount’s fraud detection capabilities to authorize high volumes of transactions and verifications in near real-time. Each credit card transaction or verification is analyzed in milliseconds using hundreds of fraud detection tests.

Money Movement

Mastercard is using Machine Learning to fix ACH transfers that fail. According to

One in 50 ACH transactions fail – through machine learning this issue is being addressed and will reduce the frequency of failed transactions being processed.

Machine Learning is also a critical tool for Risk and Compliance teams and processes in financial services. According to a Deloitte whitepaper Anti Money laundering costs $25 billion to monitor and prevent in the USA. The use of machine learning models will significantly improve results and lower costs.

Collections/Targeting

Banks leverage ML in assessing credit risk segments for multiple use cases such as credit card acquisition, collections, loan applications etc. This reduces the cost of underwriting

Collection practices and debt restructuring work best when closely aligned with borrowers’ changing circumstances and propensity to pay.

Machine learning can help companies build robust dynamic models that are better able to segment delinquent borrowers, and even identify self-cure customers (that is, customers that proactively take action to improve their standing). This enables them to better tailor their collection strategies and improve their on-time payment rates.

TrueAccord’s HeartBeat, for instance, is a machine learning tool that helps lenders customize personal interactions in real time, based on its ability to detect why a customer’s payments are late. Companies using machine learning have been able to reduce their bad debt provision by 35 to 40 percent.

McKinsey has seen 10 to 15 percent improvements in recovery rates and 30 to 40 percent increases in collections efficiency.***

Following an account delinquency, issuers allow a brief time window (usually 90 days) before they write off the receivables and turn collection over to third-party providers. This brief period is an ideal time for issuers to apply collection strategies that draw heavily on the capabilities of machine learning.

There are great efficiencies in using AI to determine credit worthiness for acquiring new customers. Machine Learning can be used in targeting and determining risk segments which can save financial institutions millions of dollars in charge offs. These models can also help to identify the right products for different types of customers like “new to credit” or underbanked.

These are just a few examples of way Machine learning is transforming customer experience in financial services – keep an eye out for more sophisticated solutions surfacing as technology and data continue to evolve.

*Forbes AI And ML Can Transform Financial Services, But Industry Must Solve Data Problem First

** The case for artificial intelligence in combating money laundering and terrorist financing A deep dive into the application of machine learning technology

***McKinsey & Company Beyond the buzz: Harnessing machine learning in payments

September 1, 2016 | Article