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Generative AI is changing the way we think about modern technology. ChatGPT is everywhere, and businesses are investing billions to explore how this emerging technology can enhance their offering.
One of the most interesting (and potentially impactful) use cases for artificial intelligence is within the payments industry.
Machine learning (ML) — a subset of AI — ingests vast quantities of data to detect patterns, make predictions and optimize decisions in real time.
This unlocks compounding benefits for businesses, consumers and financial services when effectively layered into financial technology (fintech).
See how machine learning is being used for better payments technology. Plus Aeropay’s approach to AI for smarter pay by bank transactions.
Basic forms of machine learning have been around and in use for decades.
Back in the '90s, early algorithm-based filters scanned emails, looking for certain words in order to calculate spam.
In the early 2000s, online retailers started using ML for personalized product recommendations.
What's new about machine learning models today is their ability to learn and improve upon themselves.
ML also processes far more data, makes real-time decisions and can adapt instantly to new patterns with minimal human intervention.
All this makes machine learning a powerful fit to improve and disrupt the payments ecosystem.
Machine learning models work best when they’re given access to well-structured data sets. The better the data, the more effective the output.
Payments are especially well-suited for machine learning disruption because they involve structured, repeatable decisions made at scale.
Machine learning models can be effectively intertwined within payments systems—helping to improve transaction approvals, fraud prevention and financial automation.
Here are some specific examples:
Retail banks spent $4.9 billion on AI platforms in 2024. This level of investment will greatly improve automation in what is a traditionally labor intensive sector of the United States’ financial infrastructure.
For example, The U.S. Department of the Treasury's Office of Payment Integrity began using machine learning AI to deal with increased fraud and improper payments, preventing and recovering over $4 billion in fiscal year 2024.
As AI becomes further realized across financial infrastructure, we will see an increase in automation that will ultimately help financial institutions better serve their customers.
In some cases, AI can be a double-edged sword. While the technology enables faster innovation, it can also be used by fraudsters to develop more advanced schemes and bots.
Fraud prevention is evolving because fraudsters are using AI too. It’s an arms race, but one that actually fuels innovation as more companies invest time and resources in AI to fight fraud.
Many innovations in payments, from chip cards to 3D Secure to biometrics, were driven by the need to counter bad actors. AI is the next frontier in that fight.
Traditional fraud detection has been reliant on binary, predefined rule-based algorithms, which often fail to keep pace with new fraudulent tactics. The success rate of these systems is low—only around 2% of flagged transactions actually reflect a true crime or malicious intent.
Machine learning algorithms use predictive rules that automatically recognize anomalies in payment data sets. These advanced algorithms are much better equipped to reduce false positives and increase actual fraud reduction.
How ML works for fraud detection: Machine learning learns from transaction behaviors to recognize anomalies that indicate potential fraud. The models do this before the transaction is processed. AI tools better support fraud prevention because they're able to sift through massive data sets and extract meaningful insights, all while constantly improving.
Still, AI alone is not enough. Human oversight remains critical to refine AI models and ensure the right data points are being considered.
At its core, payment decision-making begins with a simple question:
Does the customer have sufficient funds to pay for this transaction?
That starting point seems basic, but has proven difficult for some providers to achieve at scale, particularly in guaranteed payment scenarios.
For example, guaranteed pay by bank historically saw a mere 40-60% average approval rate. Which is why Aeropay has specifically focused on building models that help balance risk and approval.
Aeropay’s risk engine uses machine learning to continuously adjust based on user activity across the entire network, including all merchants, multiple industries and each user’s behaviors. The result is far lower return rates, alongside 90%+ approval rates.
Instead of simply rejecting borderline cases, a smart AI system uses algorithms to intelligently approve transactions by evaluating additional variables such as:
See how Aeropay’s machine learning models accurately approve over 90% of guaranteed pay by bank transactions.
At the end of the day, AI improves the technology powering payments. For merchants, this makes a difference in the efficiency and cost of your payment methods.
Specific benefits include:
AI has the capacity to both reduce manual tasks and optimize operations for payments:
AI can help merchants convert more customers and ensure funds are actually processed in a timely manner:
Machine learning models offer a smarter, automated approach to fraud detection and reduction:
Pay by bank is an emerging payment solution that combines instant account verification with modern ACH payment processing. Operationally, this involves connecting a consumer financial account using application program interfaces (APIs).
Instant bank linking unlocks real time data which is used to initiate and authorize payments.
Historically, ACH payments were unpredictable. eChecks and wire transfers have limited standardization, risk assessment and data to support reliable transactions at scale.
Now, pay by bank solutions like Aeropay leverage robust bank connections and real time data to enhance the entire end to end payment process. This provides businesses access to the affordable pricing of ACH processing, with a modern interface that protects revenue and improves payment experiences.
At Aeropay, we have been investing in machine learning and AI technology since inception. Our platform is built to harness the right data for advanced machine learning models, enabling smarter, safer payments at scale.
Using a proprietary suite of tools, including an aggregator designed to access structured data points, our machine learning models reduce returned or failed payments and optimize pay by bank transactions at scale.
Aeropay’s models analyze millions of transactions across millions of users, continually learning and improving, to generate industry-leading approval rates, with optimally low returns.
AI thrives on structured data. The better the data, the better the decision-making.
Aeropay’s framework is effective because our ML models learn and make decisions from the right data points.
Using structured financial data from millions of consumer transactions in the Aeropay network, the platform accurately makes real time decisions that optimize each step in the payment journey.
Many pay by bank providers still rely on legacy verification methods like screen scraping or micro-deposits, leading to authentication failures and user friction.
But without the real time insights that come from direct bank connections, these brittle solutions don’t provide enough comprehensive information to make smart decisions.
To solve for this, Aeropay developed a proprietary data aggregator, called Aerosync. It’s used to onboard users in under 20 seconds, instantly linking their financial accounts using open APIs and instant account verification methods.
Aerosync collects real time data using the most data-rich connection possible. This well-structured data is what enables our machine learning models to make accurate decisions at scale.
Aeropay’s platform utilizes highly effective ML models—making it the smartest pay by bank system available.
Our approach is unique because it doesn’t involve unnecessary data points that add friction and increase costs. Aeropay models intelligently analyze only the most relevant inputs, including:
Those data points allow the A2A payments platform to intelligently determine transaction success at an industry-leading 90% of the time, on average. Decisions made by our AI models are fully transparent, with audit logs allowing businesses to review why transactions were approved or declined.
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Each component of the Aeropay platform is designed and owned in-house. User bank authentication, account verification, money movement and settlement are handled within the same system, which streamlines payment efficiency and reduces failures.
Aeropay owns its network for continuously improving data points used by AI models to learn, adjust and enhance decision making. As more merchants are added across new verticals, those models will continue improving.
AI doesn’t operate in isolation. Which is why Aeropay builds merchant-specific guardrails to enhance fraud prevention by tailoring verification and detection to industry-specific patterns.
The outcome for merchants is;
Get in touch to learn about implementing Aeropay at your business.
