Big data. It is one of the most valuable assets in the world of business, as data-driven AI touches almost every aspect of our lives—whether it’s making our homes and cities smarter, making medical care more accessible, or getting our internet shopping packed and shipped within minutes of us placing an order.
But huge datasets alone are not enough, particularly in payments—an industry that is fast becoming the axis around which many people’s daily lives revolve. It needs to be the right data.
So how can you make sure that the AI you choose to use in your business is powered by enough data and, more importantly, by the right data?
Quantity and quality: two sides of the same coin
In payments, AI powered by big data has been a gamechanger for years, from helping prevent fraud to making global transactions quicker, safer, and simpler. It's easier than ever to buy what we want, when we want, from almost anywhere we want and how we want—worry free.
This is thanks in part to the large, valuable, and actionable datasets that drive payments AI. VisaNet, for example, uses precise analytics to process more than 141 billion global transactions annually.1 That staggering number of high-quality data is then used to drive our AI powered payments solutions.
But how should we assess the quality of data measured? Here are some of the fundamental markers of data quality you can look for:
Accuracy and reliability: achieved by using advanced analytics on large and diverse global datasets, so that the information is more accurate and does not contradict other trusted resources.
Relevance and completeness: this is the process of making sure that you have all the relevant information. Sounds simple enough, but getting this insightful data requires access to a broad ecosystem and historical trend analysis across a wide range of criteria.
Timeliness: in a world that moves fast, the necessary data at the right moment can be crucial. Having access to real-time transaction data, for example, can be crucial for payments acceptance.
When data delivers on all the above, businesses know they can rely on their AI models to deliver the right outcomes for their requirements.
How data influences AI in payments
AI is so much more than the sum of its parts, particularly when so many of those parts are often invisible. You should understand how data is fuelling AI, and to do that you should know how to evaluate the data itself.
Improved model performance: With more data, AI models can learn more complex patterns and make better predictions, by capturing nuances that might be missed with smaller datasets.
Feature representation: Big data provides a more comprehensive representation of features and attributes in the underlying distribution, leading to more accurate and meaningful feature extraction and improving the model's ability to make accurate predictions.
Generalization and robustness
Enhanced generalization: AI models trained on big data are better equipped to handle a wide range of scenarios, as they have learned from a diverse set of examples, reducing the risk of overfitting to specific instances.
Rare events: Big data increases the likelihood of encountering rare events, which are crucial for training AI models to handle unexpected situations effectively.
Complex problem solving: Big data is crucial for tackling complex problems. The sheer volume of data allows AI models to capture and understand intricate relationships and multiple variables more effectively.
Fair and ethical AI
Reduced bias: A larger dataset can help mitigate bias by providing a more comprehensive and diverse representation of the population. This can lead to fairer and more ethical AI systems that avoid making unjustified or biased decisions.
Learning and adaptation
Continuous learning: With a large and constantly refreshed dataset, AI models can continuously improve their performance over time as they encounter new examples.
Transfer learning: Using large datasets to pretrain AI models teaches them useful representations of data, accelerates the training process, and improves the model's ability to adapt to new tasks.
Scalability and innovation
Scalability: Big data frameworks and infrastructure allow AI systems to scale up and handle larger datasets efficiently. This scalability is essential as data volumes continue to grow.
Innovation and research: Big data enables the development of more sophisticated algorithms and architectures, opening up opportunities for innovation and research in AI.
Working hard with 141 billion data points
As our everyday actions and interactions become increasingly digitalized, and in some cases exclusively so, businesses are learning more about our behaviors, our preferences, and our expectations. Payments are a source for this very data.
VisaNet processes over a hundred billion transactions which leads to incredible insights.
And it’s these insights that allow us to build AI models that help you get more out of your business, by finding the right balance between reducing fraud rates, improving approval rates, and lowering operational costs.
In 2022, Decision Manager, a Cybersource product and part of the Visa Acceptance Solutions family, screened over $347 billion worth of transactions.2
That’s not just a remarkable amount of money—it is, again, a staggering number of insights into people’s spending habits and behaviors. Harnessing them through powerful AI opens up the future for businesses.
Are you open to the possibilities?
Explore more on our Knowledge Center for AI in payments.
1 VisaNet transaction volume based on 2022 fiscal year. Domestically routed transactions may not hit VisaNet.
2 Represents the value of payments processed through Decision Manager in the 2022 fiscal year. Normalized for USD.
Disclaimer: Information, content, comparisons, research, and recommendations 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 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.
This content includes references to offerings from Cybersource and Authorize.net, which are part of the Visa Acceptance Solutions family of brands.