Predictive artificial intelligence (AI) has long been the chief financial officer’s best friend.
For years, cutting-edge technology, better known as machine learning (ML) or automation, has silently performed tedious and high-value tasks across operations, allowing businesses to streamline previously manual processes within their domain. We have helped them achieve future-proof efficiencies that drive growth by Accounts Payable (AP) and Accounts Receivable (AR), Cash Flow Forecasting, Credit Scoring, Fraud Prevention, Compliance, and more.
Predictive AI can not only perform large-scale processes faster than humans, but it can also make inferences that humans often miss when it comes to finding patterns and connecting seemingly disparate sources of information.
If a task feels mundane or tedious today, it may be subject to algorithmic advancements tomorrow.
The capabilities of this technology will revolutionize the finance sector and the use of data is at the heart of modern advances in money movements.
As generative AI emerges, many observers wonder what role the accompanying innovations will play in the payment process.
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Accelerate business process agility and effectiveness
Broadly speaking, AI solutions can improve data-intensive and complex processes.
Many day-to-day activities such as sales, invoicing, and inventory management generate large amounts of potentially valuable data. Although the data is not generated for language, the learning potential is large and often rich in intellectual and practical significance.
The history of AI development is driven by the singular insight that large data sets collected for a single purpose can yield potentially new kinds of commercial knowledge through computation and actionable analysis. It’s been done.
Discover how statistical analysis can form the flywheel of digitally-enabled (and often automated) solutions by layering statistical analysis on top of the modern infrastructure needed to collect, process, and generate insights from data. , so almost every sector had its own “data moment”. These solutions accelerate the agility and effectiveness of your business processes.
Predictive AI focuses on analyzing data to predict future events, performing statistical identification to identify core threads of replicable patterns.
Generative AI is a little different. Its algorithms aim to create new content based on the increasingly large data sets it trains on, weaving something new out of the uncovered threads.
Both tools have transformative potential, as evidenced by the fact that “AI” was mentioned more than 200 times in recent earnings calls by Meta, Microsoft, and Alphabet.
Also read: Companies are using their data to improve efficiency with AI
Generative AI plays an important but supportive role in finance
said Andrew Stucchio, vice president of global pricing and analytics at Discover. Global Network, he told PYMNTS in March. “It’s helping them optimize their inventory.”
Emerging applications of predictive AI are already helping payment firms with regulatory compliance, especially for Know Your Customer (KYC) and Anti-Money Laundering (AML) controls. Don’t trade security for convenience.
The use of AI is “very important” for fraud prevention and approval automation, Andrew Gleiser, chief revenue officer at payment provider Aeropay, told PYMNTS this week.
In addition, predictive AI has greatly improved the efficiency of cross-border payments as it streamlines transaction settlement and enables near real-time money movements.
Generative AI is another beast.
By integrating natural language processing (NLP) capabilities, its innovative ability to reveal information and create transaction and customer reports has the potential to transform customer engagement in many ways.
Emily Glassberg Sands, Head of Information and Data Science at Stripe, told PYMNTS in March:
Stripe alone allows Stripe software developers to enter questions and receive condensed answers, and Stripe customers to query and receive answers on their own business analytics.
Echoing that thinking, Aeropay’s Gleiser told PYMNTS that one of the future-friendly use cases for generative AI he sees is integrating the solution into merchant payment portals to improve things like average order value. to present customers with compliant information about their best clients related to the indicators of Overall volume and purchase frequency.
Still, training model integrity and data privacy remain major concerns with generative AI.
“A lot of value [around generative AI capabilities]but the key question is when can we use it without fear of bias, and where does this information come from?” “We understand how AI-driven decision-making can be done to meet the demands of our customers to be fair and to comply with laws and regulations regarding lending, etc. need to do it.”
Erik Duhaime, co-founder and CEO of data annotation provider Centaur Labs, told PYMNTS earlier this month:
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