The generative AI boom is set to become a major disruptive force throughout global industries, and the finance sector will see a significant transformation thanks to the next-generation technology.
While much of the furor surrounding generative AI has been focused on the emergence of large-language models (LLMs) like ChatGPT and its ability to comprehend and answer virtually any complex query or user prompt, the technology’s impact on the world of finance has been more nuanced.
There’s little doubt over the scale of the GenAI boom. Forecasts suggest that the generative AI in fintech market size is set to swell to $6.256 billion by 2032, representing a CAGR of 22.5% during the forecast period.
These great expectations underline the disruptive potential that this fresh iteration of artificial intelligence holds for the financial sector.
The timing of the generative AI boom couldn’t be any better for a payments industry seeking ways to embrace its own open finance revolution.
For an industry that’s spent many years battling legacy processes in its bid to embrace digital transformation, generative AI may be the technology that the financial sector and payments industry as a whole have been waiting for.
With this in mind, let’s dive into the ways that generative AI is already working to transform the payments industry as we know it today:
Democratizing Big Data
Perhaps the most powerful force that generative AI will bring to the payments landscape will revolve around the democratization of big data.
One driving force of the generative AI boom is the growing capabilities of machine learning (ML) and its ability to combine with GenAI to provide data-driven insights and even deliver autonomous personalization tools to users.
This will help to provide open finance users and firms alike with unprecedented insights surrounding payment data.
With open finance providing interconnectivity between a multitude of financial instruments, users will be capable of gaining comprehensive overviews of how they’re spending their money and their most common payment processes for transactions.
The masses of big data users generate when using financial services will empower consumers to ‘have a conversation with data’ using LLMs, according to Daragh Morrissey, Director of AI at Microsoft Worldwide Financial Services.
However, generative AI has the power to adopt a far more proactive approach when it comes to open finance. Big data insights identified through machine learning and curated through generative AI will be capable of offering spending advice for consumers based on their designated budget each month, adaptive investment advice depending on their specific saving goals, and even bespoke payment recommendations based on transaction fees and security insights.
Generative AI will transform the modern payment processing system as we know it and can help guide more users toward digital wallets and even cryptocurrency payments based on metrics like location, funds, security, fees, and transaction speed. Should an alternative payment method offer greater value, safety, or efficiency, generative AI in open finance can make a suggestion or even automate the payment process depending on user preferences.
Unlocking the Power of Prediction
The utility of big data within open finance can also empower generative AI models to offer predictive analytics to help anticipate future outcomes and trends.
This means that generative AI can help businesses anticipate customer behavior, identify risks that could harm efficiency, and optimize business processes to help improve the quality of payments in-store and online.
As we’ve already touched on, generative AI can use consumer trends to recommend alternative payment methods, and this extends to businesses accepting payments also. Should data indicate that more customers are intending to use digital payments, providers can act on these insights to ensure their payment processors support a wider range of payment options.
Additionally, we can see machine learning mechanisms improve the borrowing and buy now pay later (BNPL) landscape by taking a more nuanced look at historical payment data for customers to create an accurate prediction on their likelihood of paying back the loan they’re requesting.
This could see archaic credit checks offered by Equifax and Experian replaced by a more dynamic risk assessment tool, opening the door for better payment functionality for BNPL payment options like Klarna and Afterpay.
Mitigating the Threat of Fraud
Generative AI has the potential to innovate far beyond the confines of convenience and can offer some significant security benefits to businesses seeking to shore up their defenses against fraudulent activity.
Because ML can instantly contextualize and analyze a multitude of data points linked to transactions, generative AI algorithms can operate as an intelligent payment gatekeeper that can decide whether to approve, refuse, or quarantine attempted transactions in a fraction of a second.
Because open finance relies on the seamless integration of various financial services and tools, combatting fraudulent acts has become more important than ever before. The age of AI will invariably lead to more sophisticated attacks, but we’re already seeing examples of artificial intelligence actively improving defenses to keep users and businesses safe.
Many companies have sought to utilize generative AI as a means of keeping payments protected, and the recent launch of Visa’s Account Attack Intelligence (VAAI) Score underlines the role that GenAI will play in the future of fraud detection.
VAAI analyzes transaction data in real time to determine the likelihood of enumeration attacks in card-not-present payments. With $1.1 billion lost to this form of attack annually, generative AI is already building its presence as a protective force in the age of open finance.
Next-Generation Compliance
McKinsey data suggests that the first wave of adoption for generative AI among financial institutions will focus heavily on matters of security and compliance.
Already, use cases are emerging of GenAI becoming adopted at an enterprise level as a digital regulatory assistant that can actively monitor compliance by training machine learning algorithms on existing regulations, company policy, and operational guidelines.
Because of its functionality as a code accelerator, generative AI tools can scan code frequently to ensure that compliance is observed at all times as well as alert decision-makers over any misalignment and gaps.
In monitoring for prospective breaches in compliance, generative AI can serve as a significant money-saving tool for financial institutions and their payment solutions.
Responsive Customer Service
We’re also seeing plenty of use cases emerge for generative AI solutions improving the quality of customer service for open finance users surrounding payments.
Large-language models like ChatGPT are pioneering technologies in the field of generative AI, and they act as a seamless fit for optimizing the customer experience throughout the industry.
LLMs can actively analyze queries, issues, and customer pain points for contextual cues and tap into banks of machine learning insights to offer focused answers in a natural manner.
These solutions can offer around-the-clock support for customers or offer assistance in summarizing and translating international regulations and contracts. This can offer a significant level of help internally and externally in accessing essential information without having to jump through comprehension hurdles.
Crucially, for customers, generative AI chatbots can provide a more inclusive customer experience by adapting answers to a conversational level that complements the user’s preferred lexicon.
Generative AI can also excel in breaking down international boundaries for companies that offer payment services in different countries. Here, users can ask questions and receive answers in their preferred language, helping financial firms offer a positive CX in a borderless manner.
Innovation Through Code Generation
Finally, generative AI will be capable of driving innovation throughout the payments landscape for ambitious financial firms thanks to its ability to generate new code for a wide range of programs.
Where developers would be needed to create innovative payment solutions for businesses, GPT-3 can create sample code for countless scenarios with a future view to producing brand new payment hardware and software at a far lower cost and in a more efficient manner.
This implementation can complement existing developers, who could use the time-saving tool as an opportunity to improve their creativity and add more complex additional features to boost innovation.
Machine learning could then use its collaboration with developers to further refine its coding to help make the end product even easier to implement.
With the help of generative AI coding, we could see a far quicker level of digital transformation throughout a payments landscape that’s long struggled to overcome legacy processes. Additionally, the time to market for these features could be significantly shortened.
Today, GPT-3 can work as a real-time assistant in identifying and removing bugs within its coding. This can help to streamline the R&D process for financial firms and accelerate the growth of payment technology throughout the industry.
Embracing Payment Transformation
Generative AI is set to have a profound impact on the payments industry and wider financial landscape as a whole.
From revolutionizing the way consumers pay for products to safeguarding against instances of fraud and payment insights at enterprise level, artificial intelligence may be the technology that a financial landscape in need of digital transformation has been crying out for.
For the firms that embrace this transformative technology, it may be possible to innovate ahead of rivals and to offer a far more conducive customer experience that will be rewarded in the bottom line. Generative AI will change the future of finance, and decision-makers should begin their preparations today.
About Author
Dmytro Spilka – CEO
Dmytro Spilka is a CEO at Solvid and founder of Pridicto. His work has been published in Entrepreneur, Creative Bloq, Shopify, Zapier, Make Use Of, Mention, WordStream, and Campaign Monitor.
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