The rise of generative artificial intelligence is set to become a major disruptive force in global industries, and the financial sector will experience significant transformation thanks to next-generation technology.
While much of the buzz around generative artificial intelligence has focused on the emergence of large language models (LLMs) like ChatGPT and their ability to understand and answer virtually any complex user query or challenge, the technology’s impact on the world of finance has been greater. nuances.
There is no doubt about the scale of the GenAI boom. Forecasts indicate that the generative artificial intelligence in fintech market size will increase to $6.256 billion by 2032, representing a CAGR of 22.5% during the forecast period.
These high expectations underscore the disruptive potential that this new iteration of AI has for the financial sector.
The timing of the generative AI boom couldn’t be better for the payments industry, which is looking for ways to embrace its own open finance revolution.
For an industry that has spent many years struggling with legacy processes to embrace digital transformation, generative artificial intelligence may be the technology that the financial sector and the payments industry as a whole have been waiting for.
With that in mind, let’s dive into the ways generative AI is already working to transform the payments industry as we know it today:
The democratization of 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 of the drivers behind 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 provide users with autonomous personalization tools.
This will help open finance users and businesses alike provide unprecedented insights into payment data.
Thanks to open finance, which provides a link between a multitude of financial instruments, users will be able to gain a comprehensive view of how they spend their money and their most common payment processes for transactions.
According to Daragh Morrissey, director of AI at Microsoft Worldwide Financial Services, the large amount of big data users generate when using financial services will allow consumers to “conversate with data” using LLM.
However, generative AI has the ability to take a much more proactive approach when it comes to open finance. Big data insights identified by machine learning and managed using generative artificial intelligence will be able to offer consumers spending advice based on their designated budget each month, adaptive investment advice depending on their specific savings goals and even tailored payment recommendations based on transaction fees and security. observations.
Generative AI is transforming the modern payment processing system as we know it and can help drive more users towards digital wallets and even cryptocurrency payments based on metrics such as location, funds, security, fees and transaction speed. If an alternative payment method offers greater value, security or efficiency, generative artificial intelligence in open finance can design or even automate the payment process depending on the user’s preferences.
Unlocking the Power of Prediction
The utility of big data within open finance can also enable generative AI models to offer predictive analytics to help predict future outcomes and trends.
This means that generative AI can help businesses predict customer behavior, identify risks that could harm efficiency, and optimize business processes to help improve the quality of payments in stores and online.
As we mentioned earlier, generative AI can use consumer trends to recommend alternative payment methods, and this also applies to businesses accepting payments. Should the data indicate that more customers intend to use digital payments, providers can act on these insights to ensure that their payment processors support a wider range of payment options.
In addition, we can see machine learning mechanisms improving the buy-now-pay-later (BNPL) lending environment by taking a closer look at historical payment data for customers to make an accurate prediction of their likelihood of repaying the loan they are applying for.
This could lead to the replacement of archaic credit checks offered by Equifax and Experian with a more dynamic risk assessment tool, opening the door to better payment features for BNPL payment options such as Klarna and Afterpay.
Mitigating the threat of fraud
Generative AI has the potential to innovate far beyond convenience and can offer some significant security benefits to businesses looking to strengthen their defenses against fraudulent activity.
Because ML can instantly contextualize and analyze large amounts of data points associated with transactions, generative AI algorithms can act as intelligent payment gateways that can decide in a fraction of a second whether to approve, decline, or quarantine attempted transactions.
As open finance relies on the seamless integration of various financial services and tools, fighting fraud has become more important than ever. The age of AI will always lead to more sophisticated attacks, but we’re already seeing examples of AI actively improving defenses to keep users and businesses safe.
Many companies have sought to use generative AI as a means of maintaining payment protection, and the recent launch of the Visa Account Attack Intelligence (VAAI) Score underscores the role GenAI will play in the future of fraud detection.
VAAI analyzes real-time transaction data to determine the likelihood of card-not-present billing attacks. With an annual loss of $1.1 billion due to this form of attack, generative AI is already establishing its presence as a protective force in the age of open finance.
New generation compliance
Data from McKinsey suggests that the first wave of generative AI adoption among financial institutions will focus heavily on security and compliance issues.
Use cases are already emerging where GenAI is being adopted at the enterprise level as a digital regulatory assistant that can proactively monitor compliance by training machine learning algorithms on existing regulations, company policies and operational guidelines.
With its code accelerator function, generative AI tools can frequently scan code to ensure compliance is maintained at all times, as well as alert decision makers to any inconsistencies and gaps.
In monitoring potential regulatory violations, generative artificial intelligence can serve as a significant money-saving tool for financial institutions and their payment solutions.
Responsive customer service
We also see a lot of use cases for generative AI solutions that improve the quality of customer service for open finance users in relation to payments.
Multilingual models like ChatGPT are pioneering technologies in generative artificial intelligence and act as a seamless solution to optimize the customer experience across the industry.
LLMs can actively analyze customer queries, issues and pain points for contextual cues and leverage machine learning knowledge banks to offer targeted responses in a natural way.
These solutions can offer customers round-the-clock support or assistance in summarizing and translating international regulations and treaties. This can offer a significant level of internal and external assistance in accessing essential information without having to jump hurdles of understanding.
Crucially, for customers, generative AI chatbots can provide a more inclusive customer experience by tailoring responses to a conversational level that complements the user’s preferred lexicon.
Generative AI can also excel in breaking down international borders for companies that offer payment services in different countries. Here, users can ask questions and get answers in their preferred language, helping financial firms offer a positive CX without borders.
Innovation through code generation
Finally, generative AI will be able to drive innovation across the payments landscape for ambitious financial firms through 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 the future view of producing entirely new payment hardware and software at a much lower cost and more efficiently.
This implementation can complement existing developers who could use the time-saving tool as an opportunity to enhance their creativity and add more complex add-on features to support innovation.
Machine learning could then use its collaboration with developers to further refine its coding to make the final product even easier to implement.
With the help of generative AI coding, we could see a much faster level of digital transformation throughout the payment landscape, which has long struggled to overcome outdated processes. Additionally, time to market for these features could be significantly reduced.
Today, GPT-3 can act as a real-time assistant in identifying and removing errors within its coding. This can help streamline the research and development process for financial firms and accelerate the growth of payment technology across the industry.
Includes payment transformation
Generative AI is set to have a profound impact on the payments industry and the wider financial landscape as a whole.
From revolutionizing the way consumers pay for products to fraud protection and payment visibility at the enterprise level, AI may be the technology that a financial landscape in need of digital transformation is crying out for.
Businesses that embrace this transformative technology may be able to innovate ahead of the competition and offer a much more favorable customer experience that will ultimately be rewarded. Generative AI will change the future of finance, and decision makers should start preparing today.
About the author
Dmytro Spilka – managing director
Dmytro Spilka is the CEO of Solvid and the founder of Pridicto. His work has been published in Entrepreneur, Creative Bloq, Shopify, Zapier, Make Use Of, Mention, WordStream and Campaign Monitor.