Risk vs Reward: AI development for payments

February 12th 2024
Mark Westbrook
Global Solutioning Director (Payments / Banking)

An increasingly digitized world means that the way payments are made has become increasingly digitized, too. Digital payments are delivering new levels of convenience and assurance for billions of people all over the world, whether it’s speed of transaction, financial security, adaptation to new consumer trends, or access to personalized finance services.

Artificial intelligence, which is already transforming just about every sector you can think of, has the ability to take all four of those vital customer priorities to the next level. Firms in the finance sector are already using AI to good effect in back-end services: Visa has found that 70% of financial services firms worldwide are already using machine learning for credit score analysis, fraud detection and cash flow prediction. However, ML and AI can make a real difference in the world of payments, too.

The most forward-thinking of finance companies are already actively exploring artificial intelligence payments, and how it can help them deliver more efficient, customer-friendly and cost-effective services. So if you haven’t done the same already, now is the time to get on board and stay on the pace of change. But while the rewards of AI in finance can be great, there are also plenty of risks for your organization to steer clear of along the way. This blog explores that vital balance between risks and rewards.

The evolution of payment methods

If you take a step back, it’s incredible to think how the way we all pay for goods and services has changed in the first quarter of the 21st century. 

Travel back in time to the turn of the millennium, and cash transactions were still commonplace in most developed economies, and while debit and credit card payments were gaining traction, they were still done by swiping the magnetic strip. And internet-based retail (what we would now know as eCommerce) was still a very small part of our buying lives back then, so the idea of paying for things online was still alien to many of us.

But now, in 2024, the payments landscape has been transformed virtually beyond recognition. Cash now generally makes up only a minority of overall transactions, replaced by contactless use of debit and credit cards (which itself superseded ‘chip and PIN)’, and digital wallets that connect bank accounts to smartphones and smart watches. Buying online using credit and debit card details is now a standard everyday occurrence, one that has been joined by digital-native methods of payment like cryptocurrencies. These, in turn, may well be joined by state-backed ‘central bank digital currencies’ (CBDCs) over the next few years.

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What is AI in payments?

AI in payments refers to the intelligent technologies that improve the processing, security and management of digital transactions. This includes the use of machine learning, natural language processing, predictive analytics and other artificial intelligence technologies to support automation, and therefore greater efficiency and accuracy.

What are the risks in adopting AI for payments?

While AI in payments is full of potential, like any new technology adoption, it doesn’t come without its fair share of risks and challenges. The good news is that none of them are insurmountable, but if you’re exploring an AI-based payments implementation, then you should definitely watch out for the following:

Icon1_Data and privacy Data and privacy

AI requires access to large volumes of data to work effectively: the more data that an AI tool is fed, the more it can learn, and the more relevant and accurate the insights and services it can deliver. Inaccurate or incomplete data can lead to flawed predictions and decisions, so ensuring data quality is a critical aspect of implementing AI in payments.

But in a finance and payments deployment, that means using highly sensitive information, such as customer’s bank details and transaction data, or their personal info. If these are seized at high volumes, the consequences - for them, and for the business involved from a compliance perspective - will be catastrophic. It’s for this reason that the financial industry is highly regulated, and adapting AI systems to comply with these regulations can be challenging; non-compliance may result in legal consequences and reputational damage.

Icon2_Fraud and security Fraud and security

Just as you can use AI as a force for good within your own organization, cyber-criminals are increasingly utilizing AI as a force for evil. It’s never been easier for them to generate spam emails, phishing attacks or spoof websites at an incredibly large scale, and target huge numbers of people with scams that can seize their personal data and/or financial information. So as well as keeping data safe, you’ll also have to work hard to demonstrate to customers that your technology is secure and authentic.

Icon3_poor operative training Poor operative training

AI is a helpful tool in supporting human payments teams, but should not be considered an outright replacement for them. AI still needs human oversight to ensure that it’s working correctly and ethically, and the humans tasked with looking after the solutions need to know what they’re looking at. 

It’s vital that payment teams are well-trained in what AI can do, what it should do - and perhaps more importantly, what it shouldn’t do. This is where many AI projects are falling short: according to EY 55% of decision-makers say insufficient internal expertise is a barrier to establishing a dedicated team for generative AI.

Icon4_over-reliance on machines Over-reliance on machines

Putting too much emphasis on AI is the technological equivalent of putting all your eggs in one basket. If something goes wrong that is unexpected, or the deployment of an AI solution is flawed from the start, then the disruption to business can be enormous. It’s for this reason, as well as the oversight and training mentioned above, that humans and AI machines should work together, rather than one superseding the other.

Icon5_Bias and legal consequences Bias and legal consequences

AI algorithms have been known to inherit biases that are present in the training data. This can often lead to discriminatory outcomes, particularly in financial transactions, that disproportionately impact certain demographics. Given the social and legal responsibility on all businesses to ensure that everyone is treated fairly and equally, these biases can   raise ethical concerns and potentially lead to legal challenges.

Icon6_Customer adoption Customer adoption

AI is still treated with caution by many customers, especially in applications that involve sensitive personal and financial data. This is where extensive research and user testing is vital, so that users can gain trust in the current AI tools they’re presented with, and feel more comfortable with those that will follow in the future.

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The rewards of an AI future

If you can steer your way past the challenges above, then you’re in pole position to use AI to maximum effect, and take the payment services you offer to the next level. Potential areas for transformation include:

Icon1_reduced-human-error Reduced human error through automation

One of the core strengths of AI is its ability to repeat the same process over and over again, extremely quickly and without making any mistakes. For certain workloads that are repetitive, this is a far better option than relying on humans, who are much slower and are more prone to making mistakes when they get bored, tired or complacent. Given that even one bad experience caused by an error can cause huge stress and negativity for a customer, cutting out these mistakes can make a real difference to customer loyalty and satisfaction.

Icon2_Efficiency Efficiency

AI and automation can make payment processes much quicker, not only for customers, but at the back-end, too. Customers get their transactions completed far faster than before, which adds to their experience, while AI’s ability to take care of repetitive tasks (mentioned above) can lighten some of the operational load on development teams that can hinder progress.

Icon3_UX improvements UX improvements

Tools like generative AI can be used to personalize customer experiences of payments to a level that human teams simply wouldn’t have the time or the resources to achieve. The result is that customers can be offered payment experiences that are more in line with their individual preferences, demonstrating that their payment provider cares about their business. They can also get faster responses to complex queries, and complete authentication and verification faster, thanks to AI tools and machine learning-driven chatbots.

Icon4_Competitive advantage Competitive advantage

The payments and FinTech market is extremely competitive, and is evolving all the time. Barely a month goes by without new innovative services and solutions getting to market, and it’s often those that get there first that grab the biggest market share. AI gives ambitious start-ups and established players alike the opportunity to innovate with new capabilities, and to do so far faster than would otherwise be possible. In particular, AI can play a major role in generating large amounts of accurate code in a very short space of time, enabling FinTechs in particular to speed up their IT development and get new products to market faster.

Icon5_Improved monitoring Improved monitoring

When it comes to security and anomalous patterns of behavior, AI can spot suspicious activity very early. This can minimize the potential disruption of a security breach to a business, either through automatically taking care of a threat, or if appropriate, alerting IT security teams to take further action.

Icon6_Compliance Easier compliance

Complying with financial regulations like KYC and AML are vital for all financial businesses, but doing so can be very expensive and time-consuming. AI tools and machine learning can assume many of the key processes, reducing the time, cost and risk involved in compliance-related work.

In summary: exploring the potential of AI in payments

One of the most important things to remember about artificial intelligence is that the technology is still in its infancy, relatively speaking. The pace of progress in AI is so quick that new capabilities are coming on stream all the time, which means there are always new opportunities to explore. But being able to do so, and to do so before your competitors, requires access to the latest and most extensive AI expertise.

When you partner with Ciklum for your AI in payments development, you’re working with a business that has proven success with AI deployments across a wide range of sectors. Whether you’re most interested in exploring large language models, fraud detection, predictive maintenance or anything else that’s AI-related, we have the people and the solutions that can get your organization where you want it to be. We can help you accelerate eCommerce enablement, innovate with open banking, build reliable and trusted financial products, and automate critical processes for maximum efficiency.

We also understand that cost implications can play a big part in any AI deployment, and that concerns about spiraling expenditure can be a real barrier to AI adoption. That’s why we always keep you in the loop with clarity and transparency, thanks to an end-to-end solution across discovery, strategy, proof of concept, integration, implementation and maturity.

To find out more, take a closer look at our unique approach to artificial intelligence, or get in touch with the team to discuss your specific needs and priorities.


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