Smart Money: How AI Facilitates Cross-Border Payroll Payments

The current AI discourse is dominated by utopian and dystopian prophecies. AI will either save humanity – or destroy it; it’s either an angel – or a demon. This dichotomy has its roots in technological determinism, a theory that suggests that technology is the driving force for social and cultural change.

The alternative approach is called the social construction of technology (SCOT), a theory within the Science and Technology Studies field. Advocates of SCOT argue that human action shapes technology – not the other way around – and believe that technological development cannot be understood without social context.

SCOT, in other words, views users as technological change agents. According to the theory, multiple groups of users – as the stakeholders who adopt and apply new technologies – define the trajectory of each technology’s development. That’s certainly the case with stakeholders in the business world, who realized early on that AI could be used to support their product goals.

Supply and demand

Funding to companies using AI has increased considerably over the past few years. In 2013, companies using AI raised $3 billion in less than 1,000 deals. In 2021 that sum reached $69 billion across more than 4,000 rounds. Demand for AI among businesses has also grown steadily; 35% of the 7,500+ companies surveyed in IBM’s latest Global AI Adoption Index utilize AI – an increase of 4 points from 2021 – with 42% reporting that they are exploring the use of AI.

Using AI is particularly prevalent in the finance sector. In the UK, for example, a survey conducted by the Bank of England and the Financial Conduct Authority (FCA) revealed that 72% of financial services companies use machine learning (ML), a subset of AI. The widespread adoption of AI in UK financial services led the Bank of England to publish a discussion paper, which examines whether AI can be managed through the existing regulatory framework, or whether a new approach is needed.

While regulators debate which legal requirements and guidance best apply to the new technology, the implementation of AI use cases in the FinTech industry continues to accelerate. Some of the main use cases are services related to cross-border payments. According to NVIDIA’s latest “State of AI in Financial Services” report, 22% of global companies already employ AI for Know Your Customer (KYC), Anti Money Laundering (AML), and other fraud detection processes.

All aboard the AI train

Financial institutions are exposed to various types of fraud. 67% of the organizations that participated in LexisNexis’s 2022 “True Cost of Financial Crime Compliance Study” admitted they are exposed to crimes involving digital payments, and over 60% to different forms of money laundering. The estimated cost of financial crimes across financial institutions worldwide is $274.1 billion – up from $213.9 billion in 2020.

AI can mitigate this risk. Any function with high volume, high frequency, and relatively structured data – such as recurrent payment streams – can benefit from AI’s ability to identify suspicious patterns and anomalies in data. As a result, Juniper Research forecasts that global spending on AI-based financial fraud detection and prevention methods will reach $10 billion by 2027.

Papaya Global, the only PayTech designed for cross-border payroll payments, was one of the first FinTech companies to jump on the AI train. Papaya’s platform leverages AI to perform KYC and AML processes for each new client and each worker being paid via our system. In addition, we run rigorous screenings for every payment, every cycle, to keep employees’ data and clients’ funds safe.

Invisible to the human eye

Another risk to clients’ funds is the foreign exchange market. In a corporate treasury risk management survey conducted by HSBC, 57% of CFOs (rising to 77% in EMEA) reported a drop in earnings due to unhedged FX risk. The past couple of years, characterized by geopolitical tension and rising inflation, added volatility to the global FX markets. It should come as no surprise, then, that according to Deloitte’s 2022 Global Corporate Treasury Survey, FX volatility remains a top 5 challenge for organizations.

Thankfully, AI-driven tools are rapidly transforming FX risk management. The FX market produces large amounts of data, such as price changes, economic indicators, and news events’ impact. AI and ML algorithms can analyze this data, spot patterns, trends, and correlations invisible to the human eye, and accurately predict FX rates.

That’s precisely how Papaya’s transfer processing algorithm works. And since our PayTech relies on payroll-dedicated rails that ensure speedy delivery, payments are made at the last possible moment, giving the transfer processing algorithm maximum time to generate a prediction that reduces currency conversion costs to a minimum.

If these are the early benefits of AI-driven payroll payments, we’re doing a pretty good job of defining its trajectory. Schedule a demo to learn more.