The True Cost of AI: A Double-Edged Sword for the Payments Industry?

Artificial intelligence (AI) is widely regarded as a transformative force in the financial and payments industry. From real-time fraud detection to large-scale automation, AI promises to drive significant efficiency and innovation. However, behind this optimistic narrative lies a more complex reality. The cost of adopting AI may exceed expectations, especially for small and medium-sized enterprises (SMEs) that lack the scale and resources of major financial institutions.

Understanding SMEs in the Payments Industry

Within the payments sector, SMEs typically include fintech start-ups, regional payment processors, independent merchant service providers, and niche financial platforms. These organisations often operate with lean teams and limited budgets, serving specialised market segments such as cross-border remittances, small business payments, or domestic e-commerce. They play a vital role in promoting financial inclusion and driving innovation.

Despite their importance, SMEs often face structural challenges. Limited access to capital, advanced infrastructure, and in-house technical expertise can severely constrain their ability to adopt and operationalise AI-based solutions, particularly in areas such as fraud prevention, anti-money laundering (AML), and transaction monitoring.

Infrastructure, Talent & the Hidden Costs of AI

The implementation of AI is far from 'plug-and-play'. Successful implementation demands investment in scalable infrastructure, robust data architecture, cyber resilience, and highly skilled personnel. The global demand for data scientists and machine learning engineers continues to exceed supply, creating intense competition and escalating costs. Many SMEs find themselves unable to compete for this specialised talent.

For instance, developing an AI-driven fraud detection platform involves more than just deploying algorithms. It requires integrating real-time and historical transaction data, building secure and efficient data pipelines, and continuously refining the model to maintain accuracy. These processes depend on a sophisticated and often costly workforce.

Large financial institutions such as JPMorgan Chase and HSBC are able to commit substantial budgets to AI research and development, often investing millions annually. In contrast, SMEs may find these requirements financially unviable. As a result, they tend to rely on third-party solutions or partnerships, which may offer limited adaptability or innovation.

The Consortium Model as a Strategic Alternative

One promising approach for SMEs is the consortium model. This involves collaboration among smaller organisations to share access to AI platforms, pooled data sets, and regulatory tools. By working together, SMEs can reduce individual investment burdens, accelerate innovation, and enhance regulatory compliance.

In Europe, several fintechs have joined forces under regulatory sandboxes initiatives to trial AI applications in controlled environments. These collaborative models support knowledge sharing, foster a culture of responsible innovation, and improve negotiating power when dealing with vendors or synthetic data providers.

Examples such as the Nordic Financial CERT and the Financial Data & Technology Association (FDATA) in the UK highlight the potential of shared intelligence and secure data exchange, particularly for fraud detection and compliance enhancement.

Synthetic Data: A Workaround with Caveats

Synthetic data, which is artificially generated to replicate the patterns of real-world transactions, is increasingly used to address privacy concerns and data availability issues. It allows SMEs to train and test AI models for fraud detection, customer risk scoring, and KYC procedures without exposing actual customer data.

Although synthetic data presents clear advantages, its value depends on how closely it resembles real-life behaviour. If the data does not capture relevant variables such as regional nuances or seasonal patterns, models trained on it may underperform. A retail bank in 2023 found that its fraud detection system performed poorly in production after being trained solely on synthetic data that failed to reflect important transaction spikes and customer behaviours.

Moreover, synthetic data built on outdated or biased source material can replicate those flaws. Many financial institutions still operate on legacy systems that store incomplete, unstructured, or low-quality data. When such data is used to generate synthetic versions, it can perpetuate errors and bias within AI models, undermining both accuracy and trust.

Ethical Concerns & Public Perception

As AI becomes more integrated into financial decision-making, public concern is growing around its ethical implications. Issues such as algorithmic bias, surveillance, and data misuse have led to increased scrutiny from both regulators and consumers. Notable incidents, including credit scoring algorithms that disadvantaged minority groups, have raised significant questions around fairness and accountability.

Financial institutions must now demonstrate ethical stewardship in addition to technical competence. Transparency, explainability, and fairness are becoming essential criteria for AI deployment. Regulatory frameworks in jurisdictions such as the European Union and Singapore now require institutions to document model decision-making processes, conduct regular bias assessments, and ensure human oversight remains a core part of operations.

Despite automation, human oversight remains essential. AI should augment and not replace human judgment. Analysts, investigators, and compliance officers provide critical context and interpretive insight that AI systems alone cannot replicate. Their role in validating alerts and ensuring appropriate responses safeguards the integrity of financial systems.

Cross-Functional Collaboration & Regulatory Partnerships

Successfully integrating AI into compliance and financial crime frameworks requires more than technology investment. It demands collaboration between compliance professionals, data scientists, and IT teams. This cross-functional approach ensures that models are operationally viable, technically robust, and aligned with governance standards.

Early engagement with regulators helps build trust and allows institutions to shape AI frameworks that are both practical and enforceable. Innovation hubs and sandbox environments provide valuable opportunities for dialogue and experimentation, supporting more informed and balanced regulation.

Industry reports increasingly highlight the importance of embedding governance into AI projects from the outset. Institutions that do so are more likely to implement systems that are scalable, sustainable, and resilient in the face of regulatory scrutiny.

Conclusion: A Measured Approach to AI Adoption

AI holds enormous promise for the payments industry, but its adoption is not uniform across the sector. For SMEs, the challenges extend beyond financial investment to include data quality, technical capability, ethical responsibility, and regulatory compliance.

Collaborative models such as consortia and synthetic data initiatives offer a pathway to responsible and inclusive AI adoption. However, success requires a measured and transparent approach that prioritises trust, fairness, and human oversight.

SMEs that can strike the right balance between innovation and accountability will be best positioned to thrive in the next phase of digital financial services. By embracing cross-sector collaboration and regulatory engagement, they can help build a more secure, inclusive, and future-ready financial ecosystem.