Artificial Intelligence (AI) has shifted from a peripheral experiment to a central, structural component of the global financial ecosystem Currently, 81% of financial services firms have already adopted AI at some level1. While 51% of firms in advanced economies have optimized AI integration, emerging markets are also showing significant progress, with 32% of institutions in these regions already scaling or transforming their workflows. AI provides players in emerging markets with critical tools for overcoming obstacles such as high costs and a lack of traditional credit histories — making it an invaluable asset for up-and-comers in the industry

What is AI?

In broad terms, AI is a machine-based system that infers how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments from the input it receives. Different AI systems vary in their levels of autonomy and adaptiveness .These systems display intelligent behavior, as they are capable of analyzing their environments and taking autonomous actions to achieve specific, pre-specified outcomes.

The FinTech Arsenal: AI Tools and Technologies

Recent advancements have enabled this sector to utilize a diverse range of AI tools to redefine service delivery. These tools include:

  • Machine Learning (ML): Systems capable of learning statistical patterns from historical data to reliably reproduce outcomes without the need to engineer features manually
  • Natural Language Processing (NLP): Technology that enables machines to understand, interpret, and generate human language. It is often used for voice and chatbot interfaces
  • Robotic Process Automation (RPA): Software “robots” that mimic human actions to automate repetitive, rule-based tasks like data entry and account reconciliation
  • Predictive Analytics: Analytics utilizing AI to analyze data and forecast future outcomes, such as stock prices or loan default rates.
  • Agentic AI: An emerging frontier of autonomous systems capable of planning and executing multi-step workflows independently. These systems are currently being adopted or piloted by 52% of industry respondents.
  • Generative AI (GenAI): A subset of AI models that utilize the data they were trained on to create new, “original” content in response to user prompts. GenAI provides sophisticated tools for fraud detection and prevention

AI is consistently redefining the foundations of modern-day FinTech, with notable applications in loan underwriting, fraud detection, and risk assessments6. This provides an unprecedented level of personalization of financial services for consumers. The expansion of mobile payments in Africa, which FinTech firms have built on AI tools such as ML and predictive analytics, has even helped to establish alternative credit scoring models, extending credit to previously unbanked and underserved consumers.

The velocity at which AI tools automate repetitive tasks and analyze large datasets — far faster than humanly possible — has contributed to improving operational efficiency, compliance checks, and fraud detection, thus improving the security of financial systems and reducing costs and potential fraud losses. AI tools have also facilitated a new era of investment portfolio management, with algorithmic trading and real time engagement with consumers via AI powered chatbots and virtual assistants. Agentic AI is taking customer engagement to the next level, offering a variety of self-service choices for consumers and businesses, particularly around ecommerce and investment trading.

Overall, growth in AI capabilities is contributing to more efficient, secure, and accessible financial services.

A Key Arena for Innovation and Competition

The FinTech industry is the primary launchpad for AI-driven innovation, with firms consistently outpacing traditional banks in advanced AI adoption and implementation. A staggering 87% of FinTech firms are now piloting and even scaling AI. The transition is much slower for traditional financial institutions, which tend to be hampered by legacy complexity. This competitive pressure is driving global spending on AI in banking toward a projected USD 97 billion by 2027.

Diverse Applications in Action

Africa has become a world leader in leveraging AI to bridge the financial inclusion gap through innovative applications, including:

  • Agricultural Credit Scoring: Kenya-based agricultural data analytics company, FarmDrive, uses ML and personal mobile phone data to build alternative credit profiles for underserved smallholder farmers, allowing them to access growth capital.
  • Reducing Default Rates: The African FinTech company, MyBucks, utilizes its AI platform, “Jessie,” to generate lending profiles from personal mobile phone data, reducing its default rate in South Africa by 18% in a single financial year.
  • Automated Customer Engagement: MTN in Côte d’Ivoire integrated AI-supported digital dialogues into its MoMo wallet, successfully automating 95% of its digital conversations, thus improving financial literacy and product understanding.
  • Climate Risk and Insurance: WorldCover uses AI to analyze satellite and weather data to trigger automatic insurance payouts via mobile money platforms like M-Pesa, protecting farmers from climate-related financial shocks.
  • Open-Weight Model Adoption: Sub-Saharan Africa shows the highest regional adoption rate (21%) for cost-accessible, open-weight models like DeepSeek, allowing firms to build sophisticated AI tools with lower investment barriers.

AI Challenges in the Financial Sector

AI has not only exponentially increased service choices for consumers, but also enabled efficiency gains in front and back-office operations. These opportunities are, however, not without challenges. Access to AI tools and technology as well as AI adoption remain unequal across the globe. This inequality only deepens the existing digital divide between urban and rural areas, and high and low-income populations. Depending on the sources of data and the inherent biases in the training data used, AI algorithms and predictive models can reinforce inequality and misuse of personal information, raising data privacy and security concerns.

In the era of instant payment systems, incidences of fraud, scams, and platform hacking remain on a high trajectory fueled by sophisticated AI tools enabling deep fakes and synthetic identity fraud. AI enabled scams are outpacing traditional fraud as demonstrated by the exponential increase of Approved Push Payment (APP) fraud in the UK, reaching £485 million in 2022.

Regulatory and Industry Response

The risks faced during AI usage in the financial sector are new, but tend to be exacerbated by data related risks (privacy and security), consumer protection risks (biases and fairness) and prudential risks (credit and operations). Due to this novelty, very few jurisdictions, besides the European Union (EU), have issued any AI regulations. Signed in June 2024, the European Union Artificial Intelligence Act is one of the first AI regulatory frameworks in the world. The EU AI Act takes a risk-based approach to assessing AI systems, ranking risks across four tiers: minimum, limited, high, and unacceptable. AI systems presenting unacceptable risks can be banned, while strict compliance and transparency requirements can be imposed on high and limited risk systems. Penalties under the Act can be as high as €35 million or 7% of global turnover of the penalized AI provider.

Overall, the industry response has focused on issuing cross-sectorial AI policy and guidance by national and international authorities, and standard-setting bodies alike, emphasizing transparency, explainability, governance, accountability, and reliability of the AI tools and technologies used by financial institutions.

While the benefits of AI in global finance are well-recognized, regulators across the board stress the need for increased governance, investment in data science and AI expertise, and oversight of third parties to balance innovation with consumer protection. For instance, the UK’s Payment Systems Regulator (PSR) has mandated reimbursement for APP fraud victims starting October 2024, shifting liability to banks and payment providers. Increasingly, AI governance and ethical frameworks have also emerged. This includes the ASEAN Guide on AI Governance and Ethics, which focusses on four key areas: internal governance measures, human involvement in AI augmented decision-making, operations management, and stakeholder interaction and communication.

Conclusion

AI innovations are a double-edged sword for financial ecosystems. While benefits in terms of operational efficiency, affordability, safety and personalization of financial services are increasing real time engagements with consumers, the same AI tools and technologies can also threaten platform security and increase fraud sophistication levels that can significantly affect consumers’ trust in the financial ecosystem. As such it is critical to ensure ethical use of AI across the board, balancing human and AI-driven decision-making, and continuously strengthening labor skills and expertise around AI tools and technology.