AI innovations are now closely intertwined with most fintech operations. AI and Fintech share an element of innovation, meaning that Fintech and AI have a complex and multifaceted relationship that impacts various aspects of financial service delivery and drives efficiency for Fintech operators.
According to htfmarketintelligence.com, the artificial intelligence (AI) market size in banking is expected to reach USD 66.24 billion at a CAGR of 33.61% by 2030.
The accelerated growth in the adoption of AI applications by the Fintech sector is partly due to the falling costs of AI tools, rapid advancements in AI technology, and the growing demand for operational efficiency, cost-effectiveness, and data-driven decision-making. In addition, the Fintech sector is extremely competitive, and AI-first Fintech operators are using AI as a differentiator.
The history of AI applications in Fintech dates back to the 1980s and early 2000s, when the foundations of AI applications in Fintech innovations were laid, including expert systems to help automate credit scoring and loan underwriting, among other use cases.
Fast forward to the 1990s, when financial institutions began using machine learning and natural language processing (NLP) to automate critical functions like fraud detection and customer service.
The application of AI by the fintech sector accelerated in the early 2000s, with more fintech players leveraging AI for purposes including better risk management, improved product and service development, fraud detection and mitigation, and financial forecasting based on the increasing availability of big data.
Recent years have seen the emergence of AI-powered robo-advisors, chatbots and virtual assistants for real-time credit assessment, as well as AI in cybersecurity and fraud detection in digital payments. More recently, generative AI has begun to influence the fintech sectors, developing predictive models and offering personalized financial products.
The ability of AI to improve a variety of Fintech tasks, operations, processes, and systems is due to the application of a variety of technological innovations, such as machine learning (ML), natural language processing (NLP), extended language model (LLM), robotic process automation (RPA), and predictive analytics.
These tools improve operational efficiency, automation and complex decision-making processes in Fintech and impact tasks such as customer onboarding, risk assessment and data analysis and streamline workflow forecasting and customer-facing processes.
Fintech is increasingly showing evidence of AI applications, enabling services such as algorithmic trading, loan underwriting, automation of accounting processes, peer-to-peer (P2P) lending, crowdfunding, regulatory technology (REGTECH), digital and mobile payments, digital asset management, and the delivery of personalized services to customers.
AI is now essential to data management and analysis, enabling fintech companies to generate insights on actions to improve digital payment processing. Overall, the benefits of AI in fintech could include improved customer experience, enhanced fraud detection capabilities, increased efficiency, the ability to make complex data-driven decisions, cost savings, and improved profitability.
Despite Fintech’s growing pains, the race is on; some of the leading Fintechs are moving beyond experimentation and rapidly moving to extend AI across their operations with a view to disrupting existing traditional financial systems and introducing cutting-edge Fintech solutions that meet complex consumer demands.
Given the boom in AI applications in the financial sector, several central banks around the world have started introducing regulations, guidelines, and directives to govern the use of artificial intelligence (AI) in the financial system.
For example, the European Central Bank (ECB) and the European Union have proposed that the AI Act include provisions on finance. In addition, the European Central Bank (ECB) provides specific guidance on the use of AI in financial institutions under its supervision, in particular on algorithmic transparency and fairness of AI applications in credit assessments and transactions.
The Monetary Authority of Singapore (MAS) has introduced the principles of fairness, ethics, accountability and transparency (FEAT) in AI applications. Other central banks, such as Canada’s Office of the Superintendent of Financial Institutions (OSFI), the Bank of England, the US Federal Reserve, the People’s Bank of China and the Hong Kong Monetary Authority have similar guidance or directives on the use of AI in banking.
In the future, AI in Fintech will be characterized by advances in natural language processing, increasing reliance on blockchain technologies, emphasis on deep learning, voice-enabled financial services, increased automation of services, improved predictive analytics powered by big data, AI-driven personalization, AI-driven financial inclusion, and enhanced collaboration based on seamless interoperability of systems. Citi (https://www.citigroup.com/global/insights/ai-in-finance) estimates that AI could replace 54% of banking jobs and impact financial markets, insurance, and energy sectors.
In conclusion, a myriad of applications of AI in Fintech are now fully established and the future trend indicates a complex relationship between AI and Fintech that will lead to unprecedented innovations leading to revolutionary applications that enhance the consumer experience, help balance risks and improve Fintech outcomes while transforming our society.
It is worth noting that while AI adoption can come with many opportunities and benefits, it is imperative for Fintech companies to consider serious privacy, security, and ethical implications when reengineering their operations and systems to incorporate AI.
Kwami Ahiabenu, II (Ph.D.) is a consultant in technological innovations. Email: kwami@mangokope.com
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