Revolutionising risk management: The impact of AI and ML in the financial services industry

Computing advancements and data aggregation have led to Industry 4.0, which changes the way we think, interact, and work. Technology affects every industry, but the banking sector is especially affected.

A wave of financial technologies, or fintech, has emerged to revolutionize money management. With big data and improving data processing capabilities, fintech companies are creating more efficient, accessible, and secure banking systems. The size of the fintech industry was valued at $110.57 billion in 2020 and is projected to reach $698.48 billion by 2030.

In the last decade, artificial intelligence (AI), machine learning (ML), and high-performance computing have played a pivotal role in financial industry innovation. The process of making decisions is becoming more efficient and effective as financial institutions enhance processes for better customer experiences and for fraud and financial crime prevention.

AI algorithms can detect suspicious patterns, anomalies, and potential risks by analysing large volumes of transactional data. As more data is supplied to these algorithms, they can adapt and improve. They can identify and mitigate emerging risks, reduce losses, and prevent poor customer experiences by staying one step ahead of criminals. Increasing automation also reduces costs, improves accuracy, and streamlines processes for financial institutions. Finally, ML models can help financial institutions leverage their data to protect their customers.

A promising and already proven effective initiative that could benefit the entire ecosystem in the UAE is federated machine learning. By enabling multiple financial institutions to collaborate without sharing sensitive customer data, this method uncovers and mitigates risk. Through collective ML training driven by shared insights, financial institutions can discover common risk factors, identify emerging trends, and improve risk management strategies. In this collaborative approach, risk is assessed and mitigated more accurately. As such, this collaborative model is an effective and secure way to manage risk.

AI has the potential to combat financial crime, yet the traditional financial services industry has been slow to adopt it. There are several relevant challenges contributing to this limited uptake. Data privacy and security concerns, stringent regulatory frameworks, and AI ethics concerns are among these challenges. Assimilation of cutting-edge technologies is challenged by organizations’ digital culture not evolving, digital immobility, or staff resistance.

Technology providers, regulators, and financial institutions must collaborate to overcome these barriers. The financial industry will use AI more efficiently if industry-wide standards and regulatory frameworks balance innovation and risk management. Moreover, financial institutions can invest in data quality improvement initiatives, partner with technology companies, and train their workforce.

AI and ML have the potential to transform risk management strategies in the financial services industry. Through Federated Machine Learning, transaction monitoring and risk discovery are enhanced. While challenges and barriers persist, proactive collaboration and investment in AI adoption will make the industry more resilient and efficient. Therefore, the traditional financial services industry must leverage AI and ML to gain a competitive edge and remain profitable.

The writer is managing director at K2 Integrity, Abu Dhabi

• Business