Integrating Risk Management in Fintech and Traditional Financial Institutions through AI and Machine Learning
Bibitayo Ebunlomo Abikoye
*
Cornell University, SC Johnson Business School, Ithaca, NY, USA.
Wunmi Adelusi
Banking Supervision Department, Central Bank of Nigeria, Abuja, Nigeria.
Stanley Chidozie Umeorah
University of Michigan, Stephen M. Ross School of Business, Ann Arbor, MI, USA.
Adesola Oluwatosin Adelaja
University of Virginia Darden School of Business, Charlottesville, VA, USA.
Cedrick Agorbia-Atta
Indiana University, Kelley School of Business, Bloomington, IN, USA.
*Author to whom correspondence should be addressed.
Abstract
The rapid evolution of financial technology (fintech) has significantly transformed the financial services landscape, creating opportunities for innovation and introducing new risks. Traditional financial institutions and fintech companies operate under different paradigms, resulting in disparate risk management practices. This paper proposes a comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML), the framework aims to ensure consistent and effective risk assessment across the financial sector. The financial services industry is characterized by rapid innovation, primarily driven by fintech companies offering various services that enhance efficiency, accessibility, and customer satisfaction. However, the growth of fintech brings substantial risks, including cyber threats, data privacy concerns, regulatory compliance challenges, and operational vulnerabilities. Traditional financial institutions prioritize stability, security, and compliance within established risk management frameworks. The divergence in operational models and risk management approaches creates a fragmented risk landscape, posing significant challenges to the financial system's stability and security. This paper identifies the critical need for a unified framework integrating the risk management practices of traditional financial institutions and fintech companies. The proposed framework leverages AI and ML to enhance the accuracy and comprehensiveness of risk assessments, utilizing a centralized data repository for real-time risk assessment. Unified risk management policies covering cybersecurity, operational risk, regulatory compliance, financial crime, and real-time monitoring and reporting tools ensure robust risk management protocols and prompt response to potential risks. Aligning with regulatory requirements and incorporating best practices from both sectors, the integrated risk management approach enhances the financial ecosystem's stability, security, and public confidence.
Keywords: Integrated risk management, AI and ML adoption, traditional financial institutions, fintech, regulatory compliance, technology infrastructure