However, the adoption of AI and ML in banking is not without challenges. Larger banks often struggle to integrate new software platforms with their existing systems. The article suggests that these banks could benefit from partnering with disruptive fintech companies. The author concludes by emphasizing the importance of operational resilience and the need for financial institutions to modernize their compliance tools.
Key takeaways:
- Artificial intelligence (AI) and machine learning (ML) are crucial in combating financial crime, helping banks to build operational resilience, prevent regulatory fines, and increase profit margins.
- AI technology is essential for gathering 'financial intelligence' and recognizing changing patterns and evolving trends in financial crime. AI-powered anti-money laundering (AML) transaction monitoring (TM) tools can spot trends and modify algorithms and policies in real time.
- AI/ML can be deployed to fortify anti-financial crime compliance throughout the client lifecycle, including verifying customer identity through KYC, automating and streamlining client onboarding processes, and automating the analysis of large amounts of data in the ongoing monitoring of customers, regulatory changes and transactions.
- Despite the benefits, larger banks face challenges integrating static legacy systems with new software platforms. However, partnerships with disruptive fintechs are increasing, and 80% of financial crime and compliance decision-makers are looking to increase their operational resilience.