Here is the rewritten article in Markdown format:
Financial Crime Data Analytics in Argentina: The Need for Change
Fighting financial crime has never been more challenging, with increasingly rigorous compliance requirements and growing levels of data. According to a 2018 Refinitiv Survey, global financial crime activity costs an estimated US$1.3 trillion annually. In the last decade, global regulators have imposed over US$26 billion in fines for non-compliance with Anti-Money Laundering (AML), Know Your Customer (KYC) and Sanctions regulations.
Governments and regulators are putting financial services firms on the front line in the fight against financial crime, with increasingly rigorous compliance requirements. Trade institutions are finding it particularly challenging to meet these heightened expectations due to manual processes and legacy technologies that no longer keep pace with the huge volumes of data being produced and the complexity of the global banking environment.
From Manual Processes to Machine Learning
Traditionally, financial institutions have relied heavily on manual, human intervention in regulatory reporting. However, with the enormous amounts of data flowing in and out of banking systems, it’s impossible for humans to keep pace with demand. Advanced data and analytics techniques such as artificial intelligence, machine learning, natural language processing and cognitive automation can be used to accelerate or automate a significant portion of the labor-intensive work.
Examples of Innovative Solutions
Argentina’s financial institutions can benefit from using advanced data & analytics techniques and technologies to improve regulatory compliance, enhance customer experience and lower the cost of operational risk management. Here are three examples:
Transaction Monitoring
- Machine learning models can enrich transaction monitoring alerts and boost Suspicious Matter Report (SMR) conversion rates by predicting AML scenarios before they occur.
Know Your Customer (KYC)
- Augmenting human activity with machine learning techniques can achieve a more holistic view of the customer, enhance data used for due diligence, and provide a more contextual basis for determining customer risk and detecting suspicious activity.
Sanctions Screening
- Emerging AI and analytical methods can be used to address operational efficiency issues related to case investigation by filtering out false positives and improving inefficiencies in existing investigative processes.
Intelligence-Led and Data-Driven Approach
It’s evident that Argentina’s financial service organizations are being challenged in keeping up with the onerous demands of mitigating financial crime risks. To align operational effectiveness with these demands, organizations must seek innovative ways to address issues surrounding SMR conversion rates, KYC due diligence and screening alert management.
Complete and accurate data is essential to resolving these issues, and an uplift of data quality will have immediate effects on the performance of existing monitoring and screening engines. Advanced analytics and cognitive techniques can help filter out false positives and improve inefficiencies in existing investigative processes, providing opportunities for data and analytics to drive efficiencies and operational cost reductions.
Conclusion
================
Argentina’s financial institutions must adopt a more intelligence-led and data-driven approach to fighting financial crime. By leveraging advanced data and analytics techniques, they can not only drive efficiencies and operational cost reductions but also identify innovative ways to tackle financial crime. When implemented correctly, these solutions can help accelerate results and improve the overall effectiveness of financial crime prevention efforts in Argentina.