Artificial intelligence (AI) is arguably the hottest topic in the financial industry today. Financial institutions are using AI to help mitigate costs, generate business, and improve customer experiences. However, there are two types of AI that are used specifically for the benefit of financial crime management:
- Machine Learning: the use of algorithms to make effective decisions, most often used to improve transaction monitoring by reducing false positives
- Robotic Process Automation (RPA): the high-speed automation of manual tasks, most often used to reduce administrative work and free up investigator time
With the majority of financial institutions already investing in AI approaches, your institution should carefully consider how these advanced solutions can best benefit your compliance and financial crime mitigation programs.
Machine learning: reducing alerts
Dealing with an increasing volume of false positives is a significant challenge for financial crime investigators. Machine learning can play a critical role in your alert reduction strategy, resulting in increased investigator efficiency.
When deployed on a large set of quality, labeled data to train models, machine learning algorithms significantly improve the analytical performance. Though most often leveraged for its benefits in fraud detection, with the right data set, machine learning has the potential to reduce false positives in many areas of financial crime management. By reducing the time spent reviewing alerts, machine learning improves investigators’ effectiveness, significantly reducing administrative efforts and allowing them to focus their time on truly suspicious activity.
RPA: automating tasks
Both fraud and BSA/AML departments can improve efficiencies by leveraging RPA for data collection and workflow management.
By automating the tedious, manual tasks associated with data collection, such as customer onboarding processes, institutions can save significant time for both their staff and their customers. Likewise, by automating regulatory report completion and filing, such as for Suspicious Activity Reports (SARs) and Currency Transaction Reports (CTRs), RPA can save valuable time and help investigators focus on their decision-making process.
Considerations for financial institutions
As your institution evaluates AI, machine learning, and RPA for your financial crime management program, you should consider:
- The needs of your institution: Start with a firm understanding of the challenges facing your institution, including process inefficiencies and the limitations of your current technology systems. This will help you identify opportunities to leverage specific AI approaches that would offer the greatest benefit to your fraud and BSA/AML programs. Consider areas where technology solutions might result in immediate improvements, and what projects might require longer-term evaluation.
- The quality and quantity of data: Evaluate whether your institution will have access to the high-quality data necessary to ensure success with artificial intelligence approaches. With a rich set of labeled data on which to train machine learning algorithms, financial institutions will benefit from fewer false positives, more confident decision-making, and increased investigative effectiveness.
- Technical expertise: You should consider the technical expertise required to develop and maintain any new technologies. A successful evaluation and implementation of AI approaches requires insight from experienced integration experts, data scientists, analytics experts, and AI specialists. When considering technology options, you should evaluate solution providers that specialize in big data intelligence with proven data and domain expertise.
Financial institutions need to evolve at the speed of innovation, to keep ahead of financial crime trends. As the industry accelerates adoption of AI, your institution should assess how to maximize the potential of machine learning and RPA for financial crime management.
In upcoming articles, we take a deeper dive into machine learning and robotic process automation approaches, and the impact of these technologies for financial crime management.
