How to start using AI to combat money laundering – Part 2
In Part 1 of this blog, we discussed how to use anomaly detection to get started with using machine learning and AI to combat financial crime. The next step is to look at transaction monitoring and robotic process automation.
Transaction monitoring using machine learning
Most organizations are familiar with rules-based transaction monitoring where instances like when transactions exceeding a certain value or fund transfers from certain high-risk accounts are flagged.
While these are still valid and need to be part of any transaction monitoring system, technology has allowed financial institutions to augment the effectiveness of these systems with machine learning.
Instead of focusing on monitoring and flagging transactions that did not pass a specific threshold, the system is designed to look for suspicious or fraudulent behaviour. Like in anomaly detection, the system using data science and machine learning to take in various inputs, including historical data that led to a filing of a suspicious activity report (SAR), to determine whether a transaction is likely to be fraudulent. This is not a binary response of suspicious/not suspicious but rather the likelihood of being fraudulent.
Effective AI technology solutions leverage a layered analytics approach which allows financial institutions to reduce the high levels of false positives generated by rules based systems. Less false positives allows compliance and fraud prevention staff to focus on investigating true compliance risks and fraudulent cases.
The compliance department sets the threshold for which they hold transactions for further investigation. Again, these can be tailored to the institution’s risk tolerance. Some may decide that they investigate transactions with a threshold of “50 or more” while more risk-tolerant organizations may increase the threshold.
Aside from the ability to catch more fraudulent transactions, another positive outcome of evolving from rules-based transaction monitoring systems is that implementing these models requires the organization to revisit their risk assessments. The information captured can then be used to show auditors and financial regulators that reasonable measures were in place to detect illicit activities.
Robotic Process Automation (RPA)
Analysts spend a significant amount of time reviewing a large number of alerts and cases each month. The process is exhaustive and prone to human error – which can be costly for an organization. Robotic process automation not only increases the speed and efficiency at which decisions are made during the review process, it also takes away the need for compliance staff to get involved with routine tasks.
Organizations that incorporate automation into their programs enjoy many benefits including time and cost savings, increased compliance to policies and procedures, fewer errors and the ability to quickly adapt to new rules and regulations.
Tasks that can be successfully automated include those that are:
- Rules-based, rather than those that require human intervention
- Are triggered and supported by digital data
- High volume and repetitive.
When starting on their automation journeys, many financial institutions look at their tasks and processes that are very time consuming and routine. Examples of where automation and workflows can be used by financial institutions for AML compliance include
- Validating client information against internal and external sources during the customer due diligence phase
- Screening customers against sanctions, politically exposed persons (PEPs) and negative news lists
- Acknowledging and resolving alerts created by the transaction monitoring system that should be treated the same way
- Verifying account activity of high-risk activities
What I have provided in this article is a high-level view about how anomaly detection, transaction monitoring using machine learning and automation (RPA) can impact and enhance AML programs. To learn more, contact us to start a conversation.
About Khaled Ghadban
Khaled Ghadban (LinkedIn) is Director of Analytics and Data Science at CaseWare RCM and has more than 20 years of experience in the data analytics space in various industries, including financial services. He is a regular contributor to conferences and a frequent speaker on the topics of analytics and cognitive (AI).