Efficient Transaction Monitoring for Smart Compliance
The COVID-19 Pandemic has enabled digital transaction volumes to increase quicker than previously projected. According to a recent research study, digital transactions will triple to $6 trillion worldwide by 2024, up from about $2 trillion in 2020. This is due to the number of mobile transactions increasing and banks encouraging the use of wallets and contactless cards. However, with an increase in digital payments, many financial institutions are dealing with a sharp increase in transaction inefficiencies, including an increase in legitimate transactions being rejected as fraudulent.
Monitoring such hefty amounts of transactions involves elaborate processes and steps. Banks and financial institutions usually monitor transactions automatically using sophisticated software solutions. It is done in response to the AML (Anti Money Laundering), CFT (Countering the Financing of Terrorism), and KYC (Know Your Customer) requirements as stated by the regulatory authorities. During customer transactions, if any suspicion triggers the rules set by the financial institution, the transaction monitoring software generates an alert. When the software generates an alert, the transaction monitoring process is automatically paused, and the process is reviewed in detail by the firm’s Compliance or Risk Department. If they detect any genuine suspicion or criminal activity in the transaction, it is reported to the regulators in the form of Suspicious Activity Reports or SARs. The results of the findings are usually sufficient to predict a client’s overall transaction behaviour and determine if there is a threat.
Generally, any fraudulent transaction process involves multiple steps and plenty of human intervention. Moreover, it involves even more time and money. And even then, there’s a high chance of the transaction alert to be a false positive. Financial institutions should now have the ability to quickly and efficiently investigate and respond to suspicious activity through real-time data precision. This reduces the risk of financial loss due to delayed response to alerts and streamlines the AML transaction monitoring process.
A Framework for Efficient Transaction Monitoring
Banks and financial institutions need to have a robust decision support system by leveraging an efficient predictive analytics platform which uses a self-learning mechanism. Such platforms help in using high accuracy decision support, by using predictions and risk scores which can further be used to have detailed audit features for recording of model output to trace the rules creation within the organization. This helps banks to improve their system’s prediction accuracy and drive operational efficiency. In parallel, this will help banks and FIs move from static rule-based applications to a dynamically adaptive system, thus driving significant operational efficiency in its processes.
Here is the framework that can help banks and FIs in making their Transaction monitoring model more efficient:
The first step in making any transaction monitoring system more effective is to strengthen Know Your Customer (KYC) processes. KYC processes acquire first hand data about the customers and hence plays a crucial role in determining risks. Understanding a customer’s general behaviour, helps banks and Financial Institutions to identify unusual/anomalous transactions, which are not in line with the customers’ activity or behavioural patterns. This is an extremely critical measure, especially in the current environment where customer behaviours are changing rapidly.
One of the biggest challenges a financial institution undergoes is to ensure that their transaction monitoring systems are configured to achieve regulatory compliance as well as an improved performance. In order to monitor the efficiency of their AML compliance systems, banks have to identify spikes in the volume of transactions by monitoring high risk jurisdictions, identifying rapid movement of funds, and screening against sanctioned individuals and politically exposed persons (PEPs). Banks and FIs should grasp this opportunity and use it for repetitive analysis. Solutions today deploy Artificial Intelligence (AI) or Machine Learning (ML) techniques to identify any suspicious activity that is currently not being flagged by existing rules or profile-based monitoring.
Moreover, banks and financial institutions should periodically review and refine their rules and scenarios to ensure they remain updated and effective in identifying ML/TF risks. Tuning of rules and scenarios is a highly effective way in keeping up with the changing legislation and it also provides an opportunity for the organisations to tune the system and reduce false positives.
Most importantly, the effectiveness of any transaction monitoring solution is dependent on the quality of the data used in their analytic models. Systems have evolved over the years with many of them originally designed to collect and process data for various avenues, which may not be specific for AML compliance requirements, thus making the systems siloed. Banks and financial institutions should ensure that they have a robust data quality programme that periodically reviews the completeness and accuracy of data that is fed into the transaction monitoring systems.
Summary
Transaction Monitoring is an important function that helps banks and financial institutions track customer transactions instantly. By replacing legacy systems with dynamically adaptive systems that can drive significant operational efficiency in their processes, is the way forward. Banks and FIs can substantially reduce operational costs and at the same time effectively tackle scattered data and improve alert predictions by shifting to leading AI and ML driven platforms. An increase in reporting accuracy impacts greatly on the compliance environment of an organization.
The author is associated with Navaera Worldwide, A global knowledge management company that works closely with the financial services industry globally assisting them in enhancing their regulatory compliance and customer engagement processes.
