As per PwC’s 2018 Global Economic Crime and Fraud Survey, 49 percent of the global companies interviewed had been victims of fraud or economic crime, up from 36 percent reported in 2016. Moreover, 58 percent of financial institutions (FIs) encountered regulatory enforcement or inspection within the last few years.
Now is the time for FIs to ramp up anti-money laundering (AML) transaction monitoring (TM) operations. Implementing a ‘band-aid’ solution can no longer serve as a long-term compliance solution. Just as the rate of fraud or economic crime is increasing, FIs are also increasing their spend on preventive measures.
How is your organization currently assessing financial risk?
The current AML ecosystem in banks is facing multifold challenges
To combat illicit transactions, FIs are striving to implement a risk-based strategy in their AML transaction monitoring system. It will help FIs take more efficient preventative measures, reduce false positives, and channel efforts to investigate real fraud cases.
Even though FIs have been adopting third-party TM systems to combat threats, they still seem to be struggling. Mismatched expectations about how a vendor's TM system can improve the FIs' TM program, an increasing number of false positive alerts due to various configuration of business rules (including poor scenario and threshold logic), incorrect segmentation and/or profiling, data availability and quality issues (due to non-reconciliation of source system data) are just a few challenges related to third-party TM systems.
Consequently, FIs often end up with a system that is not as effective at combating money laundering and meeting regulators’ expectations as they assumed it to be.
Why it is important to involve RegTech in the overall AML strategy
RegTech plays a crucial role in addressing several traditional problems for FIs and helping them stay compliant with ever-changing regulations. For instance, RegTech can drive down the cost of compliance by simplifying and standardizing processes through automated mapping of regulatory risks to key business processes.
Moving away from rigid enterprise risk management systems and utilizing sustainable and scalable solutions can allow FIs to smoothly scale up in response to business demands. This can be achieved by building real-time risk evaluation tools with scenario analytics and horizon scanning technology to proactively identify risk and adapt to new regulations, as well as enhance operational efficiency and effectiveness.
It is important to include machine learning (ML) in the existing third-party product ecosystem to analyze transaction flow and customer behavior data to determine if a customer is a threat or not. FIs can then segment the profile activities to quickly detect and handle threats. To enable that, customer profiles can be grouped into suspicious versus non-suspicious datasets and ML can be used to detect anomalies within those classifications.
Leveraging ML at the alert triage stage can also help FIs increase operational efficiencies by allowing them to classify alerts into categories and helping them decide whether alerts need to be suppressed, hibernated or, dismissed. In this way, FIs can appropriately use compliance data and apply matching rules that output real-time risk assessments and alerts. For instance, alerts can be auto-enriched with accounts, transactions and customers’ information from external and internal data sources leveraging intelligent automation and Natural Language Processing (NLP); and narratives for Suspicious Activity Reports (SARs) can be auto-generated using Natural Language Generation (NLG).
As a result, ML can improve operational efficiency by 30 to 50 percent approximately as well as reduce false positives in the system up to 80 percent.
The Road Ahead
The ability to detect fraud before it turns into financial loss is crucial and should be the focus of a solution with the intelligence to discern false alerts against real threats. Start by evaluating business objectives and comparing them to existing tools and processes. Are they fluid, or are they fraught with manual, paper-based steps, slow reaction times, and false positives?
Integrating compliance into an intelligent AML transaction monitoring system not only gives FIs the ability to react swiftly to fraud incidents and decrease costs, but also sets a precedent for standardization. Transparent controls and a risk framework designed in parallel to institution-wide governance standards will improve compliance integrity. Built-in standards paired with more automated processes and less manual tasks reinforce compliance standards and significantly reduce the risk of internal mistakes and slow reaction times that lead to breaches and monetary loss.
Furthermore, rapid advancement in the RegTech space and its amalgamation with technology enables enhanced customer experience through a robust fraud detection platform that can keep financial information secure and prevent disruption in the agility and integrity of financial markets. A risk-based approach integrated with modern technology provides improved and efficient governance and compliance due to enhanced transparency and proactive reporting of risk and compliance issues in the financial ecosystem.
The shift from homegrown and third-party traditional risk management systems to scalable and sustainable solutions gives FIs the infrastructure to manage growth and keep financial information secure and compliant. Customers trust FIs that protect their data and assets against fraud, and in turn, this technology protects them from disruption of market agility and creates a competitive edge. All in all, a win-win situation for both customers and FIs.
Sandeep Kumar leads the compliance practice for the banking and financial services business unit at Virtusa
Tagged under Compliance, Risk Management, Tech Management, Security, Feature3, Fintech, Technology, Online, Compliance Management, Compliance/Regulatory, Cyberfraud/ID Theft, AML & Fraud, Feature, BSA/AML,