A Guide to Anti-Money Laundering for Crypto Firms

Alert Prioritization: How AI Makes Legacy Processes More Efficient

Transaction Monitoring Knowledge & Training

AML teams using legacy transaction monitoring programs frequently deal with backlogged systems. Their analysts often experience burnout from processing high volumes of alerts with too many false positives. Without a way to triage incoming alerts, highly-qualified investigators can spend most of their working day on rote tasks: clearing overloaded systems and low-risk alerts. 

This doesn’t just create frustrations – it wastes company time, financial, and energy resources, overloads personnel, and makes it more likely that teams will miss illegal activity. It can also lead to unwanted organizational costs and losses. For example, team burnout means high turnover rates and costs to recruit and train replacements. Poor screening can result in losses to fraud and resulting disputes. And most importantly, if a company is deemed to have insufficient risk management processes, it can face regulatory fines and legal action.

Many financial institutions may worry that a system overhaul would cost even more. But it’s actually possible to keep a firm’s core system in place and merely overlay artificial intelligence algorithms to enhance its abilities. How would this work in practice?

Advantages of Alert Prioritization

Consider a scenario. A senior analyst, Allison, has been dealing with bloated and imprecise alert queues due to rigid rules and no triage by priority. Every day, she spends hours painstakingly working through individual alerts without an efficient way to tell which are critical and most worth her time investigating. When she does come across a high-risk alert, she has less time to research it because of wasted time clearing false positives. In fact, if the system is backlogged, alerts tied to actual financial crime may sit in the queue for days or weeks before they’re found. The team has lost several members recently, but Allison doesn’t have time to keep up with her queues and train new teammates effectively.

Imagine Allison’s company adding a layer of artificial intelligence over its existing system to handle alerts more intelligently. The AI knows how to triage the incoming alerts by risk level, assigning a high level of risk for those showing the most suspicious activity. It will also continuously improve based on analysts’ feedback. When Allison comes into work, she immediately begins looking at the high-risk alerts queue. She can confidently spend appropriate time researching the most suspicious activity in the queue. If her queue is overloaded and a backlog starts to accumulate, she can rest easy knowing she’s tackled the riskiest alerts assigned to her.

Meanwhile, the lowest-risk alerts are either resolved in bulk by the system or used to train newer analysts. And when mentoring advancing team members, Allison can use the high-risk queue to illustrate how to handle risky alerts. Alert prioritization maximizes Allison’s value to the company and also reduces the risk associated with training new hires.

An artificial intelligence overlay can provide a simple, cost-effective option for companies that need AI’s benefits but aren’t in a position to make a major overhaul. Using an overlay also involves fewer unknowns, since the algorithms don’t replace existing processes but enhance them. With minimal disruption, firms can improve AML/CFT compliance efficiency with AI-enhanced alert prioritization and escalation – reducing risk and related costs while improving team morale and supporting employee retention rates.

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Originally published September 29, 2022, updated September 30, 2022

Disclaimer: This is for general information only. The information presented does not constitute legal advice. ComplyAdvantage accepts no responsibility for any information contained herein and disclaims and excludes any liability in respect of the contents or for action taken based on this information.

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