Alert Prioritization: How AI Makes Legacy Processes More Efficient

Alert Prioritization: How AI Makes Legacy Processes More Efficient

AML teams using legacy transaction monitoring programs frequently deal with pending systems. Their analysts are often exhausted from processing large volumes of alerts with too many false positives. Without a way to triage incoming alerts, highly trained investigators can spend most of their working day on rote tasks: cleaning up overloaded systems and low-risk alerts.

This doesn’t just create frustration – it wastes company time, financial and energy resources, overburdens staff, and makes it more likely that teams will miss out on illegal activities. It can also lead to unwanted organizational costs and losses. For example, team burnout means high turnover rates and high costs to recruit and train replacements. Improper selection can lead to losses due to fraud and resulting litigation. Importantly, if a company is deemed to have insufficient risk management processes, it can face regulatory fines and legal action.

Many financial institutions may fear that a system overhaul will cost even more. But it’s actually possible to leave a company’s core system in place and simply overlay AI algorithms to enhance its capabilities. How would this work in practice?

Consider a scenario. A lead analyst, Allison, faced bloated and inaccurate alert queues due to rigid rules and a lack of priority sorting. Every day, she spends hours painstakingly working on individual alerts without an effective way to determine which are critical and most deserving of her investigation. When she comes across a high-risk alert, she has less time to research it due to the time wasted eliminating false positives. In fact, if the system is late, alerts related to real financial crime can sit in the queue for days or weeks before being detected. The team has recently lost several members, but Allison doesn’t have time to keep up with her lineups and train new teammates effectively.

Benefits of Alert Prioritization

Imagine Allison’s company adding a layer of artificial intelligence to its existing system to manage alerts smarter. The AI ​​knows how to sort incoming alerts by risk level, assigning a high risk level to those with the most suspicious activity. It will also continuously improve based on analyst feedback. When Allison arrives at work, she immediately begins checking the high-risk alert queue. She can confidently spend the necessary time looking for the most suspicious activity in the queue. If her queue gets overloaded and a backlog starts to build up, she can rest easy knowing she’s tackled the riskiest alerts assigned to her.

During this time, the least risky alerts are either resolved in bulk by the system or used to train new analysts. And when mentoring advancing team members, Allison can use the high-risk queue to illustrate how to handle high-risk alerts. Prioritizing alerts maximizes Allison’s value to the business and also reduces the risk associated with training new employees.

An AI overlay can provide a simple, cost-effective option for companies that need the benefits of AI but are unable to do a major overhaul. Using an overlay also involves fewer unknowns because the algorithms do not replace existing processes but enhance them. With minimal disruption, companies can improve AML/CFT compliance efficiency with AI-enhanced alert prioritization and escalation, reducing risk and associated costs while improving morale of the team and supporting employee retention rates.

AML teams using legacy transaction monitoring programs frequently deal with pending systems. Their analysts are often exhausted from processing large volumes of alerts with too many false positives. Without a way to triage incoming alerts, highly trained investigators can spend most of their working day on rote tasks: cleaning up overloaded systems and low-risk alerts.

This doesn’t just create frustration – it wastes company time, financial and energy resources, overburdens staff, and makes it more likely that teams will miss out on illegal activities. It can also lead to unwanted organizational costs and losses. For example, team burnout means high turnover rates and high costs to recruit and train replacements. Improper selection can lead to losses due to fraud and resulting litigation. Importantly, if a company is deemed to have insufficient risk management processes, it can face regulatory fines and legal action.

Many financial institutions may fear that a system overhaul will cost even more. But it’s actually possible to leave a company’s core system in place and simply overlay AI algorithms to enhance its capabilities. How would this work in practice?

Consider a scenario. A lead analyst, Allison, faced bloated and inaccurate alert queues due to rigid rules and a lack of priority sorting. Every day, she spends hours painstakingly working on individual alerts without an effective way to determine which are critical and most deserving of her investigation. When she comes across a high-risk alert, she has less time to research it due to the time wasted eliminating false positives. In fact, if the system is late, alerts related to real financial crime can sit in the queue for days or weeks before being detected. The team has recently lost several members, but Allison doesn’t have time to keep up with her lineups and train new teammates effectively.

Benefits of Alert Prioritization


Imagine Allison’s company adding a layer of artificial intelligence to its existing system to manage alerts smarter. The AI ​​knows how to sort incoming alerts by risk level, assigning a high risk level to those with the most suspicious activity. It will also continuously improve based on analyst feedback. When Allison arrives at work, she immediately begins checking the high-risk alert queue. She can confidently spend the necessary time looking for the most suspicious activity in the queue. If her queue gets overloaded and a backlog starts to build up, she can rest easy knowing she’s tackled the riskiest alerts assigned to her.

During this time, the least risky alerts are either resolved in bulk by the system or used to train new analysts. And when mentoring advancing team members, Allison can use the high-risk queue to illustrate how to handle high-risk alerts. Prioritizing alerts maximizes Allison’s value to the business and also reduces the risk associated with training new employees.

An AI overlay can provide a simple, cost-effective option for companies that need the benefits of AI but are unable to do a major overhaul. Using an overlay also involves fewer unknowns because the algorithms do not replace existing processes but enhance them. With minimal disruption, companies can improve AML/CFT compliance efficiency with AI-enhanced alert prioritization and escalation, reducing risk and associated costs while improving morale of the team and supporting employee retention rates.

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

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