SOLUTIONS — TRANSACTION MONITORING

Automate Transaction Monitoring

Mitigate risk by enabling earlier and faster escalation of potentially suspicious alerts.

3-5X
ROI

Takes less than 6 months to break even after deployment

Fewer Manual
Touchpoints

Eliminate human error-prone activities

Analysts to
Editors

Shift people from L1 screenings to investigations

Defensible Automated
Decisions

AI results trained and tested on millions of alerts

The problem

Transaction monitoring (TM) enables FIs to detect and mitigate the risk related to customer behavior and activity. It is required internal control for anti-money laundering/countering the financing of terrorism (AML/CFT) programs globally and is a critical tool for fighting financial crime by enabling institutions to detect unusual and possibly suspicious transactions, conduct deeper investigations, and file suspicious activity reports (SARS) with regulators.

However, while TM is necessary, it is a difficult compliance obligation for financial institutions. Every day, teams of analysts within financial institutions review large numbers of alerts associated with transactions, patterns, or behaviors that are flagged as potentially suspicious for money laundering or other financial crimes. Analysts must determine whether these alerts are false positives, which approximately 95-97% are, or truly suspicious activity. These suspicious activity monitoring programs take a lot of time, require large teams of people, and cost a lot of money.

The existing manual process poses these challenges:

High number of false positives
Countless hours spent aggregating vast amounts of data and supporting documentation in case management systems
Enriching data from third-party sources and internal tools
Analysis for links and relationships between multiple data points
Anomaly detection for unusual patterns and outliers
Expectation analysis to compare actual vs expected activity
Any decision must be auditable and justified for regulators with supporting documentation
Inconsistent analysis and decision narratives
Laborious work might lead to errors, both technical and material

The Solution: Isaac, an AI Transaction Monitoring Investigator

Turn your analysts from authors to editors with Isaac, WorkFusion’s AI transaction monitoring investigator Digital Worker. Isaac is not a transaction monitoring tool and does not generate alerts. Isaac is AI that helps with TM alert management by using machine learning capabilities to work first-level alerts, auto-escalate alerts that are likely to require investigation and the ability to close alerts that are non-suspicious with supporting narrative and documentation, allowing your analysts to focus on the highest risk activity.

Isaac helps with common AML transaction monitoring scenarios that generate a high volume of alerts such as:

Structuring
Excessive funds transfers/movement of funds/patterns of funds transfers
Unexpected account usage/behavior
High-risk factors such as activity with high-risk jurisdictions
Use of dormant accounts

Isaac’s decisions are supported by human-readable justification, supporting documentation, and a confidence threshold to maintain transparency for examiners and auditors.

Mitigate risk and alleviate busy work associated with L1 alerts.

Isaac automates L1 transaction monitoring alert reviews, collecting data and either closing non-suspicious alerts or escalating them to an investigator. The solution picks up alerts generated from surveillance monitoring systems and then investigates and evaluates the activity. It then creates a dossier of each decision supported by human-readable justification, supporting documentation, and a confidence threshold to maintain transparency for examiners and auditors.

With Isaac you have a consistent approach to alert review and investigation, as well as the quality of the alert narrative and supporting documentation. Each of Isaac’s decisions is supported by human-readable justification, supporting documentation, and a confidence threshold to maintain transparency for examiners and auditors. Isaac takes over the time-consuming work of gathering all of the TM data. The automated solution adjudicates level 1 alerts with greater efficiency, better accuracy, and with a more comprehensive audit trail than teams of people.

Automate L1 review of TM Alerts

Isaac auto-escalates suspicious alerts for deeper review, allowing investigators to focus on higher-value investigative work.

Turn Analysts from Authors to Editors

Using the dossier that Isaac compiles, analysts can easily read through the data and narrative, as opposed to the time-consuming task of searching and compiling data.

Mitigate Risk

Mitigate risk by enabling earlier and faster escalation of potentially suspicious alerts. Automated L1 review saves time and effort of human analyst review, reducing and eliminating manual touchpoints based on risk thresholds, allowing the analyst to focus on the risk at hand.

Improve Compliance

Isaac provides documented, transparent, human-readable decisions of the alert-review process with a confidence threshold for regulators.

Industry Insights

Blog

Sick and Tired of Day-30 Scrambles in Your L1 Transaction Monitoring Alert Reviews?

Blog

Overcoming AML Transaction Monitoring Challenges with AI

News

WorkFusion Launches New AI Solution for AML Transaction Monitoring