What’s New in Version 5.4 of Alessa’s CCM Solution
Version 5.4 of our software offers many new and exciting features for your continuous controls monitoring projects including:
- How new advanced fraud detection models, including clustering, data/text mining, machine learning and network analysis can detect more suspicious transactions and behaviours
- How workflow decision learning will make your system smarter by learning based on previous decisions and interactions
- How batch file attachments can be used to attach invoices, receipts and other documentation to alerts for proper record keeping during investigations
- Our new search feature that allows organizations to search alerts, work items, cases, regulatory reports, comments and attachments, as well as data from outside sources, to look for potential risks (for example, searching Export Control Lists to screen for export controlled goods)
- How Concur users can now open original images of receipts directly in our software, making investigations easier
Here’s a short summary of what is covered:
This release brings with it a series of enhancements made to help improve the remediation process. Navigation panel quick links have been added, which allows users to quickly jump from folder to folder or from item to item. The platform also now offers the ability to batch attach items.
With the click of a button, the system will search folders, and if it finds any files that correlate with an alert in the system it will automatically attach it to the case. Remediation is easier because all of the information for a case is in one place—there’s no need to search through a number of folders to find what you need.
This version includes workflow decision learning, which reduces workload and repetitive decision making and improves consistency.
The system learns based on previous decisions, is configurable to re-perform decisions, and provides the option to break decisions.
For example, if your P-Card program bans technology purchases for all but one employee, the system can learn that this employee is exempt. The compliance manager doesn’t need to review an alert: the system will see that the purchase was from the exempt employee and will let it through.
Our newest release includes a new enterprise search capability that returns results incredibly fast, even for tens of millions of records.
Available from almost any window in the platform, the tool searches internal sources (such as customer and vendor masters, alert, cases, regulatory reports, HR records, etc.) and external sources (such as export control lists).
To refine searches, there is also an advanced search window that allows users to add filters to reduce inapplicable results. This new tool makes it easier to find additional pieces of information to tie into investigations.
Real-Time Onboarding Due Diligence
This version offers real-time onboarding due diligence, which helps to minimize the number of alerts that come in.
This API allows you to use our solution from your onboarding system to screen and verify the customer. It then feeds the information back to the onboarding system so you can make an informed decision.
The platform also run analytics and generates customer risk-scores to further aid sound decision making. By catching issues before onboarding a customer, the number of post-acquisition investigations is reduced.
Rules-based analytics are no longer enough to effectively detect fraud and other types of financial crimes. The newest release features advanced analytic models focused on fraud detection, including:
- Predictive analytics – Leveraging current and historical information via models to predict an outcome based upon a set of inputs or conditions
- Prescriptive – Models and data simulate or optimize a course of action based upon a set of rules, models and business process
- Anomaly detection – Detects changes in behavior based on history, and creates clusters to detect behavior that is drastically different from similar peers
- Network linking – Uses static and/or transactional data to identify associations by link analysis, and adjusts risk scores based on strength and distance of associations
- Machine learning – Analytics feed back into the platform to feed machine learning and continuously improve processes