©2019 by Lassio Pty Ltd

Audit, Records, Risk, Compliance


Transaction monitoring systems generate a large number of alerts that must be investigated to identify suspicious activity. For example the majority of processes involved in resolving and closing alerts are semi-automated or completely manual. This leads to delays in review and remediation. Most of the work involved in resolving alerts is standardised and repetitive, which is ideal for automation solutions embedded with cognitive capabilities. By automating judgement based tasks related to suspicious activity alert investigation, cognitive RPA can speed up issue resolution and improve overall fraud management within government, corporates and regulated industries.


During the onboarding process, collating and populating data from disparate internal systems and external sources is a challenging task. Automation can be used to collect and retrieve and verify data from regulatory agencies (such as ASIC ) and other government agencies to speed up the onboarding process. This could relate to certifications and document validation. This can help identify interaction with undesirable suppliers or employees.


Internal management reporting and external regulatory reports such require teams to spend significant time on manually gathering and assimilating data from different sources. Data gathering and consolidation is a lengthy and tedious process, which doesn't leave adequate time for reviews, adversely impacting the accuracy and quality of these reports. By Automating the data assimilation process, teams will be better able to focus on analysis and review of reports.


Reconciliation activities are standardised and can support collation of data from disparate systems based on specific requirements. Once automated the process can also carry out business rule checks to enable faster resolution of issues. Faster reconciliation in turn results in quicker management information reporting (both internal and external) and timely reviews that can help discover errors and anomalies.


Stress testing requires data to be aggregated and consolidated from multiple systems and data slioes. Line items can be analysed based on business rules and alerts generated.