Crises often result in significant strain on financial services firms. Take the COVID-19 pandemic – it has contributed to a substantial rise in fraud instances as people under financial strain seek out dishonest ways to access funds and criminal organisations take advantage of a vulnerable population.
A raft of regulatory changes – like the ability to access super schemes earlier – have also placed enormous strain on operating models. Financial institutions have seen huge spikes in the number of potentially suspicious activities, causing a corresponding series of spikes for reporting entities to manually sift through these potential risks.
Where there is a reliance on outsourced administrators, we have observed the struggle wealth managers have to keep pace with additional activity being flagged in reporting.
For organisations that manage administration in-house, these functions may not have been set up to flex and scale for the changes in volume and velocity of customer activity. Within wealth management organisations, large amounts of customer and transactional data are often fragmented as a result of distributed roles and responsibilities, or multiple disparate systems and processes being joined together from inorganic growth and mergers.
The resources required to not only pull this information together in a timely manner, but also make assessments on any potentially suspicious nature of the red flags, require a colossal effort. And it’s been difficult for the industry to get it right with manual processing.
AUSTRAC recently shared feedback on the adequacy and data quality of suspicious matter reporting, which they highlighted as a challenge for reporting entities.
Technology solutions can and are making a difference to these issues. Combining Robotic Process Automation (RPA) and Natural Language Processing (NLP) have presented the opportunity for a huge shift in effectiveness and efficiencies in this process. RPA uses pre-determined logic and inputs to automate manual business processes enabling organisations to capture, interpret and manipulate data such as copying information from different systems, and filling out forms. Coupled with NLP, this can automate more sophisticated tasks that require complex reasoning and judgements to determine suspicious activity that require reporting – moving away from purely rules-based systems that can quickly become outdated or produce large numbers of false positives.
Our work with a Singaporean client highlighted that this powerful technology combination contributed to a 40% reduction in false positives for transaction monitoring, and a 5% increase in true positives.
Organisations that operate leaner financial crime teams with fewer dedicated specialists have the opportunity to leverage these technologies to streamline both the alerts coming in, and the judgement required for day-to-day escalations. Less time spent on manual tasks, means more capacity for financial crime experts to be operating at a more holistic level for the organisation.
Read our Asia Pacific case studies to uncover how technology can transform financial crime risk management in financial institutions. Find out more about Deloitte’s Financial Crime solutions.