How intelligent automation is transforming the asset and wealth management space.
Traditionally, the financial services industry has been slow to implement digital operating models due to concerns over regulatory oversight and data security, with many Asset and Wealth shops left behind. According to a 2020 Retail Banking survey that surveyed 750 banks and financial companies across the world – only 7% said they deployed digital transformation initiatives at scale. However, the onset of the pandemic combined with the explosion in fintech has given the industry a renewed sense of urgency to embed digital across its operations.
Many wealth managers, asset managers and asset servicers are turning to intelligent automation to quickly address current challenges and create a more efficient, effective, scalable, and controllable operating model. Intelligent automation combines Robotic Process Automation (RPA) with technologies such as Artificial Intelligence (AI) and Optical Character Recognition (OCR) to establish end-to-end business processes that can work on their own – enabling efficiency, improving control and enabling companies to meet changing client needs.
Here are three common use cases that can benefit from intelligent automation:
1. AI for trade surveillance
AI and Machine Learning (ML) are revolutionizing the process of trade surveillance – monitoring activities of a firm for market manipulation/ insider trading – by increasing the quality of alerts and the time taken to process them. To date, trade surveillance has been based on traditional rule-based systems that were designed to meet the requirements of regulators. As a result, they would generate a high volume of false positives (surveillance alerts on suspicious behavior) which could not be feasibly monitored. Coupled with the need to carry out a manual follow-up, this meant it was difficult to identify suspicious activity.
Now, asset and wealth managers are employing ML and RPA techniques in the surveillance fight. ML is helping companies recognize anomalies in trader behavior based on historical alerts and patterns while RPA is giving companies the freedom to gather information from different trading venues and asset classes. These accurate analytics are reducing the human workload involved in investigations and enabling asset and wealth managers to focus their efforts on investigating false positives.
2. Client Lifecycle Management
Next, with rising demand for providing best-in-class client experience across the lifecycle ecosystem, the extremely manual process of gathering documents and data for onboarding new clients no longer supports growth. Previously, analysts would gather all the necessary documentation from clients including financial/ personal data, complete credit checks, and carry out legal due diligence. According to Deloitte, this process took around 20 to 90 days with the possibility of being extended to 16 weeks. The length of this process coupled with the possibility of a human error made it unfeasible.
In comes intelligent automation which has completely re-engineered this process by leveraging Optical Character Recognition (OCR) a key feature of RPA. This technology has automated operational business processes with its ability to scan documents, extract the required client data, and populate the Client Lifecycle Management (CLM) and reference system. It has yielded a range of benefits including reducing operational costs by streamlining work and allowing for tasks to be completed with far fewer human touchpoints. Ultimately, this improves accuracy around regulation risks and overall control of the process and allows for savings to be felt immediately – as it only takes a fraction of the
current time frame. Customers also benefit from employees directing their attention to higher value-add activities which increases client satisfaction.
3. Order Management
To date, the majority of asset and wealth managers leverage vendor order management platforms (a process of managing people and processes connected to an order as it moves through the lifecycle) that are embedded into a fragmented, out-of-date ecosystem, and do not fully adapt to changing client demands. Firms tend to have limited budgets for their operating models which means they are suffering from capacity, flexibility, performance, and reliability issues. For example, due to the lack of full integration, many firms still rely heavily on manual functions, such as spreadsheets to provide a quick offering to client requests. Although it does allow for flexibility and a bespoke element, it is accompanied by risky offline processes. Despite this fragmentation, it is a common objective among firms to achieve operation risk reduction in their operating models.
Intelligent automation has the power to help these firms manage and reduce risk by combining RPA and Intelligent Document Processing (IDP) techniques. These techniques can improve operations by bringing additional information from other portals thus eliminating the need for a manual look-up and identifying errors based on past experiences. By digitizing and fully integrating the order processing workflows, it improves communications, fulfilment speeds, and the ability to measure efficiencies.
Ultimately, the implementation of intelligent automation has enabled asset and wealth managers to address current challenges and establish more efficient operating models. Across the board, from trade surveillance to order management platforms, they’ve reinvented critical business areas, introduced new processes, and added value to their customer experiences in previously unimaginable ways. Issues over regulatory involvement and limited budgets have been overruled as companies understand how these new technologies can streamline processes, allow for capacity creation, and support risk reduction.
