By Stuart Robson, Financial Services Business Analyst, Future WorkForce
The UK’s wealth management sector has experienced significant growth in recent years and handles around £948bn of assets, equivalent to about 46% of the UK GDP. As such a vast industry, it’s not surprising that financial institutions and wealth management firms are looking to be early adopters of the latest AI technologies.
Wealth management is a data-rich industry that has traditionally relied heavily on human data re-keying processes. Many thousands of person-hours are devoted annually at these firms to repetitive, mundane data inputting tasks and simple data interrogation and cleansing. Data can be about clients or markets and using the two sources intelligently is a principle of the wealth management business model. But the latest artificial intelligence (AI) technologies will change this model.
Like many other organisations, asset management firms often have a segregated structure (apart from those areas that need to be separated under financial regulations). And with finance being such a broad and deep field, employees build their experience or specialise in subfields. This partitioning is also true of the niche legacy applications used in firms that have developed over time. For example, one application can be used by the performance team to calculate portfolio returns, and another by the client reporting team to produce reports that contain information about those returns.
While understandable, these silos mean that the systems often don’t interact and use incompatible data formats. Thus, employees become the go-between manually duplicating data between systems which is inefficient and results in human error. Further manual work is often then required to correct mistakes. Client deadlines are not met, and SLAs breached, resulting in immediate monetary loss or penalties to the wealth management firm.
So, how can AI help? The most significant gains are to be had in the intelligent automation field. Technologies such as robotic process automation (RPA), process mining, conversational AI, and automated decision-making can deliver real outcome improvements.
RPA software can be used to transform legacy systems without replacing or redeveloping them. The latest zero-code machine learning tools mean a person without coding skills can build and teach a software robot to do simple tasks. They can assume the role of data scientist to automate even complex processes. For example, a software robot can read performance returns from one application and enter them into the client reporting application without error. Not only does this create massive time savings and improve operational efficiency, but it also frees up employees from the repetitive tasks that they don’t like to focus on more strategic work.
Communications mining tools are also set to revolutionise the industry. Deep learning technology can now convert unstructured communications (emails, calls, chats, notes) into structured data in real-time. It does this by using unsupervised learning algorithms on a source of unstructured data, e.g. an email inbox. These then create clusters of similar communications (emails in this example) to be presented back to the user. The software will cleverly group emails it thinks are about the same thing. It then asks the user to ‘label’ the cluster to learn what conversation these emails are covering. The tool will then present back more examples to be confirmed and continuously develop its learning.
With machine learning, the user can also label data points within emails by simply highlighting an entity and giving it a name. This is where automation possibilities are unlocked. Users can educate the machine learning tools to identify what a Sedol, ISIN, gross return, or net return looks like from within the body of an email. Company name, client name, fund name and more. The tool will then find each entity’s nuances to present back to the user for affirmation or correction.
IT requests are another area where AI can help create enormous efficiencies for organisations. Using metadata gathered from communications mining, wealth management firms can see where automation benefits could best be realised. For example, a high volume of IT requests relate to onboarding new clients and new employees. A machine learning product can be trained to understand an onboarding request coming into the IT mailbox and recognise and pick out relevant elements.
Using RPA robots then communicate to relevant teams and systems, setting up the client or worker correctly. The whole process can be automated using a suitable workflow tool that manages how each process is handled and by whom.
AI can transform how the wealth management industry operates by reducing the person-hours spent on mundane, repetitive tasks. As a highly competitive industry, firms that embrace AI will keep a competitive advantage by driving efficiency and customer service improvements.
Using RPA, robots then communicate to relevant teams and systems, setting up the client or worker correctly. The whole process can be automated using a suitable workflow tool that manages how each process is handled and by whom.
For the wealth management industry that spends so many man hours on mundane, repetitive tasks AI had the ability to transform they way they operate. As a highly competitive industry, those firms that embrace AI will keep competitive advantage by driving improvements in efficiency and customer service.
Global Banking & Finance Review
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