Machine learning (ML) has become a key weapon in the technological armory for wealth managers. A subset of Artificial Intelligence, (AI), ML is about algorithms that can not only follow the instructions set but also feed data back to themselves to continuously improve their decision making and predictions. 

An important part of ML is that it also knows when it does not know and thus when to refer something back to a human – it then learns from the human.

So, as well as being able to enhance and automate processes by taking decisions, it can also help humans make decisions by making data and experience-based predictions and suggestions with far more accuracy than humans can alone.

A key facet of ML is explainability. This is where the ML has to be able to show how it came to a decision and where unintentional bias can be shown to be removed, if necessary. This is important where the ML is being asked to make decisions with limited information or where it has made a decision by learning from itself. Knowing when you don’t know and being able to explain how you got to a decision is key in terms of portfolio management, compliance as well as customer service. 

Machine learning algorithms have a spread of case uses and can be usefully leveraged in an operational setting as well as to better inform the front office conversation.

Here’s how. 

Compliance – risk
Machine Learning is very good at spotting the needle in the haystack and can contribute significantly when it comes to monitoring the vast quantities of data from trades, cross-check transactions, and dealings with counter-parties. This is useful in terms of spotting when something is not quite right in a variety of contexts. 

Exceptions management is one. The ML can pick up the exception and know what to do with it. Risk tolerances are another. The ML can pick up on patterns that might lead to a breach and suggest alternatives. Potential misconduct is a third - working along much the same lines as risk tolerance to know when the road ahead is riskier than ideal and alerting humans when triggers have been breached. 

Portfolio and asset management
Machine Learning is not just the computational power to take in vast quantities of both structured and unstructured data, but to also put it all together in a meaningful way and provide predictive models that improve decision-making. 

In particular, ML can also be applied to esoteric investment types that are proving increasingly popular with investors as they seek alpha. Asset types like ESG or digital assets often have a lot of contextual information around them that is not easy to classify. ML can help with this - by identifying various scenarios where investments with x characteristics are likely to perform, the ML effectively sifts through what would otherwise be an unworkable volume of information for a human. 

Suitability
ML can also be useful in a suitability situation, again, particularly with riskier products that would previously been available only to ‘experienced investors’ and that are now coming down the value chain - such as hedge funds and real estate. Using technology to automate, spot patterns make decisions around what to do next in waters that have not previously been extensively explored is useful – as is the knowledge to know the boundaries and when to refer something to a human.  

Operational efficiency
Machine learning is also helpful in a more holistic operational efficiency context and can help with making better strategic decisions to grow the business. 

Robotic process automation (RPA), using ML, has come to the fore as a good solution for streamlining data management, reporting, and all sorts of research and report production activities. Machine learning takes the process one step further, allowing a firm to move away from simple automation and rule-based report generation and toward on-demand custom reporting, thus saving human and financial resource.

ML also has a role to play in a merger and acquisition situation where systems, processes, and data from two companies need merging. previously distinct entities need to come together to make a new whole. Different ways of classifying data can be a particular sticking point so the ML can work towards a single data classification process and system. This is an efficiency and accuracy play; hyper-charging automation for operational gain, be that human or financial. 

ML can also help to pinpoint missing links within a company to reveal where a company needs to plug revenue gaps or where the opportunity lies going forward; informing better strategic decision making. The idea is to identify patterns and work to optimize a given situation going forward. 

Client experience
Machine Learning is also a highly effective tool in the customer-facing world. It allows for decisions to be made on a broader range of information to identify, and even predict, clients’ needs and provide a service that is tailored and personal. Important in today’s wealth management environment where value add is all! 

The technology takes in the advisor already knows about clients with data on the products and services each client has and combines it with other information, internal, external, and both structured and unstructured. The idea is that the ML informs the decision over what to offer the client and when. 

This is not just in terms of management but also applies equally to the whole gambit of services on offer by wealth managers; succession planning and inheritance, philanthropy, tax, and legal services, and more. In this way, Machine Learning can create models that alert wealth advisors to a change in their clients’ situation or behavior, which may flag the need for a conversation with a view to introducing a new product or service.  

Machine Learning makes all of this possible and more. However, a workman is only as good as his or her tools and thus the data being fed into the algorithm needs to be plentiful and suitable for use. 

This is where the Hyperscience solution comes in. It automatically captures and syncs data from anywhere in one streamlined process. It can collect information automatically from incoming emails, create customized workflows that route documents directly to the right person, and set rules to identify or isolate sensitive documents. Hyperscience also applies powerful machine learning tools that ignore unnecessary fields and descriptions. So, wealth managers only get what they need, without having to sift through the haystack, so to speak.

Hyperscience can use your entire internal document library to pull metrics out of silos in any area of business, this allows the identification of weaknesses, and development opportunities – in terms of clients, middle office type functions or general operational efficiency. It makes data collection both fast and accurate and thus suitable to be fed into an ML algorithm for further decisioning.  

Ultimately, solutions like Hyperscience use ML at the data input level to present clean and classified data to further ML case uses. The ML model output is only as good as the data fed into it and so a tool that makes sure the corporate data management infrastructure is sound in the first place is well worth considering.

This is the job of Hyperscience – to ensure that Wealth Management companies have data that is accurate and accessible and that the processes of data sourcing and analysis are as good as they can be.