How AI and machine learning could improve wealth management

By Daniel Lanyon on Tuesday 31 October 2017

Savings and Investment

From robo-advice to chatbots, the much-hyped technology will be revolutionary for finance, claims a new report.

In the past 12 months interest in artificial intelligence (AI) and machine learning has soared with almost every aspect of the UK economy touted to be disrupted by the two technologies over the next decade.

You could even say it is pretty hyped, perhaps an understatement when observing the below investment return of some of the funds investing in the listed equity of AI firms.

Performance of Global X - Robotics & Artificial Intelligence ETF over 1yr

Source: FE Analytics

Asset management and financial services is no exception with huge applications expected to transform various aspects of the market, according to a recent report, entitled the State of AI in 2017, from Numis.

These functions include firms’ investment strategies, portfolio construction, risk management and client services.

AI’s ability to extract content hidden in unstructured data using natural language processing,  it says, will help to find subtle patterns in disparate data sets. This will enable machine-to-human communication via ‘chatbots’ and has multiple applications in asset management.

“By augmenting or automating many of an asset manager’s tasks, AI enables asset managers to deliver to the mass affluent a degree of personalisation and service quality previously reserved for high net worth clients,” it said.

Additionally, it says AI could decrease the cost of portfolio construction while improving quality, bringing about the era of the ‘robo-advisor’ across the field of asset management.

The report says investment strategies could substantially shift as AI improves a firm’s number crunching. This is done by ‘synthesising’ its research and data, and incorporating broader data sets including unstructured information to bring about greater understanding of market trends.

“Superior pattern recognition can then deliver better multi-objective optimisation. AI can balance a diverse range of inter-connected objectives (including fund deployment, risk and profitability) to enhance returns more effectively than rules-based systems,” the report said.

Within portfolio construction AI tools can augment, and increasingly automate, an asset manager’s process of portfolio construction this again could potentially lower costs at a time when active managers are under pressure to cut fees.

AI-powered ‘robo-advisors’ can also “analyse a client’s goals, and within a firm’s investment rules develop personalised, optimised portfolios at low cost and high speed,” it says.

Risk management is one of the areas that could be impacted the most, as AI can improve risk management for the same reasons it enhances investment strategy. Through the interpretation of broader data sets and improved cognitive processing, risk metrics may be improved in their predictive power.

“90 per cent of data generated today is unstructured information, stored outside traditional databases Natural language processing enables additional data sets to be incorporated into firms’ analyses. Other AI techniques, including deep learning, then enable patterns in data to be identified with greater granularity and confidence.  Together, these capabilities enable risks to be identified and quantified more effectively,” the report said.

Another area the technology could have profound implications is simply in how firms communicate to clients.

“Chatbot interfaces are being applied within and beyond asset management firms. Deployed in client-facing channels, natural language systems enable client enrolment, support and self-service."

"Embedded in internal tools, chatbots let account managers query client details and understand developments relevant to a client’s portfolio in seconds instead of minutes. Fewer account managers can then provide a higher quality service to a greater number of clients,” it said.

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