How much of a role will artificial intelligence realistically play in the investment management industry?
As we discussed in a previous blog, implementing more traditional automation initiatives, to get the foundations right before adding AI complexity and data demands on top, should be firms’ initial priority. But then what?
While the potential is seemingly limitless, we see AI coming to the fore in three key areas.
Customer interactions
As artificial intelligence capabilities become a growing part of our day-to-day lives, customer expectations and acceptance of AI-powered services will accelerate. More adept chatbots in client-facing portals to enhance the user experience and automate processes are an obvious example.
Abetting customer choices is another. Wealth and asset management clients have traditionally had to spend long hours researching and reviewing myriad stocks and funds to compare the strategies and returns, and determine whether they match their risk profile and investment ethos. AI will eliminate that effort.
A bot that knows the customer’s financial situation, their investment goals, risk appetite, ESG preferences, etc. will be able to instantly match the individual’s profile with the universe of products available. Armed with this information, it can recommend the top five ETFs that person should invest in, the mortgage they should have or the best available loan.
Turning prospects into customers can be made easier too. Gen AI can provide staff with personalised client onboarding training, ensure the right onboarding documents are used and prepopulate them with key details, noted a recent Accenture Wealth Management Report. And by accelerating Know Your Customer (KYC) data intake and due diligence, it can augment end-to-end operations and improve quality assurance/quality control.
Ongoing account maintenance can then be improved in multiple ways. AI can support relationship managers’ client meeting preparations by aggregating and analysing client data, and identifying relevant topics and potential products/services based on client needs, added the Accenture report. Generating post-meeting summaries for clients highlights the action items discussed, and ensures the RM keeps on top of the decisions made and what is owed to the client. AI can also reveal opportunities to proactively engage clients by sending personalised recommendations.
Investment decision-making
Gaining a competitive investment edge increasingly depends on trawling through huge raw datasets in search of hidden patterns and information nuggets. Humans alone can no longer process the scale and complexity of data involved, and risk introducing error and bias into the process. AI allows for faster, more accurate analysis of potential investment opportunities, improved due diligence and better decision-making.
“It is not difficult to envisage a world where Gen AI engines are an integral part of all the higher-skill tasks of asset allocation, model portfolios, security selection and risk mitigation,” reckons former PIMCO chief Mohamed El-Erian. “These engines will be trained on the enormous data sets that reside in the sector and, currently, are grossly under-exploited.”
For example, a Financial Times article noted AI systems can study executives’ calls with analysts, scanning for “clarity of purpose, analyst responses, and whether companies’ results live up to what their bosses are saying.”
Middle- and back-office support functions
In addition to enhancing investment research, asset and wealth managers can harness AI to transform their middle-office operations, improve asset valuations, strengthen internal next-best-action frameworks, generate code and automate workflows, EY explained in a recent note.
Communicating returns and performance attribution to clients can be done faster and more accurately, El-Erian observed.
Risk and compliance functions could benefit by scanning metadata for certain indicators, such as evidence of undeclared offshore bank accounts, if a person visited a red-flagged jurisdiction or is conducting transactions with a sanctioned company or individual – buttressing firms’ anti-money laundering and tax and sanctions evasion checks.
For software vendors and system users, AI’s ability to analyse large amounts of data in real time is a major boon, offering instant diagnostics to find and resolve problems. When issues emerge, firms historically have had to download system logs and manually scour the lines of data searching for the cause. AI can scan that data and pinpoint why the application went down in seconds.
Similar principles apply in cybersecurity. Cyberattacks’ growing scale and complexity risk overpowering security professionals. AI can exacerbate that, making deep fakes, viruses, phishing attacks and so on easier to produce. But it can also help counter the trend.
By analysing logs and events, AI offers real-time threat detection of zero-day vulnerabilities. Threat modelling becomes easier and more comprehensive, enabling teams to respond pre-emptively. And by detecting security risks, it frees professionals to focus on more strategic initiatives.
AI is already disrupting the financial services sector. Those use cases, and their impact, will only proliferate. Realising AI’s potential though will depend on building trust in the technology and curbing its weaknesses – which we’ll explore in the next blog in this series.
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Deep Pool is the #1 investor servicing and compliance solutions supplier, providing cutting-edge software and consulting services to the world’s leading fund administrators and asset managers. Our flexible solution suite, developed by an experienced team of accountants, business analysts and software engineers, supports offshore and onshore hedge funds, partnerships, private equity vehicles, retail funds and regulated financial firms. Deep Pool is a global organisation with offices in Dublin, Ireland, the United States, the Cayman Islands and Slovakia. For more information, visit: www.deep-pool.com.