Future Proofing the Front Office: AI
Part 3 of a 3-part series on Future-Proofing the Front Office. Want to download the full Whitepaper directly? Get it here!
From a technology perspective, Artificial Intelligence (AI) is currently the most discussed topic on the buy side. AI is to today’s technology conversation what Big Data and the Cloud were several years ago.
With Microsoft’s release of ChatGPT, the discussion of AI has shifted into hyperdrive. Conversations have evolved from being largely technically oriented and focused on use cases for AI, to philosophical discussions involving “sentient” AI and the potential for significant disruption or negative outcomes as demonstrated by some Wall Street firms banning the use of ChatGPT for business purposes.
Hyperbole aside, AI has arrived, is here to stay, and is something that investment firms should be focused on whether they are large or small. AI has practical implications for the entire investment firm, from front to back office. Before embarking on an AI strategy, buy-side firms should understand AI, how it started, and how it has evolved, and then think about the practical application of AI within their own firms.
The Business of AI
Separating fact from fiction is critical for the buy-side front office when it comes to the topic of AI.
The current global AI market is valued at over $136 billion, according to GrandView Research. Additionally, the market is expected to experience a 38% CAGR growth each year, reaching even greater heights by 2030.
If you have ever done an internet search, you are using AI. If you have ever used the voice features of your mobile phone or have used a personal digital assistant in your home or office, you are using AI.
The reality is that AI is already in widespread use for consumer applications like those mentioned above. Applications like ChatGPT will expand this further, plugging AI into the entire IT and software ecosystem. AI is making its way into business applications, including those used for financial services and investment management.
What’s more, by incorporating AI into front-office software, investment managers can streamline operations and harness the power of valuable AI-driven data insights to increase efficiency and speed up portfolio management and trading workflows, thereby creating alpha.
However, it’s important to note that AI is now only possible based on the advancement and widespread adoption of the two other technology disruptors discussed in our Future Proofing The Front Office white paper, i.e., the cloud and data analytics powered by APIs. Now is the time for investment managers to assess how AI for their own specific organizations can be of benefit.
What is Artificial Intelligence?
Defining artificial intelligence (AI) can be a complex task, as the term elicits a variety of interpretations and pop-culture references, from iRobot to our daily digital assistants.
However, it’s important to note that AI first entered our vocabulary back in 1955 at an industry conference. Merriam-Webster defines AI as “A branch of computer science dealing with the simulation of intelligent behavior in computers. The capability of a machine to imitate intelligent human behavior.”
While a broad definition, it’s an excellent basis for a conversation that explores how to use artificial intelligence in investment management. To expand on it further, AI can be considered a general umbrella that contains a variety of subfields, including machine learning, deep learning, voice or natural language processing, and robotic process automation (RPA), among others.
Avoiding a deep dive into the distinction between proven and emerging technologies, it’s safe to say that all subfields of AI share a common goal – to design computer programs that exhibit behavior considered “intelligent” by human standards. These technologies can be considered “smart,” as they process information in a manner similar to the human brain.
As a result, these AI-powered systems can effectively work with a specific set of data, analyze historical and current trends, draw meaningful conclusions, make informed suggestions, and deliver valuable insights, all seemingly automatically. This brings a level of efficiency and intelligence to various processes within your business operations.
AI vs. Machine Learning
So what is the difference between artificial intelligence and machine learning? Machine learning is a subfield of AI that can be thought of as one of its primary building blocks.
To understand the practical application of machine learning and AI, let’s take a look at its use in large consumer-based companies such as Amazon. Each time customers utilize Amazon’s search engine to find products, they provide valuable data that updates the company’s data model. The search patterns are analyzed using machine learning, comparing the customer’s searches and choices to available inventory.
Through these AI-based algorithms, Amazon can then deliver personalized recommendations to its customers, offering a more streamlined and personalized shopping experience.
Machine learning leverages algorithms to help make the comparison. However, if you’re wondering how this relates to using artificial intelligence within your investment firm, let’s dive deeper. On the buy side, algorithmic trading (algos) has been around for a long time within applications that execute trades like OMS and EMS applications. So from that perspective, your firm is probably already leveraging machine learning.
While machine learning (algos) is being used extensively by brokers who deploy them in front-office investment management solutions, they haven’t moved into areas beyond trading. Machine learning has the potential to make other front office areas more efficient in terms of investment decision-making, compliance, and client-facing functions.
The opportunities are wide-ranging. However, as with anything related to investing, it all depends on the data, and this is where the challenge lies.
Machine Learning vs. Deep Learning
Investment decision-making has spawned perhaps the most overhyped aspect of AI at present—deep learning. Deep learning seeks to imitate the workings of the human brain in processing data and creating patterns for use in decision-making. The goal is true predictive analytics, the holy grail of artificial intelligence in investment management—and in every other industry as well.
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of unsupervised learning from unstructured or unlabeled data. It is also known as neural learning or neural networks.
The challenge with deep learning in investment decision-making relates to the extreme variability of one investment strategy to another, the availability of data, which exists both in structured or relational databases vs. unstructured sources that exist out there in the world in terms of the internet, social media, etc.
Big data analytics now makes it possible to aggregate wide-ranging types of data into models that can be applied to deep learning methods. However, to put it bluntly—this type of AI approach is experimental at best. In other words, deep learning, as it stands today, is mostly a solution looking for a problem(s) to solve rather than vice versa within the world of the buy-side front office.
That’s not to say that deep learning doesn’t have tremendous potential for our industry. On the contrary. However, in its current evolution, it’s not a proven solution that wealth managers can confidently use to get results. Instead, let’s explore natural language processing (NLP), another subfield of AI, and its potential to improve front-office investment management operations.
Natural Language Processing (NLP)
Like machine learning, NLP has been around for a while and pioneered by large tech providers, including Google, Amazon, Apple, and now Microsoft with ChatGPT.
As mentioned earlier, performing a Google search employs natural language AI. So does Siri on an iPhone and Alexa in the home. These approaches are widely used in the consumer sector and are a big way in which current and subsequent generations will interact with computers.
Adam Cheyer, the co-founder of Siri, which was sold to Apple, has been quoted as saying that “AI will be the next User Interface.” As a result, it will be as important to the future as the web and mobile have been.
The crux of natural language AI is having a conversational interaction with software using a digital assistant type of approach. Natural language processing has the power to reduce endless amounts of pointing and clicking, repetitive processes, and inefficient workflows based on workarounds and spreadsheets. The resulting operational efficiencies from streamlining time-consuming processes can save endless hours across the front office for portfolio managers, compliance officers, traders, and all of their support staff.
This example perhaps best explains the potential for natural language processing to reshape the buy-side front office. Artificial intelligence for investment management that is specifically designed to solve your firm’s problems helps you gain a competitive advantage.
We believe that front office systems are ripe for AI technology like NLP. Too often, front office OMS/PMS systems are overly complicated with too many clicks, screens, and workarounds for any given process. And in doing so, they have moved away from their core tenant of automating manual processes for the sake of being an all-in-one solution.
Alternatively, the fastest way to get from point A to point B is a straight line. Natural language processing is the practical AI tech that can make that happen.
Other AI technologies like RPA (robotic process automation) are also being evaluated for certain functions. However, INDATA believes that because the front office is very much a people’s business, it will remain that way for the foreseeable future. Robots (or more precisely, robotic programs) will not replace people, however, the practical AI technologies discussed here can make people more efficient, and help them do their jobs faster, with fewer people, and with higher accuracy.
In truth, this should be the overarching goal when evaluating AI for investment management firms.
Use Cases for Artificial Intelligence within Investment Management Firms
A recent study by the World Economic Forum found that 82% of wealth management firms saw artificial intelligence as a high or very high strategic priority. As a result, they predict that AI will become “a major driver of investment returns for asset managers.”
Firms that have already invested time and energy into streamlining their front office systems, may have already reaped the benefits, which will come as no surprise. However, for companies that have not yet begun harnessing the power of AI, failing to do so may mean being left in the past.
However, if you’re unsure what implementing AI in your firm could look like, let’s explore a few examples:
Leveraging New Data Sources
With Big Data creating exponentially growing amounts of information, more data sources are available today than ever before. The challenge is that combing through the data and analyzing it is a monumental task. However, by using AI-based algorithms to interpret this data, countless hours can be saved, and value gained from access to wider datasets.
The buy side thrives on having strong intelligence. With various global data sources emerging that draw on world events, ESG considerations, past decision-making, and choices, AI and ML can deliver better forecasting to empower stronger, data-driven trading recommendations.
Deeper Degree of Personalization
By predicting client questions and enabling stronger virtual conversations, AI can power improved customer service. Additionally, AI can streamline the buyer journey so customers get the most relevant information possible based on their past and current interactions. To that end, AI can watch for specific behaviors, help users overcome potential objections, and move prospects down the funnel faster.
Following the same logic as personalization, AI can use predictive modeling to identify signs that a client may be considering alternative investment firms. Based on those set behaviors or phrases, one can continue to improve their relationship with clients, whether through automated CRM actions or by triggering a flag to reach out directly.
In addition to supporting buy-side trading and portfolio management decision-making, AI can also help firms monitor transactions for suspicious activity. In doing so, identifying potentially problematic transactions and heading them off before they happen, saving compliance teams time and energy, and freeing them up to focus on the most critical items for the firm at a given time.
While these use cases don’t encompass the full potential of artificial intelligence for investment management firms, they do help to consider some of the broad opportunities.
The Future of Artificial Intelligence in Investment Management is Bright
It doesn’t take AI to figure out that artificial intelligence within investment management will play a big role in the future of our industry. Unsurprisingly, it can now yield tangible possibilities and considerable competitive advantage by analyzing large data sets and market trends to deliver key insights for portfolio managers while simplifying trading and operations.
Similar to how buy-side front office developed software to automate workflows for portfolio managers, traders, compliance, and their support staff over the past 20 years, AI will reshape front office operations in the next 20 years. Firms should look beyond the hype and see how practical aspects of AI for investment management can help make their businesses more efficient, contain or reduce costs, and improve their competitiveness.
The reality is that the benefits of AI are only made possible through the widespread use of the cloud and data analytics powered by APIs. These vital and interconnected technologies have a very significant role to play in the future of the buy-side front office.
As is the case in racing, for traders and portfolio managers, seconds matter. AI-powered software will become the norm on the buy side and early adopters will most certainly pass their slower competitors who risk becoming permanent lap traffic.
INDATA harnesses the power of artificial intelligence for investment management to streamline workflows, improve productivity, and deliver industry-leading solutions for firms ready to gain a competitive advantage. Schedule a demo today.