AI and Machine Learning: Practical Applications for Asset Management Firms (Part 1 of a 3-Part Series)

AI. Machine learning. When you hear those terms, what comes to mind is a science fiction movie where machines achieve consciousness—and trouble ensues. At best, you might dismiss the concepts as hype or the buzz word of the day. It’s true that the AI/machine learning landscape feels a bit like the Wild West right now, with opportunists promoting solutions when, in fact, they don’t actually have a product—perhaps just some roughly defined services—but no technology to back it up.

AI & Machine Learning for Asset Management Firms

Regardless, AI and machine learning are very real—and very relevant to asset management firms. Unfortunately, traditional asset management and CRM (customer relationship management) systems like Satuit, Protrack, or Salesforce don’t have the ability to deliver the benefits of AI and machine learning.

With the right cloud solution for asset management, in the hands of qualified experts who understand your industry, your firm can realize very impressive benefits. Regardless of your business—private client, institutional, or retail/wholesale—you’ll have more ways to be more productive and efficient while delivering more value and strengthening your most valued relationships.  

This blog series discusses three scenarios in which AI and machine learning can address common challenges you face:  

Part 1:  Managing Wholesaler/Retail Line of Business – If your sales people don’t have insight into how their interactions with advisors, branch offices, or broker dealers are affecting sales outcomes, they are operating in a vacuum and can make mistakes or miss opportunities. 

Part 2: Managing Institutional Pipelines and RFPs – Chasing opportunities and responding to RFPs can be difficult and very time consuming—and often do not pan out. You need to know which ones to focus on and which to let pass for maximum ROI and increased AUM.

Part 3: Trip Planning – Wholesalers and account managers deal with the cumbersome and time-consuming task of trip planning. With hundreds of possible options for client/prospect visits in a given area, they need a way to choose those opportunities that offer the most potential ROI.

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Part 1: Managing Wholesaler/Retail Line of Business

On a daily basis, wholesalers must sift through thousands of lines of sales and trade data, broken out by advisor, branch office, and the home office. How do you derive true meaning from all this data? With so many accounts and transactional data, coupled with the fact that there is no direct relationship with the end client, it’s difficult if not impossible to identify trends and how the data you’re gathering might impact the overall relationship with advisors. In short, there is no visibility into changes in client positions.

The goal for any wholesaler is to see at a macro level trends of their products on the advisor level. With any given wholesaler dealing with thousands of advisors, it’s also difficult to determine who to call, when to call, and what action to take. Because they are unable to see individual financial accounts and flows, they depend instead on data summaries, summarized by advisor/branch/broker dealer and product/strategy. With these summaries, they can see:

  • Which financial advisors, branches, and broker dealers have the largest holdings by product
  • Flow information (summarized monthly) by the same groups mentioned above

This is where the time-consuming, less-than-reliable step comes in. Your sales people must look at both of these data sets (which are often very large) and attempt to aggregate the information as best as they can. Despite their best efforts, sales people are human. They make mistakes, they can’t catch every possible variation, exception, or trend, and they are subject to observer bias.  

With AI and machine learning, that task of aggregating is taken out of the hands of the sales person and handled by technology. It quickly and thoroughly analyzes the data sets together and provides unbiased, logical, recommended actions. The result: better decisions that drive better interactions, a time savings of 2-3 hours per day, and the added benefit of identifying at-risk clients or cross-selling opportunities. 

AI and machine learning defined

To understand how this works, we need to start by defining the two concepts:

Artificial Intelligence (AI) is the overall concept of tasks that would require intelligence in a human being carried out by machines in a “smart” way. The machine is able to optimize how a task is done and actually carry that task out.

Machine Learning (ML) is a way to accomplish AI. It is the process of “training” a compute–rather than writing code–by feeding it data to help train it. The machine will “learn” the best way to accomplish that task, and it will keep learning and becoming better at what it’s doing as it continues to work with more data.

With machine learning, you simply feed the machine every relevant factor you are aware of. This information is combined with what the machine already knows about past performance, market events, etc. The output is a carefully calculated ranking, optimized to deliver the highest success rate. In addition, the machine can take volumes of information and boil it down to a solid recommendation faster than an entire room of humans.

Intuition, backed by dependable data

Asset managers depend on their intuition and experience. That is what makes them good at what they do. But intuition can be affected by many human factors. What if you could take that intuition and test it, confirm it, and perfect it with AI?

Study after study shows that machine learning improves results significantly. By using more quantitative data with multiple variables that you define, you get better, more dependable recommended actions.

Surprisingly simple, surprisingly cost-effective

Believe it or not, incorporating AI into your processes is pretty simple and cost effective. You might not believe that any new technology could be either, but it’s true IF you have the right solution and a smart partner implementing it. It doesn’t involve learning a new application or complex manipulation of data. The system does the work, and you get the benefits. And as the data sets grow, the system “learns” and gets smarter and smarter, narrowing results and increasing the likelihood of a positive outcome.

See machine learning in action

Technology’s purpose is to make what you do easier and better. You’re doing things right—it’s just that they can be done “right-er.” Let the right technology take what you’re doing and super-charge it in a way a human can’t. Far from science fiction, machine learning is a real, applicable ability that can elevate your business far beyond what you can imagine. 

In part 2 of this blog seriies, I discuss how Machine Learning and AI are used by asset management firms for managing institutional pipelines and RFPs.

To see machine learning in action, talk to the Financial Services experts at AKA Enterprise Solutions.

By | 2018-10-19T15:57:32+00:00 August 9th, 2018|Machine Learning/AI, Sales & Service (CRM)|0 Comments
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Contributor: Bradley Monnet

With a background in finance, finance operations, and working as a CRM analyst in the financial industry prior to AKA, Brad helps lead AKA's Financial Services strategy as a Senior Consultant.

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