AI in investment management
Finance institutions are embracing artificial intelligence (AI) and machine learning (ML) for a wide range of business applications. Adopting AI is easier than you think. Here’s why.

Adopting AI is easier than you think

Finance institutions are embracing artificial intelligence (AI) and machine learning (ML) for a wide range of business applications.

Adopting AI is easier than you think. Here’s why.

For example, it could be for asset management, fraud detection, robo advisor, trading and stock selection, just to name a few. Advances in predictive modelling tools and increasingly easy accessibility to data have allowed finance enterprises to take full advantage of the benefits of AI and ML in making profitable investment and business decisions.

Though the applications of AI in the Fintech and Finance industry are seeing a lot of benefits, many companies are hesitant to put them to real use. As I’ve mentioned earlier in my posts, AI is always described in abstract terms resulting in a lack of trust in the technology. I am here to bust three myths that will show you how easy it is to implement AI in your business.

Machine learning is not scalable

I think this is one of the biggest misconceptions people have about this technology – that ML isn’t scalable outside the ‘lab’. The fact is, machines learn from massive quantities of data. Finance institutions need the required data processing power to ensure that the ML projects are successful.

The simplest solution is to set up your ML system in the cloud. This way, the ML project can be quickly scaled. It reduces cost and computing resources as well. Brainalyzed Insight’s ML models work great in the cloud.

However, there are a lot of financial institutions that have critical processes that require the ML systems to be on-premise due to the fear of data leakage or theft. In order to scale your ML efforts on-premise, all you need to do is add more of the same equipment that you already use. There is no need to bring in any kind of specialized or networking equipment.

Of course, there are computation costs that come with building or using machine learning, but those costs are dwarfed by the ROI you derive from using the ML model.

When people tell me that ML isn’t scalable, what they are implying is that they are getting the budget approvals because these ‘decision-makers’ weren’t expecting any need for hardware or bandwidth. However, that can be solved if you are able to project and communicate ML production requirements early on in the process. Most importantly, you need to factor in how these costs will win you substantial ROI.

A ‘robot’ cannot be trusted with something as sensitive as finance

I believe there is only one reason for a technology, let alone ML, to be stuck within the confines of a lab environment – how will it fare in the real world. Remember Tay, the AI chatterbot by Microsoft that began hurling racist remarks on Twitter users? Of course, Microsoft hadn’t designed or trained it to behave that way. So all was well as long as it was in the lab. In the real world, it failed miserably.

Additionally, within the lab setting, the machine is learning from historic data. In the real world, it is exposed to new and live data. It’s only natural for finance institutions to be skeptical about implementing AI. But such events belong in the past.

Let me remind you that AI has since then evolved a lot. And there are numerous ways to safely deploy the model. One way to do it is to release the full model for use only to a few customers – a closed ‘alpha’. This way you can observe how your model responds to real-world data and make any necessary changes without affecting your entire customer base.

Another way to do this is to do a canary deployment or release. In this approach, you will release the full model and use it parallelly with an existing (lab) model. This way you can compare the results from both the models to make sure that the behavior of the deployed model is consistent with that of the lab model.

ML model validation is resource and time-consuming

As a financial institution or a finance expert, you might have a lot of experience with how the stock market moves or how assets should be managed. But you might lack the experience when it comes to validating an ML model. There are a lot of aspects to model validation but it boils down to just two questions.

  • Is the model you’ve deployed consistent? Or is the model’s output confusing?
  • Did the data scientist build a logically sound ML model?

These are questions for traditional linear models. They are resource-intensive and gives way to a lot of errors. With Brainalyzed Insight, you can directly deploy and operate the ML model without having to worry about feature engineering. Additionally, a robust AI platform can monitor the model for input and output consistency. This way you save on cost and time.

Closing thoughts

Global finance leaders are confused about the potential of AI and what it can do for the finance industry. However, the technology’s potential to revolutionize the industry is unprecedented. You cannot fully appreciate what you don’t understand. Hence it is crucial that you fully understand what AI is and how it can create value for your business. Now that I have debunked a few myths, you will see that AI is not scary or confusing after all. It is just a smarter machine that will make it easier for you to make profitable investment decisions.

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