What is the Role of Machine Learning in Brokerages?

Unpacking аutomation, challenges, and unnovations.

Machine learning (ML) automates complex processes in brokerages. It also improves operational efficiency and offers personalized experiences for traders. 

But, what does this actually mean? 

In this blog, we zoom in on this high-level definition and expand on what machine learning does for brokerages, with examples of automation processes, challenges, and innovation within a real brokerage. 

Machine learning in a nutshell 

Machine learning is a type of artificial intelligence that teaches computers to think in a way similar to humans: learning and improving upon past experiences. It involves algorithms and statistical models that discover patterns and insights from data without being directly programmed where to look.

By analyzing and learning from this data, machine learning algorithms can make predictions or decisions, effectively allowing computers to learn from past experiences and detect patterns or insights that are often imperceptible to humans.

In plain terms: machine learning is like giving a computer a set of examples and letting it learn and make predictions based on what it has seen before.

Automation

Machine learning automates the bread and butter of brokerage operations—from risk assessment to trading, analytics, and client support. 

For instance, predictive models can analyze historical data to fine-tune risk parameters, reducing the likelihood of significant financial losses. 

Here are further examples of processes and functions that machine learning helps to automate within a brokerage. 

Predictive analytics for identifying investment opportunities

Because machine learning algorithms can analyze vast amounts of market data, they can predict stock price movements, identify trends, and make recommendations. 

Fraud detection and prevention

Machine learning models can detect unusual patterns and potentially fraudulent activities by continuously monitoring transaction data. 

This is a significant benefit, as it allows firms to flag these suspicious activities in real-time, protecting both themselves and their clients. 

Algorithmic trading

High-frequency trading platforms use machine learning algorithms to execute trades at optimal prices. 

These algorithms can analyze market conditions in milliseconds to make quick decisions that maximize returns. 

Customer service and support

Chatbots and virtual assistants powered by machine learning can handle routine customer queries and provide instant responses around the clock. 

This improves customer satisfaction and frees up agents to focus on more complex issues.

Learn more about machine learning chatbots dedicated to banks and brokerage firms here. 

Regulatory compliance monitoring

Compliance with financial regulations is critical for brokerages. Machine learning helps by monitoring and analyzing transaction data to make sure it aligns with requirements.  

It can also streamline client onboarding and KYC processes by quickly processing large datasets to verify client information against criteria, significantly reducing the time and manpower typically required. 

Challenges

Integrating machine learning into brokerage operations isn’t without its challenges. 

A prominent challenge is ensuring data privacy and security. Machine learning systems need access to vast quantities of data to train effectively and make informed decisions. 

So, you can imagine, that if there aren’t strict and effective protocols in place to prevent unauthorized access and data breaches, this could quickly become a very murky area with risks of penalties and breaking client trust. 

This is amplified by the fact that brokers handle particularly sensitive financial information. 

Brokerages need to consider processes like data encryption, access authentication, regular audits, data anonymization, and sharing practices. If a brokerage doesn’t have the resources to do this effectively, they need to make sure their platform or software vendor can demonstrate they have these in place. 

A second challenge is the accuracy of ML predictions—they can sometimes be inconsistent, especially in volatile markets. 

Market conditions can swing dramatically due to unforeseen events like political instability, or global crises, all of which can render previous training data obsolete or less relevant. And, machine learning needs to be resilient and perform in these scenarios. 

This requires ongoing training and refinement of algorithms, ensuring the models are fed with high-quality, up-to-date data, and incorporating feedback loops. 

Innovations

Let’s take a look at a real example. Consider the case of Invest.inc, which partnered with Devexperts to launch an advanced ML investment platform. 

This platform demonstrates how machine learning can tailor services to individual investor needs, using algorithms to adapt strategies based on real-time market conditions and user behavior. 

As a result, this brokerage now offers a level of personalization and tools to their traders, that were previously reserved for the industry elite.

Learn more about this first-of-its-kind project here. 

Our role

As fintech software developers, we are in touch with current advancements and technological trends. 

We know that machine learning is having a transformative impact on industries globally. 

As a result, the benchmark for efficiency and intuitive user experience has skyrocketed. 

That means it is an extremely important part of our research and development at Devexperts.  We make sure we place adequate focus to ensuring our experts and teams are amongst the best at understanding, creating, and deploying advanced machine learning systems within brokerage solutions. If you would like to chat to someone in person on this topic and discuss how it applies to your firm specifically, feel free to reach out to us here.