In 2021, the use case frequency of artificial intelligence and machine learning improved by leaps and bounds. Machine learning models are now being used to automate forecasting, develop predictive models, analyze trends and patterns, detect anomalies, segment customer profiles, and even provide personalized advice.

It can also make more accurate predictions about a particular market condition or a customer’s behavior in it. As a result, it helps financial institutions better manage their portfolios and stay ahead of the competition.

Below, we look at the potential revolutionizing of the finance space via machine learning.

What Is Machine Learning?

In the simplest terms, machine learning is the science of teaching machines or algorithms to improve their accuracy through experiences and data. The goal is to be able to predict the future accurately.

In the financial sector, machine learning can automate functions that use complex data sets and large amounts of information. It enables professionals to make better decisions and get improved performance from their portfolios.

For example, machine learning can build predictive models that identify market trends, suggest the right investments, and analyze market volatility. It can also detect fraud in real time and provide personalized customer advice based on customer data.

Why Should You Use Machine Learning in Financial Models?

It shouldn’t come as a surprise that machine learning is being applied to the world of finance. But why should you jump on the bandwagon? Here are some reasons.

• Better Compliance: Since machine learning algorithms can process large amounts of data quickly and accurately, they can identify financial risks more effectively than traditional methods. Consequently, you can detect and address any compliance violations, reducing the need for costly manual labor.
• Improved Risk Management: With the help of machine learning, you can better assess financial risk, allowing for more timely decision-making.
• More Accurate Predictions: Projections and assumptions make up a huge part of financial models. With machine learning, you can make accurate predictions based on real-time data from a variety of sources.

Use Cases of Machine Learning in Creating Financial Models

Machine learning has plenty of uses for businesses operating in every sector. Here’s how you can use it when creating financial models.

Risk Management

When creating a financial model, it is important to consider all the potential risks that could arise. Machine learning can help with identifying and quantifying these risks by analyzing patterns, trends, and data from previous projects or by monitoring market conditions in real time.

Suppose you’re an e-commerce company selling apparel. When you prepare a financial model for investors, you must consider the risk of seasonal changes in demand. Machine learning can help you analyze past sales and customer behavior to predict how much inventory you need to maintain a healthy cash flow during different times of the year.

Predictions and Assumptions

Most financial models are based on assumptions about the future. Machine learning can allow more accurate predictions by analyzing large amounts of data from various sources, such as:

• Historical market data
• Economic trends
• Political events
• Social media sentiment

The ability to analyze large amounts of data and make predictions based on it allows for more efficient decision-making. Businesses can use machine learning to identify potential opportunities in the market.

For instance, a SaaS company may use machine learning to identify which products and services are most likely to be successful in a certain market.

Process Automation

A lot of accounting and financial processes can be automated with machine learning. Automating these processes allows businesses to save time, money, and valuable resources by streamlining operations.

One example is robotic process automation (RPA) which can automate much of the manual labor associated with financial tasks such as invoice processing and accounts payable.

Machine Learning Challenges in Financial Modeling

While machine learning can facilitate the process of financial modeling, there is a range of challenges that companies should consider before implementing such models.

Auditability and Security

According to Statista, 56% of companies that adopted machine learning saw security issues. Building a secure model requires companies to anticipate the system’s potential attack surfaces, understand the users’ permissions and access rights, and ensure that proper data encryption is in place.

Companies also need to keep track of all changes made to the model and ensure that the data used for training is reliable. It requires a lot of resources and careful auditing and evaluation processes.

Data Availability and Quality

Inaccurate data can lead to inefficient and unreliable models. Data quality is often defined by accuracy, completeness, integrity, and timeliness.

Financial modeling requires large datasets with accurate and up-to-date information. However, data mining is expensive and time-consuming. Companies must ensure that the data they use is clean and relevant, often requiring manual intervention.

Legal Compliance

Financial modeling algorithms must comply with the laws and regulations of the country or industry. Additionally, models should not be biased based on factors such as gender, ethnicity, or age. Companies must also consider privacy regulations and make their models adhere to them.

Model Interpretability

Machine learning algorithms can predict outcomes effectively, but they are often complex and hard to interpret. That means it is difficult to explain why the model has made a particular decision. It can be particularly challenging when justifying decisions that have significant financial implications.

Machine Learning vs. Professional Intervention: What’s Better?

Yes, it’s true that machine learning can help in creating financial models. But it’s merely supplemental. You cannot eliminate the human element entirely. Professional intervention is still necessary when it comes to interpreting the data and making decisions.

In addition, machine learning on its own cannot provide insight into the level of complexity and risk associated with a financial decision. Professional expertise is necessary for making decisions that are in line with the company’s risk appetite.

At Numberly, we keep the human touch intact. Our professionals create customized financial models for your startup based on your specific needs, requirements, financial maturity, and risk appetite.

Check out this walk-through of our financial models to check out the components we include in our models.