The global data science and advanced analytics industry is expected to catapult in the next decade, with MATLAB and Alteryx taking the top spot for market share. Data has always been a critical part of financial modeling, but the disruptiveness of data science in the industry has yet to be seen.

The modern world is producing more data than ever before, and with it comes an opportunity to gain new insights into financial markets. Data science has the potential to help financial institutions create powerful, predictive models that can be used to drive better decisions and more accurate forecasts.

Data science is also helping by providing a way to capture, store, and interpret data more efficiently. Machine learning algorithms can quickly analyze large datasets to find patterns humans may miss. Let’s look at it in detail.

The Role of Data Science in Financial Modeling

Businesses can leverage data science in multiple ways to improve their financial modeling. For example, data science can help identify correlations between various stock prices or other indicators of market trends. Likewise, it can help identify which stocks are likely to be profitable in the future.

Data science can also help companies forecast their cash flow by predicting customer behavior and other economic indicators. Additionally, machine learning algorithms can be used to predict how certain events may affect the market and what strategies should be taken in response.

Here are some financial modeling branches where data science plays a significant role.

Predictive Analytics and Risk Assessment

Startups are exposed to a wide range of risks, including regulatory authorities, competitors, and market shifts. The use of data science can help predict and anticipate key risks, enabling businesses to take preventive measures in advance.

For example, startups can use data science to estimate Loss Given Default at portfolio or customer levels. Similarly, data science can be used to assess credit risk and potential fraud occurrences.

Portfolio Optimization

Data science makes it possible to optimize a portfolio for maximum profits or minimize losses. Algorithms can develop predictive models to identify which investments will have the highest returns and lowest risks.

Data science can also help answer questions about the stability of a portfolio, such as whether or not it will remain profitable in the long term. Additionally, it can track the performance of a portfolio and suggest modifications when needed.

Assumptions and Valuations

Data science can help generate more accurate assumptions and valuations. For example, data science-driven models can identify correlations in financial data, enabling companies to make more informed investment decisions.

Suppose a company wants to acquire a new business. Data science can allow them to estimate the fair value of the acquisition by taking into account various factors, such as current market trends and the target company’s performance.

Why May Data Science Not Disrupt Financial Modeling?

While data science may have the potential to disrupt financial modeling, there are several reasons why it is unlikely to do so at this point in time. Here are some of them.

Excessive Personalization

Several financial models, such as project and infrastructure models, involve a great deal of customization and personalization. The uniqueness is due to the complexity and nature of these transactions, which often involve multiple parties with varied interests. Such a level of customization is difficult to replicate using automated data science techniques, which are usually geared towards generalization and dealing with standardized data sets.

Availability of Relevant Data

Another key challenge that data science faces in disrupting financial models is the availability of relevant data. To build accurate models, data scientists need access to large amounts of up-to-date and relevant data.

However, businesses are often reluctant to share their financial information with third parties, which limits the amount of data available to build models.

Cost

Data science techniques can be expensive to implement. Companies may be deterred by the cost of setting up and maintaining a data science team. Plus, there are several costs associated with buying or renting data sets. 

How Can Startups Use Data Science to Improve Their Financial Models?

Startups can use data science to improve their financial models by utilizing a variety of predictive analytics and machine learning algorithms. Predictive analytics can provide insight into the future performance of a company, such as customer churn and sales forecasts.

Monte Carlo analysis is a powerful tool for assessing risk and uncertainty in a financial model. The technique involves running hundreds or thousands of simulations based on historical data. It allows startups to incorporate multiple sensitivities and scenarios into their financial models.

Meanwhile, machine learning algorithms can help optimize pricing strategies or detect anomalies that may be indicative of fraudulent behavior. But when using data science, it’s important to note that not all aspects of a financial model can be automated or standardized.

For example, you might be able to automate cash flow forecasts by creating an algorithm that builds on past data, but ultimately human judgment is necessary to determine various risks and assumptions that can influence the accuracy of the forecast. 

Likewise, data science can help you find historical patterns that might be useful for predicting future performance, but it’s important to use common sense and industry knowledge when interpreting these patterns.

Standardization isn’t always an option in startups since they often have limited data and resources. Thus, startups can benefit from customizing their models to fit the specific needs of their businesses. That’s where personalized financial models come in.

Get Customized Financial Models for Your Early Stage Startups

At Numberly, we’re appreciative of the impact data science can have on financial modeling. But our experience with startups has taught us that personalization, rather than standardization, is key when it comes to developing financial models for a company in its early stages of growth.

That’s why we specialize in creating customized, tailored financial models for early-stage startups. Our custom models help startup founders make quick, informed decisions that can significantly impact their business. Schedule a call with us for more information.