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Financial modelling proves to be a powerful tool in business. Although the future is uncertain, the financial modelling process can be used to provide insights and forecast what is likely to happen tomorrow based on decisions being made today.

We’ll cover everything there is to know about why you should use financial modelling, various financial modelling techniques, and software tools that can make it all a breeze.

Coming Up

1. What is Financial Modelling?

2. What are the Objectives of Financial Modelling?

3. How Does Financial Modelling Work?

4. Who Uses Financial Models?

5. Is Financial Modelling Difficult?

6. What are Best Practices for Financial Modelling?

7. How Do You Build a Financial Model?

8. What are the Types of Financial Models?

9. How Data Modelling Works with Automation?

10. How to Improve Data Insights and Analysis?

11. How To Transform Data More Effectively?

12. The Bottom Line

What is Financial Modelling?

Financial modelling is a process that summarises a company’s profits and costs in an effort to determine the impact of future events and decisions. It allows for leaders to make decisions now using data that’s available, without having complete information about what is yet to come.

The data involved includes the past, present, and forecasted future. These insights are what empowers executives and stakeholders to make the best business decisions based on what is currently known.

They may also be used to estimate the valuation of a business or conduct competitive analysis against other businesses in the same market.

What are the Objectives of Financial Modelling?

There are many uses, or objectives, for financial modelling. From financial analysts to company executives, these informative spreadsheets and calculations are heavily impactful.

Some of the main objectives include:

  • Raising capital
  • Providing a valuation for a business
  • Budgeting
  • Forecasting
  • Selling assets and business units
  • Acquiring businesses or assets
  • Performing financial statement analysis
  • Allocating capital and resources
  • Growing the business
  • Implementing strategy
  • Calculating project costs

Financial modelling works off of assumptions. Along with good assumptions, a key to accurate modelling is the data you use to carry out the calculations. The model is only as good as your input.

So, it’s always beneficial to utilise an automation software that can pull data from all sources and secure it safely in a centralised location for use.

How Does Financial Modelling Work?

Financial models are made up of historical data, assumptions, and calculations that work together to predict the future. Financial models are not simply spreadsheets. While Excel is often used to create a financial model, there are crucial aspects that make up a financial model and how it works.

The main characteristics of how a model works is that it:

  • Is Dynamic: Each input will affect the calculations and results, or output. In order to understand the impact of variables, models are created to be dynamic with built-in flexibility.
  • Displays forecasts: They are used to predict the future based on different variables that could take place. For example, what will your business cash flow be in ten years if you grow by 5% yearly?
  • Relationship-driven: If you change one input, then multiple variables will change in line with that single change.
  • Is structured: There are different types of financial models, but they all consist of inputs, scenarios, calculations, and outputs.

What are some Examples of Financial Models?

Financial models are in the world of business all the time. Since the future is unknown and decisions today can greatly affect what’s to come, it’s of great use to have the data available to gauge different scenarios.

Financial models are commonly used to predict future sales growth. With just two main components, namely the prior year’s sales and current year’s sales, a formula can be applied to forecast next year’s sales. Additionally, financial models can be utilised for cost reductions, cash flow, and mergers and acquisitions, to name a few use cases.

The application of different models can provide decision makers with valuable figures, such as the Net Present Value (NPV) of a business or the internal rate of return (IRR).

Who Uses Financial Models?

Many players within businesses will rely on financial models. For example, company executives will utilise such models to make key decisions on whether or not to move forward with a new project. They’ll use the model to estimate potential profits and costs. Then, they can conduct cost/benefit analysis to see if it’s worthwhile and in the company’s best interest to pursue.

Financial analysts use these models to understand and explain how a variable will impact the business. To exemplify, they can analyse how internal or external factors, like regulations or economic policies, may affect the company’s stock price.

Is Financial Modelling Difficult?

Financial modelling may sound difficult and hard to do at the get go, but it doesn’t have to be. Through practice or professional training, you can learn the ropes and become an expert.

Additionally, the aid of technology has made financial modelling easier than ever before. Rather than having to conduct models manually through spreadsheets like Excel, you can utilise financial modelling software to prevent errors and streamline the modelling process.

Data automation software improves the quality and access to your data, all the while making it possible to process data in a fraction of the time it would take to fulfill the same tasks manually.

Financial modelling tools make it simple to visualise models, perform analysis, and even present the findings in an easily understandable manner.

What are Best Practices for Financial Modelling?

Financial modelling and spreadsheets typically go hand-in-hand.

Whether you are going to utilise the aid of automated software solutions or not, you can benefit from this list of best practices for financial modelling:

  • Excel: When building your model in Excel, you can benefit from time-saving shortcuts and tricks. Some of these include: keeping formulas simple, breaking up calculations into steps, using the CHOOSE function to build scenarios, and taking advantage of keyboard shortcuts.
  • Format: There’s an unwritten rule about how to color code your inputs and outputs in a model. For inputs, or assumptions, you can color in blue. Formulas can  be input in black. Another option is to use shaded cells or borders.
  • Structure: Make sure your model follows a logical flow and design. The typical components in order will include: assumptions and drivers, income statement, balance sheet, cash flow statement, supporting schedules, valuation, sensitivity analysis, and charts and graphs.

How Do You Build a Financial Model?

Financial modelling can be broken down into a series of steps. When combined, it creates a unified and cohesive picture of what you can expect in your business.

While every analyst or professional may have their own method to create a financial model, the typical steps follow in this order:

  1. Historical Data: Every model relies on historical data. You’ll want to pull three years of financial statements. Through calculations, you can extrapolate assumptions for each historical period and use that to fill in the assumptions for the future.
  1. Income Statement: Now that you have forecast assumptions, you will be able to calculate the income statement’s top half, which consists of: gross profit, revenue, COGS, and operating expenses. You can fill out everything up until EBITDA at this point.
  1. Balance sheet: Move onto the balance sheet by calculating inventory and accounts receivable.
  1. Supporting schedules: You can’t finish up the balance sheet or income statement without creating a schedule for capital assets and debt and interest. You will be able to fill in the Property, Plant, and Equipment (PP&E) based on adding capital expenditures and subtracting depreciation from the historical period. Interest can be based on the average debt balance.
  1. Complete statements: Use the information gained from step 4 to finish filling in the income statement and balance statement. To calculate shareholder’s equity, pull last year’s closing balance, subtract dividends and shares repurchased, and add net income and capital raised.
  1. Build cash flow: Now that your statements are filled in, you can build the cash flow statement using the reconciliation method.
  1. Perform analysis: This 3 statement model leads to calculating free cash flow and a business valuation.
  1. Add scenarios: A critical step in financial modelling is understanding how much the assumptions you’ve made will actually impact the value of the company. You can better understand this through sensitivity analysis and adding scenarios into the model.
  1. Create charts and graphs: To visualise and communicate the results of your model, you can create charts and graphs. Executives rely on these overviews to understand their decisions, rather than being bogged down by the line items and calculations.
  1. Perform stress test: Importantly, your final step is making sure that the model will perform as you expect it to. At this step, you perform stress-tests, or apply extreme scenarios to the model.

What are the Types of Financial Models?

Financial models come in different shapes and sizes. The most commonly used financial models are as follows:

1. Three Statement Model

The most basic of the financial models, the three statement model is built upon the income statement, balance sheet, and cash flow. You link the three with formulas and can test various assumptions and their effect on these financial statements.

2. Merger Model (M&A)

Like the name implies, the merger model is used to analyse the accreditation or dilution of a merger or acquisition. Investment banking and corporate development utilise this model often.

3. Initial Public Offering (IPO) Model

Before a company goes public, it can leverage the IPO model to value the business. It helps to understand how much investors will be willing to pay for the company.

4. Budget Model

For financial planning and analysis, the budget model can be used to allocate resources on a monthly or quarterly basis.

5. Sum of the Parts Model

By mixing DCF models together, you can use the sum of the parts model to calculate the Net Asset Value of a company, for example.

6. Option Pricing Model

Mathematical criteria make up the main two option pricing models, which are Black-Scholes and binomial tree. These models are straightforward given the mathematical formulas.

7. Forecasting Model

Like the budget model, the forecasting model is used commonly in financial planning and analysis. Oftentimes, the two will be combined in the same workbook.

8. Consolidation Model

The consolidation model withholds information about different business units on different tabs. Then, you can use the consolidation tab to sum up business units. In this sense, it’s similar to the Sum of the Parts model.

9. Discounted Cash Flow (DCF) Model

The Discounted Cash Flow is based on the 3 statement model and is used to deduce the value of a company using the Net Present Value (NPV) of its future cash flow. Professionals working in equity research and capital markets rely on this type of model.

10. Leveraged Buyout (LBO) Model

This advanced type of financial modelling is highly detailed because it uses cash flow waterfalls and circular references. Someone trying to create a LBO model will need to understand debt schedules to do so.

The above models represent an abbreviated list of financial modelling options. Regardless of the type of model you wish to create, you can benefit from using automation solutions to help source, transform, format, and secure the data you need to reap the insights you seek.


How Data Modelling Works with Automation?

There are different stages at which you can implement automation within your financial modelling exercises. Given the nuances involved, an entirely automated process may not be what you actually want, you save time and reduce errors by incorporating automation along the way.

For example, in the first step of financial modelling, you have to pull financial data from three historical years. This can be a time-consuming step if your data exists in various locations and has to be organised. Instead, you can opt to automate the data processing and input step.

What is Data Processing?

Data processing is the steps needed to prepare raw data and transform it into valuable insights. Whether conducted manually or automatically, the data processing cycle typically flows as follows:

  • Input stage: Data collection, data capture, encoding, data transmission, data communications
  • Processing stage: perform instructions, transform data
  • Output stage: decoding, data presentation
  • Storage: secure storage and retrieval

Benefits of Automation vs Manual Processing

By using an automation tool, you will be able to process data more quickly and accurately. You can also rely on the system to locate and pull data on an as-needed basis. This results in:

  • Error reduction: Manual data processing and inputs is highly error-prone by nature. One single typo or miscopied number can completely change your entire financial model and affect your decisions.
  • Time savings: Dealing with data is time-consuming because it involves a high level of attention and focus. Automation software can batch process bulk data in seconds, saving your team time.
  • Lower costs: Instead of having to hire highly-skilled data analysts to perform repetitive tasks, you can utilise automation software to manage these processes instead, thereby saving money.

How to Improve Data Insights and Analysis?

Financial modelling is data in action. It’s a way to conduct analysis and glean insights from raw data. While every business collects data, not every business knows how to put it to good use.

The power lies within analysis. And, in this brief section, we’ll shed light on how to take advantage of automation to reap the game changing benefits of analysing data properly.

What is Data Insights and Analytics?

Data insights is, in essence, the value of data analytics. Insights refer to what you learn by analysing data. This form of information is then used to make decisions or design strategies for success. Insights can come in the form of highlighting areas ripe for improvement within your business, or they may help you better understand how to allocate resources for a new project.

Data analytics are mathematical algorithms and statistical models that are used to transform raw data into insights by way of uncovering patterns and trends.

How to Achieve Greater Data Insights?

While the first step in achieving optimum data insights resides in collecting quality data, there’s a lot more to the equation than meets the eye. You’ll also need to focus on:

  • Using the right data: begin by defining your goals and then assessing what information you need to achieve them.
  • Asking questions: involve your team and stakeholders in the process.
  • Utilising visualisation tools: having a lot of numbers and spreadsheets can be cause for confusion if you don’t display the data visually so that you can convey messages.
  • Understanding context: you’ll need to avoid using data that is irrelevant to the problem you’re trying to solve or answer you’re seeking.
  • Breaking down silos: since everyone on your team has a different perspective, be sure to break down barriers and involve various departments.

How Automation Helps with Insights?

The aforementioned best practices seem doable in theory, but when you get into the practical application, there are many steps that can go awry if you conduct operations manually. Since data moves at a lightning pace, you need to ensure that it’s always up-to-date and of good quality.

Let automation tools do the heavy lifting. Automation tools are able to: collect correct data within context, run continuous models that result in timely analytics for agile decision-making, create tailored reports that can be sent automatically at your desired frequency, and ensure that all sensitive data is securely stored and transferred between parties.

How To Transform Data More Effectively?

There’s no denying the importance of data when it comes to financial modelling and analytics. In an effort to manage data effectively, data transformation must occur.

Data Transformation Process

The goal of data transformation is to move data from its source to its final setting, or go from source data to target data.

Data can be transformed manually or automatically. Regardless of how your business manages data, the steps will involve:

  • Data discovery
  • Data mapping
  • Code generation (workflow)
  • Code execution
  • Data review

How to Transform Data

There are different ways to transform data, and the method that you choose will depend on your end goals and usage for the data. Some of the data transformation options include:

  • Scripting
  • Translation and mapping
  • Aggregation
  • Enrichment
  • Extraction
  • Index and ordering
  • Anonymisation and encryption
  • Modelling, formatting, and renaming

Best Practices to Transform Data

With the goal of either moving data to a new system or making data compatible with existing data sets, many organisations will leverage the aid of automation tools. Software solutions like SolveXia can connect all your data systems, clean your data, and map the raw data to transform it into usable insights.

Consider these best practices when performing data transformation:

  • Set your goals from the start so you can model the data accordingly
  • Perform data profiling so you can choose the necessary data sources
  • Cleanse data
  • Structure the data to match the target format
  • Record your actions and review data quality

How Automation Helps Transform Data

All your data needs can be carried out and conveniently managed with automation software. These tools save your business time and money. Automation will transform data and provide you with a host of benefits, like:

  • Saving money: cloud-based software prevents the need for complex coding or extensive IT teams
  • Running quickly: data is loaded, extracted, and transformed in real-time
  • Reducing errors: without human intervention, you have lowered the chance for manual errors
  • Staying secure: all data is securely stored and transferred between parties with access control
  • Offering support: you always have a team available to assist should you have any need

The Bottom Line

For the many uses of financial modelling, there’s an automation tool that can help you access, collect, store, transform, and utilise your data. With the ability to utilise data for insights, you can make optimal business decisions in a timely manner that can help to transform your business for the better.

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