Budgeting, planning, and forecasting are key elements in every financial department.
Still, many CFO’s are often contained by stagnant staffing levels as inflexible decision-making processes, outdated data systems, or modernized budget systems that increase the data quality issues. With the newest data science tool, making better budgets and making accurate financial forecasting is now possible.
Financial forecasting helps management to guide the company in the right direction. The analysis gives management insight to make the proper adjustments and creating the best possible strategic plan. Furthermore, financial forecasting is more than useful for budgeting and to improve business performance management.
The ability to ‘guess’ what the future brings, and how much it will cost, how the cash flow will look is one of the essential areas in an organization. The goal is to identify and estimate future revenue and expenditures. With data science, a financial forecast can be more precise than ever before, with up to 95% correctness.
A financial forecast can have an immediate or long-term influence on the company’s strategy and goals, using historical intern data as fx. Accounts, sales, expenses, invoices, salary payments, and external market or economic indicators, is it possible to find patterns or connections there wasn’t visible before. The identified patterns can guide the company with its policies, strategic decision-making, and cashflow management.
Fundamentally, finance forecasting can have a transformative impact on the company’s budgets; it can reduce the manual work in the finance department by automation and improved analysis. The use of finance forecasting gives CFO’s the power to impact the company besides the books but helping the management make the right strategical decisions. Developing data-driven solutions will manage project costs, analyze budget performance, and prioritize allocating funds to maximize the budget’s operational impact.
By integrating various internal and external data sources, a complete picture of the organization’s budget from initial build to execution emerges. The “actionable insights” gives the financial teams enhanced decision-making power and clarity in which directions the company should move.
Predict the future
Data science makes it possible to predict future trends and outcomes based on a large amount of raw data. It gives management more in-depth insight and understanding, allowing them to focus on the activities that will bring the company’s most prominent value.
The main focus is to develop an algorithm with the ability to ‘learn’ and don’t have to be pre-coded or know what the “goal” is. The value creation is based on finding patterns in different datasets as internal datasets mentioned before and external datasets: market research, economic surveys, and industrial economic conditions.
Because data science gives management and finance executives the possibility to understand their various datasets, the proper use of the analysis can produce extract meaning and valuable insight. One of the advantages of using data science to make financial forecasting is that it removes or validate the impact of human bias.
Why use data science for financial forecasting?
Predict the future
Today, data science financial forecasting is the best way to make predictive analysis. With the help from data science, a CFO can answer strategical questions like: How is the net present value or our different projects? What will our internal rate of return be? How is our time value of Money going to chance the next couple of years?
It can answer and give knowledge about a range of simple and complex problems. It identifies patterns, performs descriptive analytics. And it does way quicker and more precise than ever possible for a human.
Using data science, you can handle various datasets with different complexity and still transform the data into actionable insights and knowledge. Furthermore, it can make variance analysis; there is an investigative comparison between planned and actual numbers for labor, material, or overheads.
Data sciences positive effect on budgeting
In the following, we will present some areas where data science can significantly impact budgeting. The examples are from different areas and strategies. Still, they have the same thing in common – how the collected data can help the finance department make a better budget.
Risk management is an essential finance subject. The finance department is responsible for the company’s security, trustworthiness, and strategic decisions. When making risk management analysis, there are many factors to be aware of: competitors, investors, regulators, and the company’s customer.
The central aspect of risk management to identify, prioritize and monitor risks – and it is here that data science can help. The data science tool can increase the risk scoring model and enhance cost efficiency and sustainability by analyzing the different datasets.
Furthermore, data science can identify the creditworthiness of potential customers.
By transforming financial processes by analyzing large amounts of various data, is it possible with data science to quickly identify any real-time changes and find the best reactions to them. We will cover the three main real-time analytics types:
1. Fraud detection
Every company there is saving their client’s information as payment info, name, address, etc., has to guarantee a high-security level. Many companies are standing in front of the challenge to develop the perfect fraud detecting system; there can withstand the criminal’s new hacking ways.
The best data scientist can create an algorithm good enough to detect and prevent anomalies in user behavior or other areas where fraud is possible. A fraud detection algorithm can alert a particular user for unusual financial purchases, fx. will a large cash withdrawal be blocked until the customer has confirmed it.
2. Consumer analyses
This type of data science analysis helps understand customers better. Based on the knowledge, is it possible to create effective and individualized offers for each customer. This data science algorithm can generate customer sentiment analysis, which produces knowledge about clients’ behavior, social media interactions, feedback, and opinions. This analysis makes it possible to improve the individual customer’s offers and predict when and what the customer will purchase. It gives the financial department the insight to indicate how the cash flow will be and provide the insight to optimize products in stock, sales, supplier agreements, and so on.
3. Real-time analyses
The real-time analysis there is the most demanding, but also the one with the most significant impact. By analyzing and compare the most recent data with the historical data, the financial department can make the best real-time decisions. Because the knowledge from the real-time analysis sometimes is only relevant quickly, the insight gives the finance department competitive advantages and clarifies which direction there will be the most profitable in the given situation.
Combining real-time and predictive analytic is it possible to forecast market opportunities.
By processing tons of data as; information, tweets, financial indicators, news, books, and even TV-programs, is it possible to understand worldwide trends, which gives the financial department the insight to predict and optimize its budgets continuously. 1.
All in all, real-time and predictive analytics significantly change the situation in all the different aspects of finance and budgeting. For finance departments, data science techniques provide a great opportunity to understand the company’s future finance, which will give the company the chance to stand out from its competition and reinvent the business to be the best as possible.
Today there is a vast amount of continuously changing financial data. The sooner every finance department is adapting to the new opportunities data science brings – the sooner it will reach new highs.
Borbaki can help you reach new highs with the use of data science.
Curious to hear more? Send us a mail, and let us have a talk over a cup of coffee.