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How Automation is Streamlining Financial Processes

Here is how automation is revolutionizing corporate finance and streamlining otherwise lengthy processes.

Here is how automation is revolutionizing corporate finance and streamlining otherwise lengthy processes.


It's an understatement to say the world of corporate finance is being transformed. The transformation has largely taken place as automation is involved in almost every aspect of a business. Now, the role of automation is to continue to innovate and improve existing processes. From automating routine tasks like data entry and reconciliation to using advanced analytics and machine learning to identify patterns and insights, automation enables finance teams with unprecedented productivity levels and leaves more time for creative thinking.

This article will explore some of the cutting-edge technologies available to CFOs today and present some use cases to consider.

The Benefits of Automation in Corporate Finance

Improved Accuracy and Efficiency

The cost of human error is a figure that companies are wise to acknowledge as they consider investments in new technologies. Automation technologies drastically reduce the amount of error and increase the efficiency of the function they support. Even the most talented Controllers make mistakes from time to time. These technologies allow finance teams to identify patterns and anomalies faster, reducing workload and redirecting effort toward analytical tasks. 

Faster Decision-Making

Automated Financial Planning & Analysis (FP&A) tools help finance teams create dashboards and reports faster than traditional, manual methods. This allows strategic decision-makers access to faster information to capitalize on growth opportunities and cost-cutting measures. These tools can recognize trends in historical data that are difficult to see with manual efforts alone, providing more clarity into the story behind the data. 

Enhanced Risk Management

Fraud detection tools are available to rapidly parse enormous volumes of data to identify and flag possible fraudulent activities. These tools are having a significant impact in helping companies reduce financial loss and reputational damage.

Companies can mitigate market and credit risk through automated risk management systems that analyze market trends and credit reports, providing real-time monitoring and alerts. These tools allow for better visibility into markets and portfolios that provide insights otherwise unavailable to those needing them. 

Companies are also empowered to ensure compliance with regulatory bodies and laws. Automation can help standardize processes, provide real-time reporting on compliance metrics, and create audit trails.

Advanced Technologies in Corporate Finance Automation

Robotic Process Automation

Robotic Process Automation is one of the most prevalent technologies implemented today. RPA began to see a rise in large organizations throughout the 2010s. Today, large organizations have thousands of bots deployed throughout the company, and the number of midsize companies without these bots continues to shrink. 

RPA involves using robots that are developed to perform repetitive, rule-based tasks otherwise performed by humans. RPA is used in conjunction with other technologies, such as Optical Character Recognition (OCR) and Natural Language Processing (NLP), discussed below, to automate a growing variety of tasks. RPA is widely used for data entry in its most basic applications, but in combination with cognitive technologies, RPA is being deployed into more complex workstreams. 

Optical character recognition

Optical Character Recognition, or OCR, is a technology that allows computers to convert printed or handwritten text into a machine-readable format. When fed a document or image, the software scans for an area of text before converting it to a format that makes it searchable, editable, and analyzable. OCR processes data through feature analysis, pattern recognition, and other algorithms. 

Natural Language Processing

Natural Language Processing (NLP) allows machines to interpret and generate human language. NLP can be combined with Automatic Speech Recognition (ASR) technology to transcribe audio to text, further increasing access and the number of use cases. Through syntactic parsing, part-of-speech tagging, and semantic analysis, NLP enables machines to rapidly comprehend large volumes of text. NLP can also determine sentiment in a piece of writing, contextualizing the text's emotion and other subjective elements. 

Internet of Things

The Internet of Things (IoT) refers to an interconnected network of devices used as mediums for data collection and exchange. With devices talking to each other, companies are empowered to introduce more intelligent interactions and seamless communication between technologies. 

These devices communicate via Wi-Fi, cellular networks, or Bluetooth. Some applications include sensors on a device, monitoring temperature, light, humidity, motion, and other environmental factors. IoT deployments can be enhanced through edge computing, further benefiting the network's speed, security, and reliability. 

The Difference Between Artificial Intelligence and Automation

Though they are often used in tandem, automation in and of itself does not fall under the umbrella of artificial intelligence. Artificially intelligent technologies are supported by algorithms that exhibit intelligent behavior, simulating human-like cognition. AI systems are designed to understand, reason, learn, and make decisions based on data and patterns. Of the technologies listed above, NLP would be the only technology that can be considered a form of artificial intelligence by itself.

Automation involves programming technology to perform tasks without human intervention. The goal is to increase efficiency and reduce errors using software, robotics, or other technologies to reduce human effort with machine-driven actions. While automation can be rule-based and repetitive, AI technologies, including machine learning, allow systems to learn and adapt from experience, improving their performance over time. Automation and AI combined can make a powerful tool capable of learning and making decisions.

Use Cases for Automation in Corporate Finance

Financial Reporting

Before data can be analyzed and reported, it must find its way into the financial system. Before that, it must be gathered, consolidated, and validated from different sources. All of the above can be automated by combining RPA and ETL tools. Through automation in financial reporting, professionals can spend more time analyzing reports and extracting insights and less time performing repetitive tasks.

Asset Tracking

Asset tracking involves monitoring the location, status, and condition of a company's physical assets, from equipment and machinery to vehicles and mobile devices. Traditional methods of asset tracking include manual logging and spreadsheet-based systems. IoT devices can automate the real-time transmission of data on an asset's location, usage, and condition, providing real-time visibility and enabling proactive management of assets. 

Asset tracking has many benefits in supporting a company's asset management. By monitoring utilization, companies can identify underutilized assets and inform decisions on asset allocation, procurement, and disposal. Location monitoring can help prevent theft by sending alerts if an asset goes missing or exits a predefined area. Companies can also save significant costs through predictive maintenance. By closely tracking asset health, companies can anticipate the need for repairs and maintenance, reducing downtime and diagnostics expenses. 

Fraud Prevention

Automation plays a crucial role in modern fraud prevention strategies. Consider a scenario where a credit card is used for several high-value transactions within a short period. An automated transaction monitoring system can flag these activities as suspicious based on the unusual frequency and amounts. This real-time detection allows the bank to temporarily block the card and contact the customer for confirmation, helping prevent loss.

Predictive analytics is another powerful tool in fraud prevention. By analyzing historical data and identifying patterns associated with past fraud cases, predictive models can forecast potential fraud before it occurs. For example, if a series of transactions originating from a specific IP address were associated with fraud in the past, the system could alert the security team when a new transaction is initiated from the same IP.


When a security breach occurs, companies with security AI and automation implemented to protect their company save $3mm on average in breach losses. Considering that number is average, the amount your company may save could make or break your business depending on other factors. Cybersecurity is a growing risk for businesses of all sizes. While 20 years ago, only major companies were on the radar of sophisticated cyber criminals, today, those criminals are pursuing companies of all sizes.


The world of automation is continually evolving, and its impact on corporate finance will be at the forefront of that evolution. CFOs are not only struggling to find the budget to implement these technologies, but it has also become challenging to stay apprised of new developments. For these reasons, CFOs must find a trusted partner who can highlight the most effective route for investing in new technologies to remain competitive.

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