Today, finance teams are no longer merely processing unending transactions and balancing the books. The leadership of most global organizations expect their finance teams to play a more active role in deciding the future of their businesses. Digital solutions have enabled this transformation. Automation is taking over reconciliations, reporting, and financial forecasting among many other repetitive tasks. But most finance teams are also moving beyond automation and shifting their focus to Intelligent Automation (IA) to accelerate more complex tasks.

“Intelligent Automation is making waves in the finance function – we have seen many teams needing support with unending document processing and data extraction to make speedier decisions. IA is a revolutionary tool for tedious tasks like these.”
– Harshana Kuruppu, AVP Quality Engineering and Intelligent Automation

EY defines Intelligent Automation as “combining the strengths of Robotic Process Automation (RPA), AI, and human intelligence. This means the intelligent use of multiple automation approaches and tools, from integrating basic robots to fully digitizing processes and systems”. In finance, IA predominantly uses machine learning to improve transaction processes and other repetitive tasks. The technology can detect patterns and be trained to take on more complex tasks over time. 

This blog takes a step back to identify the key stages that are needed to set your finance function on the right track to get ahead with these technologies.

Where to start?

To effectively leverage IA in the finance function, it is essential to establish well-defined objectives that align with broader organizational goals. These objectives will guide the implementation process and ensure your initiatives deliver tangible benefits. The work that took four hours of your time would be completed by the time you get to the office with your morning cup of coffee.

A few common objectives include:

– Enhanced Decision-Making: Provides predictive analytics and insights that support strategic financial decisions.
– Increased Efficiency: Automate routine and repetitive tasks, such as data entry and reconciliation, to reduce operational costs and increase productivity.
– Improved Accuracy: Minimize human errors in financial processes, reporting and compliance activities.
– Strengthen Risk Management: Detect and mitigate financial risks, including fraud and market volatility.

To what extent does your data have to be prepped for IA?

The extent to which your data needs to be prepped for IA largely depends on the specific task at hand. For routine tasks such as data extraction, IA operates with remarkable efficiency, often bypassing the need for extensive data preparation. Existing models can be trained to extract data from diverse sources, streamlining processes without the need for laborious preprocessing. Moreover, the flexibility of IA allows for the creation of custom models tailored to unique requirements, ensuring adaptability to evolving needs.

However, when the focus shifts to predictive analytics, a different approach emerges. While IA itself does not mandate extensive data prep, predictive analytics within the IA framework necessitates some level of preprocessing. To achieve accurate predictive insights, data must undergo suitable preparation to ensure its quality and relevance. Nonetheless, even within this context, the preparatory phase is often less burdensome compared to traditional analytics methodologies. Using IA you can extract data from different data sources, prep them and store it in a database in a structured manner for easy analytics.

In essence, the beauty of IA lies in its versatility and adaptability. Whether it’s streamlining routine tasks through existing models or delving into predictive analytics, IA offers a spectrum of possibilities, minimizing the burden of data preparation while maximizing efficiency in the finance function.

Narrowing down on use cases

This stage involves identifying pain points and areas with significant potential for efficiency gains and error reduction. Collaborating closely with subject matter experts (SMEs) is essential in this phase, as their insights will guide the selection of high-impact use cases tailored to the unique needs of the organization.

“An Australian Food & Beverage manufacturer partnered with Fortude to streamline their remittance advice processing. The organization struggled with a surplus of remittance advice from multiple parties in varying templates. Using an IA bot, we helped the client process their remittances with minimum human interaction.”
– Kolitha Gunarathne, Head of RPA

A few areas in finance that could benefit from IA include expense receipt scanning and monitoring, classifying texts into custom categories, detecting custom objects in an image, and detecting positive, negative, or neutral sentiments in text data.

Intelligent Automation (IA) revolutionizes processes by efficiently classifying texts into custom categories, enabling swift analysis of vast datasets. Moreover, IA’s capability to detect custom objects in images enhances fraud detection and asset tracking, ensuring robust security measures. Additionally, IA’s adeptness in detecting sentiments in text data empowers financial institutions to gauge customer satisfaction and market trends accurately, facilitating informed decision-making and personalized services.

Building leadership & a skilled team

It’s important to identify key leaders who have a deep understanding of both finance and technology. These leaders will champion the IA initiative, ensuring alignment with the organization’s strategic goals and securing necessary resources. Leadership should include senior executives from finance and IT, who can provide the vision and authority needed to drive the project forward.

Next, assemble a diverse team that represents all relevant areas of the business. This cross-functional team should include finance professionals, IT experts, and representatives from various business units.

Ensuring compliance and security

Financial institutions operate in a highly regulated environment, and any IA implementation must adhere to laws such as the General Data Protection Regulation (GDPR), Sarbanes-Oxley Act (SOX), and the Payment Card Industry Data Security Standard (PCI DSS). Ensuring compliance involves continuous monitoring and updating of systems to meet evolving regulatory requirements. It is crucial to conduct thorough audits and risk assessments, establish clear data governance policies, and maintain detailed documentation of processes and decisions to demonstrate accountability and transparency to regulators.

In addition to compliance, implementing robust security measures is paramount to protect sensitive financial data from breaches and cyber threats. If you have a Cloud partner, these security measures are often accounted for in the security and compliance details.

Continuous monitoring and optimization

Establishing Key Performance Indicators (KPIs) is crucial to measure the impact of IA on finance functions effectively. These KPIs should be aligned with the organization’s strategic goals and might include metrics such as process efficiency, accuracy of financial forecasts, reduction in operational costs, and overall return on investment (ROI) from digital solutions. Regularly tracking these indicators helps in understanding how well your systems are performing and whether they are meeting the intended objectives. It also provides insights into areas where the IA applications are excelling or falling short.

As the financial landscape and organizational needs evolve, IA models must be updated and retrained to maintain their relevance and accuracy. This involves collecting and analyzing performance data, identifying patterns or anomalies, and making necessary adjustments. Moreover, incorporating feedback from finance professionals who interact with the systems can offer valuable perspectives for fine-tuning. By fostering an iterative cycle of monitoring, feedback, and optimization, organizations can ensure that their IA-driven finance functions remain robust, efficient, and capable of delivering high value over the long term.

Your IA toolbox

One of the foundational tools in the IA toolbox is Robotic Process Automation (RPA), with platforms like UiPath leading the charge. RPA automates repetitive, rule-based tasks such as data entry, invoice processing, and reconciliation, freeing up human resources for more strategic activities. UiPath offers a robust suite of tools that integrate seamlessly with existing financial systems, enabling companies to streamline workflows and reduce errors. This automation not only increases operational efficiency but also provides real-time data processing capabilities, which are critical for timely decision-making.

In addition to RPA, the rise of low code/no code platforms, such as Microsoft’s Power Platform, is democratizing automation within finance departments. The Power Platform includes tools like Power BI for advanced analytics, Power Automate for workflow automation, Power Apps for custom application development and Power Virtual Agent to build your own chatbot . These tools empower finance professionals, even those with minimal coding experience, to create sophisticated solutions tailored to their specific needs.

If your finance function is eager to unlock the potential of IA, one key, first step is to adopt Robotic Process Automation (RPA) to handle repetitive tasks with high precision and speed. In fact, one of the frequent questions we get from finance teams is what processes they can automate – Here’s our answer to that.

Fortude has guided numerous companies and their finance functions in making the right digital investments that have set them on the right path to getting ahead with IA – are you ready to take the first step?