Mapping your future in finance with Intelligent Automation (IA)

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.

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.

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.

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.