Intelligent automation is transforming how modern finance teams operate. By combining robotic process automation (RPA) and artificial intelligence (AI) technologies like machine learning, natural language processing, and computer vision, businesses can achieve end-to-end automation of complex finance and accounting processes.
As an expert in web scraping and data extraction with over a decade of experience, I‘ve seen firsthand how intelligent automation boosts efficiency, reduces errors, and empowers finance staff to focus on strategic initiatives.
In this comprehensive guide, I‘ll share my insights on how leading finance teams are using intelligent automation in 2024 across four key use cases, complete with real-world examples and recommendations to get started.
Streamlining Accounts Payable and Receivable
Managing accounts payable and accounts receivable manually requires extensive human effort, introduces errors, and hampers staff productivity. Based on my experience, intelligent automation excels at optimizing AP and AR processes.
For accounts payable, RPA bots can be trained to replicate human actions, while AI technologies automate more complex tasks:
- RPA can methodically collect invoices, extract key details, cross-reference purchase orders, and update accounting systems. This automates the repetitive manual work of data entry and validation.
- NLP can read and comprehend typed, handwritten, or scanned text on invoices to extract critical variables. This removes the need for humans to manually key in data.
- ML algorithms can detect anomalies and flag potentially fraudulent invoices for further review. This automates a crucial control function.
- Computer vision can scan invoice documents and instantly classify them based on type, layout, origin, etc. This accelerates document routing and processing.
For accounts receivable, the same principles apply:
- RPA bots can generate standardized invoices en masse while NLP comprehends client communications to extract key data points.
- ML-powered analytics enable intelligent invoicing based on client histories and real-time data.
- Chatbots can understand customer questions over email or messaging apps and resolve issues autonomously.
According to a McKinsey survey, accounts payable and receivable automation delivers the fastest ROI of any finance process, with break-even in less than 6 months. Leading companies have achieved:
- 60-80% reduction in invoice processing costs
- 50%+ increase in productivity
- 75-90% faster invoice approval turnaround times
By combining RPA and AI, finance teams can optimize AP and AR processes end-to-end, from data intake to final output. The result? Faster processing, minimized errors, improved working capital, and satisfied vendors and customers.
Streamlining Intercompany Reconciliations
Intercompany accounting is a major pain point. With over 64,000 mergers and acquisition deals in the past decade, manually reconciling intercompany transactions across disparate systems is complex, labor-intensive, and ripe for errors.
Intelligent automation can replicate human actions while also applying intelligence to streamline end-to-end intercompany processes:
- RPA bots can systematically extract transaction data from multiple ERPs and subsidiary systems.
- NLP can classify and comprehend unstructured transaction details from contracts, emails, and reports.
- ML algorithms can be trained to match intercompany entries and detect discrepancies even across different formats and systems.
- Smart rules engines can take auto-reconciled entries and create the appropriate consolidation journal entries.
According to research firm Ardent, 75% of intercompany reconciliations can be automated through RPA and AI, significantly reducing labor hours while minimizing reconciliation errors.
By combining robotic and cognitive technologies, finance teams can achieve touchless intercompany transaction processing and reconciliation. This boosts productivity, ensures consistency, and allows staff to focus on value-added analysis.
Accelerating Financial Reporting
Financial reporting is essential but often bogged down by manual processes of collecting, manipulating, and consolidating data. Intelligent automation can reduce the 60% of finance analyst time spent on data gathering and validation, freeing them to generate insights.
Key automation applications include:
- RPA bots that systematically pull data from source systems and input it into reporting templates.
- NLP that reads emails, contracts, filings, and notes to extract unstructured data.
- ML that validates anomalies in data sets.
- Chatbots that engage stakeholders to collect commentary and inputs.
- Dynamically generated visualizations that illustrate key data insights.
According to research by Deloitte, RPA and AI can reduce reporting turnaround times by 80-90% while cutting costs by 40-60%. By combining RPA, NLP, ML, and more, finance teams can drastically accelerate financial reporting cycles. Rather than chasing data, analysts can focus on deriving strategic business insights.
Enhancing Financial Planning & Analysis
Financial planning and analysis produces vital forecasts and budgets that guide business decisions. However, nearly 60% of processes still rely on cumbersome spreadsheets. Intelligent automation delivers more advanced FP&A.
- RPA aggregates data from multiple sources into dynamic models.
- NLG generates insightful commentary to accompany models.
- Predictive analytics powered by ML enhance forecasting capabilities.
- Cognitive expert systems enable scenario planning and instant recommendations.
Per McKinsey, RPA and AI can reduce planning and forecasting process times by 40-55% while boosting data quality. Rather than manipulating spreadsheets, finance staff can devote time to value-added analysis and strategy.
By combining robotic and cognitive technologies across the FP&A value chain – data collection, modeling, reporting, and analysis – finance leaders can enhance agility, insights, and decision making.
Let‘s look at two examples of organizations achieving remarkable results with finance automation:
International Consumer Goods Company
A leading fast moving consumer goods company automated over 160 processes across finance and accounting. By deploying RPA, OCR, NLP, and ML, they achieved:
- 60% reduction in manual work in Order to Cash processes
- 55% cost reduction in Procure to Pay processes
- 20% boost in collections staff productivity from automating credit hold releases
This allowed the team to deliver $20 million in savings over 2 years.
Global Financial Services Firm
A top global bank automated reporting for asset liability management which previously required 500k manual hours annually. By implementing RPA and AI:
- 60 analysts were freed up to focus on value-added insights
- $8 million in annual savings delivered
- Reporting turnaround cut from 5 days to 5 hours through automation
These examples demonstrate how combining RPA and AI can help finance teams automate end-to-end processes, reduce costs, and empower staff.
Here are best practices I recommend finance leaders consider when launching intelligent automation programs:
Pick The Right Processes – Focus first on high-volume, repetitive manual tasks with digital inputs/outputs. AP, AR, and reporting are prime options.
Start Small, Then Scale – Prove value with targeted pilots before expanding scope. Manage change and build internal skills progressively.
Take An End-To-End View – Look for automation opportunities across entire workflows vs. one-off tasks. This maximizes benefits.
Combine RPA And AI – Layer smart technologies like NLP, ML, and computer vision over RPA for greater capabilities.
Involve Your Team – Get staff input to map processes and identify automation opportunities based on pain points.
Choose The Right Technology Partner – Select a platform that seamlessly integrates RPA, AI, and other next-gen technologies.
Focus On Outcomes – Track productivity, error reduction, working capital improvements, and other metrics to quantify benefits.
Intelligent automation will become an indispensable productivity driver for finance teams. Key trends I foresee based on my experience:
- End-to-end automation will continue expanding across finance as RPA and AI capabilities improve.
- Bots and AI will operate autonomously with less human input required through advances like unsupervised ML.
- Intelligent automation will spread enterprise-wide beyond finance as a digital workforce layer across business units.
- Advanced analytics will enable finance to move from retrospectives reporting into predictive planning powered by AI.
As technology capabilities grow exponentially, the possibilities for automating complex finance processes using RPA, ML, NLP, computer vision, and other innovations are endless.
Intelligent automation represents an enormous opportunity for CFOs and finance leaders to drive greater productivity, minimize errors, and enable teams to focus on high-value initiatives like analytics and planning.
By combining robotic and cognitive technologies, finance departments can achieve end-to-end automation of everything from routine AP/AR tasks to complex reporting and forecasting processes.
Now is the time to launch intelligent automation pilots – the benefits and ROI are clear. With the right strategy and technology partner, forward-thinking finance organizations can lead the next wave of business transformation.