Financial reporting is rapidly reaching an inflection point where cloud-powered smart automation technologies and data science best practices are creating a next generation of intelligent, self-improving reporting capabilities for finance teams. Read on as we analyze key innovations through the lens of a data analytics expert that are driving more accurate, timely and strategically-focused financial reporting processes.
The Limitations of Traditional Reporting
Before exploring recent advances, it‘s important to note core shortcomings that have historically plagued financial reporting:
Labor-Intensive Manual Processes
Many closing and reporting mechanisms still rely heavily on manual extraction, consolidation and validation via spreadsheets. This created bottlenecks while increasing risk of errors. Per Ernst & Young 73% of finance staff‘s time is spent collecting and validating data during period-end reporting rather than on analysis.
Sparse Data and Metrics
Reporting has traditionally focused solely on snapshotted financial statements for broad organizational levels updated periodically. This provides limited visibility for timely insights.
IT Systems Complexity
Most enterprises use myriad legacy systems and cloud applications storing financial data across silos. Consolidating data is tedious while differences in reporting logic across systems raise reconciliation needs.
Lack of Process Transparency
With manual reporting, inputs and calculations are opaque hindering internal confidence and external audits of reported figures. Errors can go undetected for multiple cycles.
The Promise and Progress of Intelligent Automation
Finance is now aggressively modernizing capabilities. Let‘s analyze some of the most impactful data and automation technologies powering the latest reporting solutions:
Cloud Infrastructure Enables Software Agility
Running systems via cloud infrastructure rather than on-premise hardware allows quick deployment of software innovations. Cloud‘s usage-based pricing also provides cost efficiency. These testing and scaling advantages explain why 93% of organizations use cloud services today with most expanding investment further according to Hamilton Place Strategies.
Robotic Process Automation Eliminates Manual Tasks
Tools like robotic process automation (RPA) incorporate rule-based software "bots" mimicking human activities. For reporting this enables automated data extraction from multiple systems followed by spreadsheet manipulations, report population and more. RPA mimics manual work so it integrates with legacy systems. Per Gartner by 2025 over 40% of controllership tasks will incorporate RPA reducing labor hours by ~25%.
AI and ML Continuously Improve
Emerging artificial intelligence (AI) techniques including machine learning statistically "learn" from accumulating experience. In financial reporting this can power:
- Predictive analytics anticipating future period results for more forward-looking insights
- Anomaly detection identifying unusual fluctuations needing investigation
- Process mining to incrementally boost reporting efficiency
- Natural language generation translating data to written analysis updates
So unlike rigidly programmed software, AI solutions compound in intelligence over time.
Visualization and BI Extract More Value from Data
Modern reporting solutions incorporate interactive dashboards that transform opaque tables into easily-consumable charts, gauges and mapping while enabling drill-downs. Tailored self-service reporting allows business teams to slice-and-dice data to uncover custom insights beyond standard statements. Embedded business intelligence (BI) makes reporting analytics continuous rather than episodic.
Financial Reporting Innovation in Action
Let‘s analyze a few real-world examples of automation and analytics transforming financial reporting:
Fighting Fraud with AP Pattern Recognition
A retail chain deployed machine learning algorithms analyzing accounts payable (AP) invoice patterns including vendor names, purchase categories, locations, approval chains and payment details. Models detected several instances of fraudulent invoices within weeks of going live providing over $1 million in cost avoidance annually. Because the self-learning models incorporate new invoices continuously, detection improves every period.
Scientific Forecasting with Statistical Modeling
Rather than relying on simple extrapolation of historical trends, advanced analytics at a promotional products manufacturer applies multivariate regression analysis against datasets like macroeconomic indicators, commercial real estate stats, campaign response rates, website conversion metrics and more to create scientifically-powered demand forecasts which feed into revenue projections. This level of precision enabled supply chain optimizations averting millions in potential lost sales.
Continuous Exception Flagging Boosts Period-End Speed
With daily automated consolidation of general ledger data, a B2B distributor configured exception reports highlighting unusual account movements. Machine learning classifies each anomaly as red/yellow/green flags based on risk and materiality. Accountants now quickly drill into red items as soon as period closes rather than combing through ledgers manually. Financial statement turnaround improved from twelve to just 3 days – essential for agile decision making.
Integrating Cutting-Edge Data Science with Finance Operations
While reporting automation is advancing rapidly, leaders ground implementations upon these core data science leading practices for maximizing and sustaining value:
Visibility at Scale via Data Lakes/Warehouses
Modern architectures consolidate enterprise-wide data into centralized repositories providing a "single source of truth" leveraged across applications like reporting, planning, treasury and more. This boosts consistency while removing delays from gathering distributed data.
Trust with Data Quality Initiatives
"Garbage in garbage out" rules apply strongly for data-driven initiatives. Proactively ensuring completeness, accuracy and governance via tools like data quality platforms reduces downstream anomalies that erode stakeholder confidence and operational performance.
Agnostic and Adaptable Analytical Models
Given complex and ever-changing systems landscapes, insight engines purposefully avoid hardcoding dependencies allowing onboarding new datasets without requiring model rework. Agnostic models also facilitate certain types of changes like ERP migrations with reduced disruption.
Sustaining Value via Continuous Improvement Loops
Ongoing augmentation of reporting, forecasting and decision models allows systematically raising intelligence thresholds over time. Embedding feedback loops linking model outcomes to business results using statistical methods accelerates "learning" by maximizing relevant signal vs noise.
While cutting-edge automation delivers radical near-term efficiency gains, over the longer-term advanced analytics will enable even more material value creation thanks to compounding improvements.
Key Takeaways: The Future of Intelligent Financial Reporting
Financial reporting sits at the intersection of many digital transformation efforts – whether enterprise system consolidations, cloud migrations or finance process redesigns. New generations of rich automation supplemented by continuous intelligence from advanced analytics and AI promise to simultaneously speed reporting, improve quality and drive more informed decisions.
However, as with any complex enterprise initiative, well-architected change management combining updated strategies, reconfigured operations and upskilled talent is essential for sustaining success when fundamentally evolving mission-critical finance functions like reporting. Certified automation solution partners can help smooth these technical and organizational transitions in financial reporting‘s pivot toward becoming a modernized digital capability.