Finance organizations operate in an increasingly data-rich yet insight-starved business environment. While core systems contain a wealth of valuable information, finance staff spend inordinate time on manual workflows for report generation, reconciliation and transaction processing. Rote activities squander high-value skills that would be better directed toward value-creating analysis and strategic planning support.
Intelligent process automation in the form of robotic process automation (RPA) and AI provides a path to liberate finance groups from repetitive tasks. RPA bots can take on data-intensive rules-based work across multiple applications just as humans do – but faster, better and at lower cost. Meanwhile, AI tools expand the possibilities for data pattern discovery, future outcome prediction and recommendation generation based on advanced analytics.
This guide explores key use cases and proven approaches for harnessing automation to elevate finance performance. We incorporate hard data on efficiency gains along with real-world client success stories to showcase how leading organizations extract compelling ROI today while building next-generation, insight-driven finance for tomorrow.
Quantifying the Potential for Finance Process Automation
To assess the opportunity landscape, McKinsey evaluated common finance activities against multiple criteria to determine suitability for automation. By examining factors like process rules-dependency, interface complexity and exception handling needs, they concluded over 40% of finance tasks can be fully automated by current technological capabilities:
This automation potential concentrates primarily in the middle and back office across core record-to-report and corporate functions. Areas closer to front office, like management reporting, retain a greater need for human judgment.
Drilling down into specific activities shows even greater levels of automation applicability:
| Process Area | Examples | Automation Potential |
|---|---|---|
| Accounts Payable | Invoice processing Payment reconciliation |
60-85% |
| Accounts Receivable | Payment receipt Cash application |
55-70% |
| General Accounting | Journal entries Reconciliations |
55-70% |
| Regulatory Reporting | Financial statements Tax filing |
35-50% |
Realizing this level of automation potential translates into dramatic operational improvements:
Additionally, increased throughput and accelerated process speed enables new data-intensive initiatives in analytics and planning. Research shows finance groups gaining RPA productivity improvements experience 50% higher adoption of innovative technologies like big data, AI and the Internet of Things.
Driving Enterprise Value Through Finance Automation
Beyond impressive operational metrics, automating finance workflows unlocks substantial financial return thanks to:
Cost Reduction
Transitioning manual effort to bots cuts associated labor spends. RPA cost savings stem from avoiding hiring incremental headcount for increased workloads as well as optimizing current staffing models:
Albums Group achieved €2.3 million in annual savings after automation reduced invoice processing costs by 75%. Best practices driving maximum cost efficiency include use of unattended bots working 24x7x365 without human oversight.
Profit Margin Improvement
Faster process execution provides cash flow gains via accelerated invoice generation, payment cycle compression and reduced days sales outstanding:
Here automation both shrinks the fixed costs of finance activities through productivity gains while driving working capital gains through faster settlement. This directly contributes to bottom line profitability.
Risk Reduction
End-to-end workflow automation including built-in validations and controls significantly enhances compliance in transaction processing and reporting:
In one case, RPA cut the error rate for balance sheet reconciliation by over 80%, saving thousands in rework costs and preventing regulatory penalties.
Reallocating manual efforts to bots cuts overtime while increasing capacity for value-add initiatives around analytics. Redirecting even 20% of an average finance employee’s time toward insight generation rather than task execution typically delivers $50,000+ in incremental value annually.
10 High-ROI Finance Process Automation Use Cases
While early RPA adoption focused mainly on obvious document-centric processes, rapid advances in integration and intelligence enable automation across virtually all finance subfunctions. Below we highlight key opportunities yielding strong results:
Accounts Payable
| Use Case | Capabilities | Results |
|---|---|---|
| Invoice Processing | – Data extraction from email, EDI, PDF invoices – Matching invoice data to POs – Routing invoices for approvals – Resolving discrepancies |
– 85% touchless invoice processing – 55% quicker payments |
| Vendor Management | – Centralizing supplier data from sourcing systems – Enrichment with D&B data for KYC checks – Managing vendor master data changes |
– 30% increased discount capture from consolidation – 80% cut in duplicate suppliers |
| Payment Reconciliation | – Downloading bank statements – Matching to invoices & payments – Identifying & resolving discrepancies |
– 60% reduction in outstanding reconciliations – 99% match rate for electronic payments |
Accounts Receivable
| Use Case | Capabilities | Results |
|---|---|---|
| Cash Application | – Retrieving lockbox check scans – Validating against open invoices – Posting payments & adjustments |
– 50% faster cash processing – 15%+ lift in working capital |
| Collections Management | – Identifying past due invoices – Sending payment reminders – Tracking disputes & deductions |
– 20%+ increase in auto-resolution for deductions – 30% rise in cash collections |
| Customer Onboarding | – Data input & retrieval across 20+ systems – Document classification & extraction – Orchestrating activation steps |
– 60-80% drop in onboarding costs – 90% shrinkage in process time |
General Ledger Management
| Use Case | Capabilities | Results |
|---|---|---|
| Journal Entry Processing | – Sourcing supporting data from 100+ feeder systems – Applying validation checks – Routing for matching & approval |
– 55% direct cost reduction – 33% cut in days to close books |
| Balance Sheet Reconciliations | – Comparing general ledger to sub-ledgers – Researching & resolving differences – Adjustment entries |
– 90% faster reconciliation execution – 80%+ drop in accounting differences |
| Internal & External Audits | – Gathering samples across systems – Running tests to find exceptions – Creating schedules documenting controls |
– 60%+ productivity rise for audit prep – 30% growth in audits conducted annually |
Financial Planning & Analysis
| Use Case | Capabilities | Results |
|---|---|---|
| Data Aggregation | – Connecting 100+ data sources via APIs – Mapping to canonicals – Validation & cleansing |
– 50% accelerated financial consolidation – 5% lift in forecast accuracy |
| Driver-Based Models | – Importing budgets & latest estimates – Simulating impacts of input changes – Analyzing variances |
– 90% reduction in model maintenance costs – 33% faster operational predictions |
| Management Reporting | – Assembling revolt headers, charts and narration – Distributing interactive dashboards with drill down analysis |
– 80% drop in basic report generation costs – Real-time analytics vs. static period-end |
Regulatory Reporting
| Use Case | Capabilities | Results |
|---|---|---|
| Financial Statements | – Connecting trial balance systems – Mapping accounts into reporting taxonomy – Running disclosure checklists |
– 50%+ direct cost savings – 3x increase in revisions supported |
| Tax Compliance | – Retrieving data from payroll, AP, AR etc. – Making necessary calculations – Populating appropriate forms |
– 90% shrinkage in compliance team effort – 0 reporting penalties from automation accuracy |
| Audit Support | – Extracting samples per regulator requests – Annotating policies governing treatment – Responding to inquiries as needed |
– 70% fewer internal audit findings year over year – 90% regulator satisfaction with data provided |
Critical Factors for Finance Automation Program Success
Realizing maximum value from finance process automation relies on proficient change management encompassing leadership, talent, underlying systems and the target operating model:
Secure Executive Sponsorship
Finance chiefs must be vocal champions for automation from inception through scale. Leadership’s active participation accelerates organizational alignment while also inspiring the broader community of finance peers embarking on similar journeys.
Focus on Change Management
Reskilling staff on working alongside bots averts workforce anxieties about job loss. Be transparent on expectations for new roles, skills and ways of working to ease the transition. Simultaneously re-engineer processes for automation-readiness, removing unnecessary manual steps that hinder productivity.
Prioritize Integration
Break down data silos and harmonize formats across systems through APIs and canonical data models. This minimizes exceptions and enables straight-through processing of end-to-end workflows.
Architect for Auditability & Control
Programmatically incorporate compliance requirements like 4-eye validation, maker-checker and final approval into automated workflows using RPA. This prevents material weaknesses while accelerating throughput.
Measure Continuously
Track productivity and risk metrics at each RPA phase to spotlight opportunities, address obstacles and showcase wins. Refine functional KPIs like queries resolved or subledger reconciliations completed monthly to keep focus.
The Path to Autonomous Finance Operations
RPA delivers substantial benefits today by applying automation to manual, repetitive finance tasks. However integrating smart technologies like AI, machine learning and natural language processing opens the door to fully autonomous processing for many activities requiring human judgment. For example:
Auto-Reviewing Documents
Bots currently extract data from invoices and receipts to drive workflow steps. With AI, bots could interpret layout nuances and unstructured content details across numerous document types to resolve discrepancies, recommend process deviations and trigger approvals without any user oversight.
Conducting Predictive Planning
Today driver-based models rely on analyst expertise to determine and tune key indicators impacting budgets and forecasts. AI tools could model predictive relationships among thousands of internal & external factors automatically, simulate interactive business scenarios and highlight risks opportunities for optimal decision making.
Producing Narrative Reporting
Whereas basic automation generates standardized report templates, NLP algorithms can interpret meaning from data patterns over time, craft insightful narratives linking operational performance to strategic goals and tailored recommendations perfectly aligned to decision maker needs.
The combined power of automation + intelligence also enables revolutionary capabilities like continuously optimized spending control via auto examination of 100% spend under management and real-time regulatory compliance safeguards.