In today’s data-driven business environment, leveraging Accounts Payable (AP) Analytics has become crucial for organisations aiming to enhance their financial operations through strategic decision-making. By harnessing the power of AP analytics, businesses can gain valuable insights into their payables process, identify inefficiencies, and make informed decisions to optimise cash flow and improve vendor relationships. This blog explains what accounts payable analytics is, the AP data it draws on, how descriptive and predictive AP analytics differ, six examples, the KPIs to track, and how to implement it.
What is accounts payable analytics?
Accounts payable analytics involves the analysis of accounts payable data to gather actionable insights. This includes examining data related to vendor payments, invoice processing times, discounts taken and outstanding liabilities. By analysing this data, organisations can identify trends, patterns and anomalies that can help them streamline their AP processes and boost financial and operational efficiency. AP analytics and accounts payable analysis describe the same discipline: "analysis" usually means a one-off review of the payables ledger, while "analytics" means the continuous, tool-driven version that runs on live data and feeds decisions every day.
What accounts payable data does AP analytics use?
Most of the value comes from joining data that already exists in the ERP and the AP workflow tool:
- Invoice header and line data: Vendor, date, amount, currency, tax, line items and general ledger coding
- Purchase order and receipt data: The two other legs of the three-way match, which show where invoices fail matching and why.
- Vendor master data: Payment terms, bank details and change history, tax status, and category, which drive terms analysis and fraud checks.
- Workflow timestamps: Received, scanned, coded, approved and paid dates for every invoice, which make cycle-time and bottleneck analysis possible
- Payment runs: Payment date against due date and discount date, payment method and bank charges
- Exceptions and disputes: Reasons for holds, rejections and short payments, which reveal the root causes behind late payments.
Descriptive, predictive and prescriptive accounts payable analytics
AP analytics matures in three stages, and each answers a different question:
| Type | Question it answers | Accounts payable example |
| Descriptive analytics | What happened, and where? | Invoice volumes, cost per invoice, days payable outstanding and late payments by entity, vendor and month, shown on an accounts payable dashboard |
| Predictive analytics | What will happen next? | Forecasting cash outflows from invoices in flight, predicting which invoices will miss their discount date or fail matching, and flagging likely duplicates before the payment run |
| Prescriptive analytics | What should we do about it? | Recommending which invoices to pay early for discount, which to hold to protect cash, which vendors to move to new terms, and which approval routes to shorten |
Benefits of accounts payable analytics
AP analytics offers several key benefits for organisations: The gap between good and average AP functions is measurable: APQC’s benchmarking found that top-quartile AP organisations spend one-fifth of what bottom-quartile organisations spend to process invoices, and take half as many days from invoice receipt to payment.
- Improved cash flow management: By analysing AP data, organisations can identify opportunities to optimise payment schedules, utilise any discounts given for early payment, and negotiate favourable terms with vendors. This can help improve cash flow and working capital management.
- Improved understanding of spend patterns: AP analytics helps organisations better understand their spend patterns, which helps to plan better for future expenses. Overspending in any area can be identified and controlled. Spending goals and budgetary adjustments can be better managed too.
- Enhanced visibility and control: AP analytics provides greater visibility into the payables process, allowing organisations to track invoice statuses, monitor payment trends and identify potential bottlenecks. This visibility enables better control over the AP process and helps prevent fraud and errors. For example, late payments and the associated risks can be identified and mitigated leading to improved supplier relationships. Reasons for delays in invoice processing can be identified and corrected so that the process can be expedited. Potential fraud and irregularities can be identified too. The risks associated with such activities can be controlled at the right time.
- Cost savings: By identifying and eliminating inefficiencies in the AP process, organisations can reduce costs associated with late payments, duplicate payments and manual processing. AP analytics can also help identify opportunities to negotiate better terms with vendors and optimise payment strategies.
- Improved vendor relationships: By analysing vendor payment patterns and histories, organisations can better understand vendors. This can help foster stronger, more collaborative relationships with vendors, leading to better terms, discounts and service levels.
Accounts payable analytics examples
Six analyses that most AP teams can run within the first quarter:
- Duplicate and overpayment detection: Fuzzy matching on vendor, amount, date and invoice number finds re-submitted and re-keyed invoices before they are paid.
- Early-payment discount capture: Comparing discount terms offered against discounts taken, by vendor and entity, shows how much is being left on the table and which approval delays cause it.
- Days payable outstanding by segment: DPO by vendor category, business unit and country shows where terms are being paid early without benefit and where late payment is damaging supplier relationships.
- Cycle-time bottleneck analysis: The time spent at each workflow step (receipt, coding, matching, approval, payment) identifies the step and the approver group that holds up the most value.
- Vendor concentration and risk: Spend and payment behaviour by vendor highlights single-source dependencies, dormant vendors with recent activity, and suppliers with rising dispute rates.
- Off-contract and maverick spend: Invoices without a purchase order, or from vendors outside the preferred list, quantified by category and requester, give procurement a target list.
How to implement accounts payable analytics
To leverage the benefits of AP analytics, organisations should follow certain key steps:
- Define objectives: Clearly define the objectives of the organisation’s AP analytics initiative, such as improving cash flow, reducing costs or enhancing vendor relationships.
- Gather data: Collect relevant data from the AP system.
- Data cleaning and preparation: Clean and prepare the data for analysis, ensuring its accuracy and completeness.
- Choose the right analytics tools: Select the right analytics tools and technologies, such as data visualisation tools, predictive analytics or machine learning (ML) algorithms. An accounts payable dashboard that refreshes from the AP system, rather than a month-end spreadsheet, is usually the first deliverable and the one finance leaders use most.
- Perform analysis: Analyse the data to identify trends, patterns and outliers that can provide insights into the AP process.
- Generate insights: Use the insights gained from the analysis to make informed decisions and drive improvements in the AP process.
- Monitor and iterate: Continuously monitor the AP analytics initiatives and iterate on organisational strategies to drive ongoing improvements.
Tracking AP KPIs for best results
Understanding and tracking certain key performance indicators (KPIs) associated with AP can help organisations analyse and manage their AP processes efficiently. A few useful KPIs include:
- Cost per invoice: It indicates the cost effectiveness of the organisation’s AP processes. It calculates the cost of processing each invoice. AP invoice costs include salaries, payment processing costs and other software costs. Lowering this cost can help organisations make significant savings, especially in high-volume transactions.
- Days payable outstanding (DPO): Also known as AP days, the metric gives figures for the average time taken by the organisation to make vendor payments. A high DPO implies more invoices are paid after the expected date, indicating longer payment terms. A low DPO could also mean an efficient cash flow management process.
- Invoice processing time: This metric gives numbers for the time taken for invoice processing right from when it is received till it is paid. This metric differs from DPO as organisations may hold payment to maintain a sufficient cash flow even after an invoice is processed. The KPI helps identify bottlenecks, measure employee efficiency and maintain good vendor relationships.
- Supplier compliance rate: High compliance rates indicate smooth vendor relationships and fewer disputes. It indicates that vendors are complying well with the contractual terms and conditions.
- Late payment rate: This metric gives a clear insight into how frequently late payments are made and monitoring it helps organisations maintain good supplier relationships and a healthy cash flow.
For the full set, including first-time match rate and invoices processed per full-time employee, read our blog on the five important KPIs for accounts payable processes.
Challenges and automation
While the challenges of implementing AP analytics are quite manageable, the first step is acknowledging the hurdles. This is where AP automation plays a significant role. It helps build a strong foundation for efficient AP analytics. Automation drives accuracy and consistency as well as the timely availability of data in the required format.
- Data availability and quality: Data used for analysis must be accurate, of a high quality and easily available for best results. AP automation can help enhance data quality by ensuring real-time data is readily available and manual data entry errors are reduced.
- Experience and expertise: Organisations must have personnel with the right skills to understand AP analytics. AP automation platforms reduce the need for high levels of expertise by offering user-friendly dashboards and interfaces.
- Acceptance of change: Implementing a data-driven approach often meets with resistance from the staff if they are more used to traditional methods of working. It is necessary to educate and train staff to overcome this challenge.
The real-time data analysis offered by AP automation enables organisations to respond quickly to both emerging opportunities and challenges. Decisions can be made in real time, and when necessary, anomalies that crop up can be corrected immediately or even proactively.
How can Infosys BPM help?
Infosys BPM’s Spend Analytics Services enhances an organisation’s procurement ecosystem with spend classification, AI-enabled data management, opportunity identification and other critical domain services to overcome business challenges. You can use our Dynamic Guided Opportunity Analysis to continuously identify and validate improvement opportunities, turning raw payables data into accounts payable insights that finance and procurement leaders can act on.
Frequently asked questions
AP reporting tells you what happened: invoices processed, amounts paid, ageing at month end, usually as a static extract from the ERP. AP analytics asks why it happened and what will happen next: which vendors or entities drive late payments, which approval step causes the delay, how much discount will be lost next month if nothing changes. Reporting is periodic and descriptive; analytics is continuous, links AP data with purchase orders, vendor and spend data, and produces a recommended action rather than a table.
Because most AP value is time-bound. An early-payment discount expires on a date, a duplicate invoice has to be caught before the payment run rather than after, and a cash forecast is only useful before the payment is scheduled. Monthly analysis finds these after the money has moved; a real-time view of invoices in flight, approvals pending and payments queued lets the team act while the option is still open.
Yes, and it is one of the highest-return uses. Duplicate detection matches invoices on vendor, amount, date and invoice number with fuzzy rules that catch retyped or re-scanned copies. Fraud analytics looks for changes to vendor bank details shortly before a payment, invoices just under approval thresholds, vendors that share an address or bank account with an employee, round-amount invoices with no purchase order, and unusual digit patterns in invoice values. The findings are scored and routed to a reviewer before the payment run.
Paid invoices are the most complete record of what an organisation actually bought, so AP data is the base layer of any spend analysis. Classified invoice lines show spend by category, supplier and business unit; payment terms and discount data show where working capital sits; and vendor-level payment behaviour feeds supplier performance and risk scoring. Procurement uses the same data to consolidate suppliers, renegotiate terms and find off-contract (maverick) spend, which is why AP analytics and spend analytics are usually run on one data set.
