From collections prioritization to month-end close acceleration, DreamFactory + AI transforms the most painful, repetitive tasks in finance into natural language queries that take seconds.
Finance teams spend hours building AR aging reports in Excel. By the time the report is ready, the data is already stale. Collections are prioritized by gut feeling rather than data-driven risk assessment.
Execute GetARAgingWithPaymentHistory pulling current AR, historical payment patterns, credit terms, and customer tier from ERP
Calculate collections priority score combining amount outstanding, days past due, payment velocity trend, and customer lifetime value
Call {erp}_update_records to stamp priority score and recommended action on each AR record
Post-process hook sends daily notification to collections team with top 10 priority accounts via Slack webhook
Collections team starts each day with an AI-prioritized worklist written back to the ERP. No more Excel. DSO reduced 15-25%.
Staff accountants spend hours manually matching purchase orders to vendor invoices to receiving documents. It's the most repetitive task in finance and the #1 bottleneck to closing the books each month.
Execute GetUnmatchedDocuments pulling open POs, pending invoices, and receiving documents into a single result set
Three-way match: PO line items vs. invoice line items vs. receiving quantities, applying vendor-specific tolerance thresholds
Call {erp}_create_records to create match records for clean matches and exception records for discrepancies
Generate exception report as CSV via {fs}_create_file on S3 for AP manager review
85%+ of invoices auto-matched and recorded. AP team reviews only exceptions. Month-end close cut by 40%.
Companies with large supplier networks struggle to catch pricing discrepancies, duplicate invoices, and contract violations across hundreds or thousands of vendor invoices monthly.
Query vendor master (Oracle) for contracted rates and AP system (SQL Server) for submitted invoices via two DreamFactory services
Compare line-item pricing against contract rates, flag duplicates by invoice number/amount/date, check quantity discrepancies
Call {ap}_update_records to flag discrepant invoices with exception codes and dollar variance amounts
Execute UpdateVendorScorecard to refresh vendor compliance metrics based on exception history
Every invoice validated against contract terms before payment. AP processing time cut by 50%+. Vendor scorecards updated automatically.
Companies that sell through retail channels lose millions annually to invalid deductions and chargebacks. Tracking and disputing these across multiple retailers is manual, time-consuming, and often abandoned.
Execute GetOpenDeductions pulling deduction records, return authorizations, and proof-of-delivery from ERP and logistics systems
Match deductions against valid return authorizations; classify each as valid, invalid, or needs-research; calculate recovery probability
Call {erp}_update_records to update deduction status, classification, and priority for the disputes team
Generate dispute letters for high-value invalid deductions with supporting documentation references
Invalid deductions identified and dispute-ready within hours, not weeks. Recover 2-5% of revenue from retailer billing errors.
Credit risk assessment relies on periodic reviews and static credit limits. Accounts can deteriorate rapidly between reviews, leading to bad debt write-offs that could have been prevented.
Execute GetCreditRiskInputs pulling payment history, order patterns, AR aging, credit utilization, and external risk signals
Calculate composite credit risk score using payment velocity trend, utilization rate, industry benchmarks, and anomaly detection
Call {erp}_update_records to update credit risk score and recommended credit limit adjustment on each account
Pre-process hook on new orders: if customer risk score exceeds threshold, block order and alert credit manager
Credit risk scores update continuously, not quarterly. High-risk accounts flagged before they become bad debt. Order holds triggered automatically.
Month-end close takes 10-15 business days at many companies. The process involves chasing down reconciliation items, matching transactions, and manually compiling reports across disconnected systems.
Query GL, subledger, bank reconciliation, and intercompany data across multiple DreamFactory-connected databases
Identify unreconciled items, match transactions across systems, flag missing entries and timing differences
Call {gl}_create_records to post AI-suggested adjusting entries as drafts for controller review
Generate close status summary report with open items, estimated completion, and risk areas
Reduce close from 10-15 days to 5-7 days. Controllers review AI-generated adjustments instead of manually hunting for discrepancies.
Stop building reports manually. Start asking your data questions in plain English.