Order-to-Cash

Collections & Receivables Agent

Ages receivables, predicts late payers and tells the team which customers to chase first.

All agents Next: Journal Entry & Recon

An example conversation with the agent

AI Impact

43%

Lower uncollectable balances

32%

Reduction in days sales outstanding

33%

Faster AR cycle times

Key Capabilities

Payment Risk Scoring

ML-based scoring of invoice payment probability

Collection Prioritization

Risk-ranked collection queues by entity

Payment Prediction

Forecasts payment dates from historical patterns

Credit Monitoring

Tracks customer credit deterioration signals

Scenario

David Ng, Accounts Receivable Manager

DSO rising fast — CFO wants a recovery plan this week

DSO at 41 days, up from 34. Peer median is 38. The CFO wants a recovery plan

“Score our receivables portfolio — which accounts need immediate attention?”

Sembcorp: S$1.15M, 22 days overdue, average days late climbing from 15 to 22 over six months

“Pull Sembcorp's payment history — is this a trend or a one-off?”

Thai Precision: S$540K, 31 days overdue, credit downgrade to B-, radio silent — plus a S$230K open order in the pipeline

“Should we place an order hold on Thai Precision until the S$540K is resolved?”

Resolving both recovers S$1.69M and drops DSO to 36 — below the peer median

“Model the DSO impact if we resolve Sembcorp and Thai Precision”

Accounts Receivable Ledger — 48,215 Records

Open and closed invoices with aging classification, customer details, and outstanding balances

Field	Description
Invoice Number	Unique invoice identifier (e.g., INV-2025-00482)
Customer	Customer name and account code
Amount / Balance	Original invoice amount and remaining outstanding balance
Due Date	Payment due date and days past due
Aging Bucket	Current, 1-30, 31-60, 61-90, 90+ days classification

Customer Payment History — 127,340 Records

Historical payment transactions including timing, amounts, and application to invoices

Field	Description
Payment ID	Unique payment transaction identifier
Customer	Paying customer name and account
Amount	Payment amount and currency
Payment Date	Date payment was received
Days to Pay	Number of days from invoice date to payment (actual vs. terms)

Credit History — 3,842 Records

Customer credit profiles including scores, limits, and risk indicators

Field	Description
Customer	Customer name and account code
Credit Score	Internal credit rating (A+ through D)
Credit Limit	Approved credit limit and current utilization
Risk Indicators	Late payment frequency, dispute history, financial health flags

Dunning Records — 18,906 Records

Collection activity logs including dunning letters, calls, escalations, and outcomes

Field	Description
Dunning ID	Unique collection activity identifier
Customer / Invoice	Associated customer and invoice references
Dunning Level	Escalation stage (Reminder, Warning, Final Notice, Legal)
Action Date	Date of collection activity
Outcome	Result (Promise to Pay, Partial Payment, Disputed, No Response)

get_aging_report

Generate an accounts receivable aging report segmented by time buckets, customer, or business unit.

Input

as_of_date customer business_unit min_amount

Output

Aging summary with balances by bucket, customer totals, and DSO metrics

predict_payment

Predict payment likelihood and expected payment date for open invoices using historical patterns.

Input

customer invoice_id horizon_days

Output

Payment probability score, predicted date, and confidence interval

prioritize_collections

Rank overdue accounts by collection priority based on amount, age, payment probability, and customer value.

Input

min_days_overdue min_amount segment limit

Output

Prioritized list with risk score, recommended action, and expected recovery

search_invoices

Search and filter invoices by customer, amount range, status, date, and aging bucket.

Input

customer status min_amount aging_bucket keyword

Output

Invoice records with customer, amount, status, due date, and aging details

assess_credit_risk

Evaluate customer credit risk using payment history, financial indicators, and exposure analysis.

Input

customer requested_limit include_external

Output

Risk score, recommended credit limit, payment terms, and risk factors

generate_dunning

Create dunning communications and escalation schedules tailored to customer profile and overdue severity.

Input

customer invoice_ids dunning_level channel

Output

Dunning letter content, escalation timeline, and recommended next steps

Collections & ReceivablesMeridian Industries · November close

Example