Receivables Management Agent
Analyse receivables portfolio, assess default risks, determine bad debt allowances.
Calculates receivables ageing structures, assesses default risks via ML model, determines bad debt allowance requirements and monitors credit limits. Strategic decisions (payment arrangements, factoring, legal action) remain with the human.
Score Dashboard
What This Agent Does
Receivables management goes beyond the individual dunning run. It analyses the entire receivables portfolio: how old are the receivables? Which debtors have a high default risk? Where is a bad debt allowance needed? Which credit limits are being exhausted?
The Decision Layer breaks receivables management into eight decision steps. Three are rule-based (ageing structure, credit limit, reporting), one AI-assisted (default risk scoring). Four decisions remain with the human: specific bad debt allowance, payment arrangements, factoring assessment and legal escalation. Here, strategic judgement is required that goes beyond rule application.
The result: the receivables ageing structure is available in real time. Default risks are proactively identified, not reactively discovered. And strategic decisions are made on a better data basis - with full transparency over the receivables portfolio.
Micro-Decision Table
Calculate receivables ageing structure How are open receivables distributed by age? Rules Engine
Arithmetic calculation of ageing buckets
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Default risk scoring How high is the default risk per debtor? AI Agent
ML model based on payment history, industry and external data
Decision Record
Challengeable: Yes - fully documented, reviewable by humans, objection via formal process.
Determine bad debt allowance (general) How high is the general bad debt allowance? Rules Engine Auditor
Flat rates by ageing bucket and industry
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Challengeable by: Auditor
Determine bad debt allowance (specific) Is a specific bad debt allowance required? Human Auditor
Human assessment for concrete default indicators
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Challengeable by: Auditor
Credit limit monitoring Is a credit limit being exceeded? Rules Engine
Threshold check against stored limit
Decision Record
Challengeable: Yes - rule application verifiable. Objection possible for incorrect data or wrong rule version.
Propose payment arrangement Should an instalment plan or deferral be offered? Human
Strategic decision in negotiation with the customer
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Factoring assessment Should receivables be assigned to a factor? Human
Strategic decision with cost-benefit analysis
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Escalation to legal department Should legal action be pursued? Human
Strategic decision weighing cost, likelihood of success and customer relationship
Decision Record
Challengeable: Yes - via manager, works council, or formal objection process.
Decision Record and Right to Challenge
Every decision this agent makes or prepares is documented in a complete decision record. Affected parties (employees, suppliers, auditors) can review, understand, and challenge every individual decision.
Prerequisites
- ERP system with accounts receivable and complete payment history
- Credit limit definitions per debtor
- Historical default data for ML training (min. 24 months)
- Defined general allowance rates per ageing bucket
Governance Notes
GoBD relevance: high - bad debt allowances on receivables are balance-sheet-relevant and an audit focus of the statutory auditor. Specific bad debt allowances require human judgement (HGB Paragraph 252). The four human decisions (specific allowance, payment arrangement, factoring, legal action) reflect the actual governance requirement: these decisions have strategic and financial significance beyond rule application.
§203 StGB-relevant data is encrypted end-to-end and never passed to AI models in plain text.
Process Documentation Contribution
Infrastructure Contribution
The Receivables Management Agent builds the receivables analysis infrastructure. The default risk scoring is reused for credit limit setting for new customers. The ageing structure calculation feeds into the month-end close. The allowance logic is used by the Annual Statement Agent. The reporting (DSO, ageing) becomes part of the CFO dashboard.
Related Agents
Invoice Generation Agent
Generate outgoing invoices - Paragraph 14 UStG, e-invoicing, GoBD-archived.
Dunning Agent
Monitor due dates, determine dunning levels, calculate default interest - automatically escalated.
Cash Application Agent
Read incoming payments, assign to debtors, clear invoices - automatically reconciled.
Frequently Asked Questions
How does the default risk scoring work?
The ML model evaluates each debtor based on payment history, industry, company size and external signals. The scoring is regularly updated. The decision record transparently shows which factors contributed to the score.
Why are so many decisions human?
Four of eight decisions require human judgement - that is not a deficit but correct governance. Specific bad debt allowances are balance-sheet-relevant (HGB). Payment arrangements and factoring are strategic decisions. Legal action has legal consequences. The agent provides the data basis; the decision remains with the human.
How often is the receivables ageing structure updated?
Daily or in real time - configurable. Credit limit monitoring is automatic and triggers a notification immediately on breach. The monthly reporting (DSO, ageing) is automatically generated at the reporting date.
Implement This Agent?
We assess your finance process landscape and show how this agent fits your infrastructure.