Automation Approaches in Credit Management

In an increasingly volatile global economy, shaped by geopolitical tensions, supply chain risks and rising financing costs, customer credit management is moving to the forefront of strategic treasury topics. The ability to identify customer risks at an early stage and manage them efficiently determines not only a company’s liquidity position, but also its resilience to external shocks. This means that not only accounts receivable management is affected, but treasury is indirectly involved as well.

Traditionally, credit management has been heavily manual – with Excel‑based limit‑setting processes, delayed credit checks, and fragmented interfaces to insurers and information providers. New technologies, especially in the field of artificial intelligence (AI), now make it possible to achieve deep automation and intelligent decision support.

Introducing automated processes in credit management is not a "big bang" project, but rather a step-by-step optimization. New processes must first be established across the organization's standardized process chain and dependencies before the next step can be taken. Many successful companies begin with three clearly defined preparations that lay the foundation for automation potential before gradually advancing their transformation. The following approaches have proven effective, with the priority of each approach depending on the current maturity level of the company's credit management.

1. Data Harmonization – Clean Data as the Foundation for Automated Credit Management
Without clean data, meaningful analysis and effective automation are impossible. This applies to all data-driven decision processes, including credit management. A central quick win is harmonizing relevant data sources, particularly:

• Customer data: Unified master data, consolidated business partner structures
• Exposure data: Clear definition of open receivables, payment terms, and due dates
• Payment receipts: Matching open items with payment receipts

Initially, credit agencies offer services to help identify duplicates or incorrect customer records. For ongoing operational business, a governance approach is needed to prevent the creation of erroneous or redundant data sets.

2. Process Harmonization – Creating Uniform Procedures
Once data is consistent, the next step is harmonizing processes. The goal is to define a functional guideline for credit management: clear responsibilities, escalation levels, decision logics, and the tools or systems used. Clarity about credit management processes and responsibilities forms the basis for optimizing and consolidating these processes.

3. System Support – The Automation Foundation
Automation requires a future-proof platform. Many companies still work with fragmented and unintegrated solutions (e.g., MS Excel) or outdated ERP systems that do not allow for seamless process logic. A crucial optimization is establishing a central credit management platform. There are various solution providers, such as SAP Receivables Management, Professor Schumann, or HighRadius.

Systems not only provide the technical foundation for automation but also help maintain an overview of master and transaction data and establish internal and external interfaces.

Once the above data, processes, and system selection are prepared, initial process elements can be fully automated. It usually takes some time to build trust in data quality and process handling within the system. Once sufficient confidence is established, processes can be automated step by step.

Below is an overview of proven automation levers:

• Scoring models for business partners: combining internal data (e.g., payment behavior, dunning history) with external creditworthiness data to assess business partners
• Automatic limit setting: for lower exposures based on predefined decision rules
• System support for manual steering: pre-filling scoring templates with automated recommendations for limit amounts
• Ordering business information: automated ordering of credit reports or business data sets based on predefined rules (e.g., above a certain exposure, for customers in high-risk countries, or specific customer industries)
• Handling credit insurance policies: automated limit requests and overdue or damage notifications via system interfaces to the credit insurer
• Automatic reports: creation of reports and dashboards with automated report distribution and real-time data
• Workflow mapping: representation of manual process steering as workflows with system and email notifications

AI is also a potential automation lever in credit management. Generative AI solutions, for example, can be used to summarize extensive annual financial statements of business partners, extracting relevant information and data to populate downstream data tables in the credit management system. This facilitates the review and understanding of documents, especially during intensive manual checks. Credit analysts can then focus entirely on their core expertise – assessing customer risks.

Other AI applications include classic data evaluation, backtesting and extrapolation of decisions. An AI assistant can help continuously optimize scoring models, enabling more opportunities for automated score and limit assignment. AI can also enable faster responses to current macroeconomic shocks.

As always with AI, the provided data must be reliable, and results must be critically reviewed before use.

Further process optimization can be achieved by integrating collections and dispute management into the same system landscape. Significant efficiency gains often arise from reducing manual work at the interface to upstream credit‑management processes and along the value chain. The depth of integration into the systems plays a crucial role, because in collections and dispute management, predefined system rules, AI‑driven insights, or even robot‑based automation can then be deployed.

One of the most important functions is the automated creation of worklists for the collection of overdue receivables, the automation of customer communication in case of disputes, and providing smart tools to agents so they can resolve discrepancies faster.

Based on predefined rules and real‑time data from sales and accounting, prioritized worklists for collection agents can be generated automatically, with overdue customers ranked by severity and/or default risk. The collection agent is the person in the organization who specializes in recovering outstanding payments, contacts customers accordingly, negotiates payment plans and, if necessary, initiates legal action. In this way, liquidity is safeguarded and financial risks are minimized.

Business AI can also use historical data to forecast payment inflows, thereby enabling intelligent prioritization for the collection agent. This automatically pushes the most critical open invoices to the top of the worklist.

Moreover, promised payments or dispute cases agreed with customers can be checked and documented centrally, along with all relevant information from upstream and downstream processes. This significantly improves customer interaction and enables the automatic generation and dispatch of the necessary correspondence for follow‑up actions. Finally, automated follow‑up workflows can be set up so that promised payments are checked in a time‑specific manner. Clean documentation of all customer contacts supports truly cross‑functional collaboration.

Disputes, or “dispute cases”, are often recorded as part of collections work, but can also be reported directly by customers via customer support, hotlines, complaints channels or other touchpoints. For downstream finance processes, it is important that this information reaches accounting promptly so that appropriate measures can be taken. A classic response is to place an immediate dunning block when, for example, a faulty delivery has been logged as a dispute. In the digital age, customers expect information to flow quickly and automatically within the company so that their dispute is acknowledged and resolved as swiftly as possible.

There are several AI use cases in dispute management. AI can act as a dispute resolution agent, for instance, analyzing invoice and contract data, identifying inconsistencies and proactively suggesting solutions to the dispute case resolver, such as issuing a credit note. At the same time, the system can be configured so that certain types of disputes are automatically written off if defined conditions are met.

To bridge collections and dispute management, it is also possible to generate dispute cases automatically at the time receivables fall due, triggering investigation and resolution. A rule‑based configuration can again be applied here, for example depending on the size of the receivable.

Integrating dispute processes into logistics within an intelligent organization leads to significant efficiency gains. When returning goods due to over‑delivery, quality issues, transport damage or short deliveries, customers can select an appropriate complaint category. Based on specific complaint reasons, dispute cases can be generated automatically and routed to the responsible team. In an integrated system, this works across the entire process chain and all stakeholders have up‑to‑date information on each case at all times.

Subsequent finance processes can be halted, for instance by blocking dunning on an overdue receivable until the dispute case has been resolved. This ensures that information from the dispute case is distributed to all key stakeholders in real time and that follow‑on activities are triggered immediately.

In the final expansion stage of this scenario, it is also conceivable for different agents for dispute cases from logistics, accounting and collections to collaborate. Within predefined guardrails, these agents can prepare decisions automatically or even execute them end‑to‑end. This setup is referred to as “agentic AI”, because agents from different domains communicate with one another – much like employees today – to resolve recurring problems in a consistent way.

By integrating automation approaches across credit management, collections and dispute management, organizations not only achieve efficiency gains but also improve data quality and transparency, enabling better‑founded decisions and faster responses to customer requests. At the same time, manual inputs and interactions are significantly reduced, which minimizes errors and further increases efficiency.

Ultimately, this results in improved customer communication and therefore higher customer satisfaction. A holistic view of the customer relationship allows an intelligent organization to manage credit risks and receivables proactively, rather than operating in fragmented silos.

Standardization and automation, combined with higher process speed, create tangible quantitative effects – for example, a positive impact on working capital by shortening days sales outstanding and reducing risk cases in the receivables portfolio.

Periods during or immediately after a major ERP transformation project are particularly attractive moments to challenge existing processes and system support in credit management. In many cases, previously unused options in standard modules can be leveraged to unlock innovations embedded in the new software for one’s own organization.

This creates an excellent opportunity to lift the company to the next level of automation and to actively shape its future.

Source: KPMG Corporate Treasury News, Edition 160, November 2025
Authors:
Börries Többens, Partner, Finance and Treasury Management, Corporate Treasury Advisory, KPMG AG
Lukas Kallup, Manager, Finance and Treasury Management, Corporate Treasury Advisory, KPMG AG
Kathrin Jürgens, Senior Managerin, Consulting, Technology Transformation, KPMG AG