The future of credit management - step by step

We already took an in-depth look at when credit insurance makes sense in operational credit management and how a risk portfolio can be managed in the April 2025 issue of the KPMG Corporate Treasury Newsletter.

In this article, we will now highlight technical solutions for credit management and analyze how the use of artificial intelligence (AI) can change processes in this area in the future.

A digital credit management system aims to create an information base for assessing risks at any time and making decisions based on this information. A proven method of streamlining decision-making compared to manual processing is to aggregate, analyze and visualize data in a software solution—ideally automated so that the necessary information is displayed automatically as soon as changes occur, eliminating the need for follow-up inquiries. These solutions reveal how debtors are distributed across rating classes and how important key figures develop over time. They also help with automated workflows, which can then be easily customized to make your processes even smoother. The combination of market experience and targeted data analysis lies at the heart of successful credit management software. When it comes to a debtor's risk profile, it is also necessary to take a differentiated view in order to distinguish between new and existing customers in the assessment, for example, as different levels of detail may be included in the assessment. Data suppliers are a valuable resource for building up suitable information pools for individual industries, as they can provide relevant data via their interfaces, supported by credit management software. This data can then be weighted in relation to your company's specific circumstances in order to assign appropriate risk scores to your own debtors. This is where all the benefits of modern solutions really shine, letting you tailor your credit policy to your needs and keep it flexible. So, using a software solution is a smart move for your bottom line because it lets you automatically manage your risk portfolio based on your credit policy.

So, what role does AI play in such software for credit management? A traditional operational solution puts a company's individual credit policy requirements into practice and automatically keeps the data up to date and accurate so you can make smart decisions, take action and avoid mistakes. Since the amount of data involved in decision-making keeps growing, AI is a great way to replace traditional manual rules with a model that learns from available data. The advantage of this is that rules and patterns that were previously mapped in individual credit policies based on expertise can now be generated based on the company's own objective data, and the effort required to identify them can be automated. This puts a strong focus on data quality requirements, which, in addition to the development of AI strategies in credit management, can have a positive impact on a company's management in companies.¹ Going beyond conventional status quo analysis, AI can also make predictions about the future and, through generative AI, automate manual routine tasks that previously seemed too complex. Depending on the process in credit management software, it is important to consider which tasks AI can support. When it comes to evaluating structured data, machine learning is suitable for training a model, whereas language-related tasks such as summarizing texts or extracting information from a text can be mapped using language models (e.g., Mistral Large or GPT-4o). A real-life example of how this works is risk analysis based on texts like news articles, social media posts and other publicly available company publications, which previously had to be evaluated manually. This not only makes it possible to include new insights in decision-making, but also to capture them automatically.

Just like in the software industry, the key to success for credit management software is how easy it is for end users to work with. In the shift toward credit management based on AI software, it's not just about automated decision support, but also about making sure that decisions are clear and transparent. This requires the use of procedures and explanatory models that can be understood by end users. This is in line with the EU AI Act, which stipulates that employee training programs should ensure that staff are aware of both the potential and the limitations of AI-based outputs. Humans will clearly continue to play a key role in credit management, but the nature of their work will change. Until now, time-consuming information gathering, research, and verification were part of the job, but AI can do these tasks in a fraction of the time and in a way that's customized to what someone making credit decisions actually needs. This means that credit decision-makers can use AI to get the info they need to make decisions quickly and in a way that's easy to understand.

Generally speaking, the software used in credit management is unlikely to undergo any major changes, as it already supports a process-oriented data strategy that paves the way for the successful implementation of AI. However, it remains to be seen to what extent software will be allowed to make automated decisions going forward, as this will depend on the scope of its assigned responsibilities and regulatory restrictions. In this context, both the experience with and understanding of AI will be drivers for trust and thus also for shaping its use in the transformation of traditional processes in credit management. As things stand today, it is therefore safe to assume that the software landscape will also be shaped step by step – through the continuous development of processes and the targeted, gradual use of AI as a strategic player.

Source: KPMG Corporate Treasury News, Edition 154, May 2025
Guest author:
Dr. Tobias Vinhoven, Product Owner AI, Prof. Schumann GmbH

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¹ Source: https://klardenker.kpmg.de/der-schluessel-fuer-geschaeftserfolg-hochwertige-daten/ (Accessed on 11.04.2025) 

_{The views and opinions expressed in guest articles are those of the author and do not necessarily reflect the views and opinions of KPMG AG Wirtschaftsprüfungsgesellschaft, a stock corporation under German law.}