Probabilistic AI in Controlling & Finance
Where probabilistic AI works—and where it doesn't
AI is making its way into Controlling and Finance – not as rule-based automation, but increasingly probabilistic: this refers to AI models that rely on probabilities, recognize patterns, and derive forecasts. Probabilistic AI does not make decisions, but rather provides decision support, including a statistical assessment of how certain or uncertain a result is. This AI does not function where legal certainty is to be replaced, but rather in contexts where decisions are prepared, focused, and accelerated. The actual competitive advantage arises not from the model itself, but from its integration into the processes, roles, and governance of the CFO organization.
What Is Probabilistic Artificial Intelligence?
Probabilistic artificial intelligence refers to AI systems that explicitly model uncertainty and output their results not as a fixed truth value, but as probability distributions or spaces of expectations.
Unlike deterministic systems, probabilistic models use statistical probabilities to account for incomplete, volatile, or "noisy" data.
For CFO organizations, the question is not whether, but where this approach delivers tangible value today without compromising transparency, controllability, and governance.
The good news is that there are already areas of application where probabilistic AI is useful, verifiable, and cost-effective.
Key Features of Probabilistic AI Relevant to the CFO’s Organization
- Dealing with uncertainty: Probabilistic models quantify uncertainty rather than obscuring it—for example, by calculating ranges, confidence intervals, or probabilities of occurrence.
- Distribution-based results instead of point values: Instead of a single forecast value, they provide possible outcomes with associated probabilities. This is particularly relevant for volatile markets and scenario planning.
- Learning from new information: Many probabilistic models (e.g., Bayesian approaches) adjust their expectations as soon as new data becomes available.
- Different types of uncertainty:
- aleatory uncertainty (randomness in the business process) and
- epistemic uncertainty (uncertainty resulting from a lack of data or poor-quality data).
- This is central to risk assessment and governance.
Where Can Probabilistic AI Add Value—and Where Can It Not?
Probabilistic AI is not suitable in cases where:
- legal determinism is required (e.g., account determination, depreciation logic)
- Accounting decisions are made definitively and cannot be reviewed
- the technical explanation of a result must be unambiguous
It is particularly well suited for use wherever:
- Suggestions, priorities, or probabilities can be helpful
- Decisions confirmed by humans
- It's about transparency, focus, and speed—not blind automation
1. Forecasting & Scenario Planning: From Static Plans to Dynamic Scenarios
Dynamic forecasts, rather than linear plan updates, enable early detection of
They detect structural deviations and are suitable for simulating multiple scenarios (realistic/best/worst). Probabilistic AI works here because forecasts can never be exact; rather, they always represent approximations of an otherwise uncertain future. Instead of hiding this uncertainty, probabilistic models make it visible by:
- probability intervals are used instead of point values, and
- Sensitivities in the driver context (price, volume, costs) are used.
As a result, decision-making quality improves while maintaining the same data set, and responsiveness is enhanced, particularly in volatile markets.
2. Working Capital & Cash Forecasting: Focus Instead of a Graveyard of Excel Numbers
In the areas of working capital management and liquidity planning, challenges regularly arise at the intersection of accounting, controlling, and corporate finance. Typical pain points are particularly evident in the context of the cash conversion cycle: A large number of open items makes it difficult to maintain an overview, manual prioritization by controlling staff is time-consuming, and management often takes a reactive approach based on feedback from divisional management.
This is where the added value of AI-powered methods comes into play: By using probabilistic AI, the likelihood of receiving payments can be accurately estimated. Critical debtors are automatically identified, and actions can be prioritized based on a risk profile. This results in data-driven, transparent working capital management that focuses on the truly relevant cases.
It is important to note that the final decision on how to proceed remains with the human. The AI provides recommendations on where intervention seems particularly sensible, but does not assume responsibility for selecting and implementing the measures. This ensures that working capital management remains focused and efficient without getting lost in a “sea of Excel numbers.”
3. Variance & Anomaly Detection: “Where should Controlling take a closer look?”
In the analytical approach of the Controlling department, a common problem arises: nearly every observation is treated as a variance, but not all of them are actually relevant. This is where probabilistic AI comes into play as a filter: It recognizes patterns that exceed fixed thresholds more quickly and identifies unusual combinations of factors—such as the combination of cost center, time period, and specific drivers. This intelligent pattern recognition can significantly reduce the number of “false positives,” i.e., results incorrectly classified as relevant.
4. Predictive Accruals & Provisioning: Reducing Blind Spots While Maintaining Accountability
Probabilistic AI can assist in identifying liabilities and outstanding obligations during closing processes.
The model-based approach is derived from recommendations based on historical patterns, expected values for plausibility checks, and indications of missing or unusual provisions.
In the context of accounting governance, AI thus suggests areas of investigation and measures, while humans make the final decision. The results must be explainable (explainable) and documentable. Deviations from the proposal are just as relevant as its acceptance: greater consistency, fewer blind spots, without negating the responsibility of the Human in the Loop.
5. Narrative Finance & Management Reporting: FP&A explained clearly and communicated in an integrated manner
The automatic structuring of comments in the budgeting process and in interim management reporting offers significant added value, particularly when it comes to summarizing complex financial information. This structured approach makes it possible to present the relevant data and analyses in a clear and comprehensible manner. This not only promotes transparency in financial processes but also facilitates the communication of results at various levels.
The same applies to the ongoing review of the equity story and its communication to the Management Board: The continuous review and adaptation of the equity story ensures that the key aspects of the business model and financial performance can always be presented in a timely and understandable manner.
As a result, financial reporting becomes easier to understand without losing any of its clarity or reliability. Responsibilities remain clearly identifiable and are not diluted. This makes management reporting not only more efficient, but also more integrated and transparent, which forms the basis for well-informed decisions.
Implementation: Probabilistic Programming in the Context of Bayes' theorem
Bayesian and probabilistic programming represent innovative approaches in the fields of machine learning and statistics. Unlike traditional modeling methods, models are not viewed as fixed rules but are formulated as probability distributions.
The central tool of this method is Bayes' theorem: it allows existing knowledge (prior) to be combined with new data (likelihood). This update results in a more precise prediction (posterior) that takes into account both previous findings and current observations.
Common PPL Libraries and Tools for Probabilistic Modeling in Finance
In the field of probabilistic programming, various specialized libraries and tools are used that are particularly well-suited for risk modeling, anomaly detection, and financial data analysis.
Commonly used PPL libraries today
- PyMC (PyMC3/PyMC4): This Python library is widely used for Bayesian modeling and is particularly well-suited for tasks such as impairment risks, the calculation of default probabilities, and provision models.
- TensorFlow Probability (TFP): TFP provides probabilistic layers and supports Bayesian neural networks. The library is particularly well-suited for scalable forecasts, such as revenue and cash flow projections.
- Pyro/NumPyro: Based on PyTorch, these libraries are particularly powerful for deep probabilistic modeling and are suitable for complex, data-intensive applications.
- Stan: Stan is a powerful, language-independent platform for statistical inference. It is particularly strong with hierarchical models and is suitable for corporate, country, and business unit structures.
Tools for Risk Modeling and Anomaly Detection in Accounting
For risk modeling and anomaly detection—for example, to identify posting errors, fraud risks, or to analyze accounts payable payment probabilities—the following tools are currently widely used in the finance sector:
- Vic.ai: Uses probability-based methods to detect unusual transactions, thereby supporting automated audit assistance.
- Trullion: Offers risk-driven automation of accounting workflows and an audit interface that facilitates the control and documentation of processes.
- Zest AI: Focuses on determining credit and default probabilities for financial institutions, thereby enabling precise risk assessment.
Many modern finance tools do not openly disclose their "Bayesian AI DNA," but they do use probabilistic models internally, for example:
- Sage Intacct (Predictive Insights)
- Workiva
- SAP Analytics Cloud (Predictive / Smart Predict)
New Generation Finance Consulting – Consulting Goes "Tech-Native"
The financial advisory industry is undergoing a transformation: More and more advisory firms are adopting "tech-native" approaches and integrating AI-based analytics platforms directly into their advisory services. A concrete example of this is the use of AI-powered platforms that enable the automated analysis of data from various sources, the early identification of risks, and the derivation of customized recommendations for action. The future SAP Business AI Platform is a concrete example of this. It combines data, AI, and platform in a single environment, integrates business context via knowledge graphs, and ensures governance, compliance, and security. Data from non-SAP systems is also taken into account. Joule, as a new interaction layer, then automatically orchestrates the appropriate processes and agents.
Advisors can already work with their clients to make data-driven decisions and visualize complex financial matters in an easy-to-understand way. The focus here is on consistent, individualized customer orientation: Instead of standardized solutions, consulting services are specifically tailored to the specific requirements and goals of each individual company. This individual approach ensures that the proposed AI-supported measures actually create measurable added value for the customer.
The professional interpretation of AI-generated results is of central importance in this context. AI systems provide probabilities, patterns, or anomalies; however, the task of evaluating and interpreting these results based on expert knowledge remains the responsibility of experienced consultants.
Overall, the combination of technological innovation, personalized customer focus, and strong governance is creating a new standard in financial consulting—one that is transparent, efficient, and trustworthy.
Written by
Prof. Dr. Martin Wünsch is an expert in financial reporting and SAP S/4Hana Finance Consulting. He is familiar with this field from various perspectives, e.g., Big4-Audit, Corporate Functions, or Management Consulting. He holds a chair in Business Administration, in particular in Int, Accounting & Controlling, at the FOM University of Applied Sciences Düsseldorf and regularly publishes on current topics in financial reporting.