How SAP AI is revolutionising corporate finance processes

In today’s business environment, where speed and reliability in financial decision-making are vital, AI is becoming a strategic ally for finance departments. And SAP understands this well!

Integrated into SAP S/4HANA and SAP Analytics Cloud (SAC), SAP AI automates time-consuming tasks, makes processes more reliable and transforms data into a powerful financial management tool.

From accounting closures to cash flow forecasting, anomaly and fraud detection, SAP AI offers finance teams a unique opportunity: to move from an operational role to one of analysis and strategic advice.

How? Sileron has summarised the key points for you.

First of all, what is SAP AI?

AI at SAP is defined as the use of artificial intelligence technologies natively integrated into business processes to:

automate repetitive tasks (invoicing, reconciliations, accounting entries),
detect anomalies and secure transactions,
provide intelligent recommendations based on data,
generate financial, logistical or HR forecasts
adapt to regulatory changes.

Your SAP publisher therefore offers you not just “generic” AI, but business AI. This is designed to directly meet the needs of your finance, supply chain, HR, purchasing and logistics functions.

What are the different forms of SAP AI for finance?

At a time when AI is being talked about indiscriminately, it is important to redefine AI in its various forms.

It is clear that SAP’s objective here is not to provide generic AI, but rather contextualised AI. In other words, AI that is integrated into financial, logistics and HR processes to accelerate the digital transformation of the company’s services.

This includes:

1. Embedded AI

Included in SAP S/4HANA and SAP S/4HANA Cloud ERP systems, it enables the following for finance departments:

automatic bank reconciliations,
intelligent invoice processing,
intelligent suggestions in workflows (e.g. invoice approval).

It is ready to use and designed for immediate productivity.

2. Machine learning

SAP has long integrated machine learning models to:

learning from historical data,
continuously improve the accuracy of financial forecasts (cash flow, DSO, cash flow),
detect accounting anomalies or fraud patterns.

These models can be pre-trained and standard (provided by SAP) or customised via SAP BTP (Business Technology Platform).

3. Process Automation (SAP Build Process Automation)

Formerly grouped under SAP Intelligent RPA and SAP Workflow, SAP Build Process Automation is the SAP BTP building block that enables you to automate and orchestrate business processes, particularly financial processes in and around your SAP ERP.

It is a low-code/no-code solution that will enable you to:

combine RPA (Robotic Process Automation) and AI,
end-to-end automation of your financial workflows: order validation, creation of accounting entries, reconciliations, etc.
add “intelligent decision-making” through AI, where RPA is limited to mechanical execution.

For example, SAP has recorded a 71% reduction in the effort required to reconcile customer accounts. Thanks to its AI, 90% of payments are automatically matched to bank statements.

Note: SAP Build Process Automation integrates natively with S/4HANA Finance ERP.

4. Natural Language Processing (NLP)

You have probably heard about Joule, SAP’s new conversational co-pilot based on generative AI and NLP.

But did you know that before Joule, SAP was already using natural language processing (NLP) in its solutions? This was particularly the case in SAC and chatbots. However, the applications were limited and the uses more restricted.

Today, SAP Finance users can:

interact with SAP ERP using natural language: financial chatbots, virtual assistants,
perform intelligent searches for documents or transactions.

For example, an accountant can ask SAP to “show me all invoices awaiting approval this month“. And the time savings are clear! In S/4HANA Cloud Private Edition, information searches are 95%* faster than before. Your accountant can therefore expect to find the invoices they are looking for in just 30 seconds*, compared to an average of 10 minutes* previously.

*Source: SAP

5. Predictive and analytical AI

Traditionally, finance teams worked on the basis of historical data. Now, with SAP AI, you can rely on analytics to understand what happened and why. But you can also use predictive analytics to anticipate what might happen.

This means you can rely on:

integrated sales forecasts and cash flow,
financial scenario simulations,
early detection of late payments or liquidity risks
the ability to transform and present your financial data into powerful strategic analyses.

With AI, SAP S/4HANA combines the best of analytics and predictive technology to help finance managers and CFOs make quick, informed decisions. SAP estimates a 50% time saving on financial reporting. In this way, it transforms the finance function into a true strategic partner for the business.

Saving time, improving accuracy and reducing costs: the key challenge of automatable financial processes

As we have seen, AI in SAP S/4HANA (and its extensions via SAP BTP) enables the automation of a large number of financial processes, particularly those that are repetitive and time-consuming, based on structured data and subject to rules (accounting, tax or regulatory).

In its report “AI and automation in Finance 2024”, SAP highlighted the main benefits expected by financial leaders when integrating automation and AI into their processes:

Comparative table – Examples of finance process automation with SAP AI

This non-exhaustive table lists the automations you can consider in S/4HANA to improve the performance of your finance department:

Finance process	Automation using SAP AI	Key benefits
Financial closing	Automation of recurring entries Account reconciliation Detection of anomalies in entries	Reduction in closing time Reduction in errors Increased transparency
Accounts payable	Automatic extraction of invoice data Account reconciliation Detection of duplicates or fraud	Time savings Reduction in processing costs Improved compliance
Accounts receivable	Cash application with machine learning for automatic payment reconciliation	Reduction in collection times Improved cash flow
Cash flow forecasts	Analysis of historical flows Predictive scenarios	Improved forecast accuracy Better liquidity management
Budget planning	Automatic generation of forecasts Dynamic adjustments based on actual data Variance detection	More reliable budgets Accelerated planning processes
Compliance and fraud detection	Detection of transaction anomalies Configurable rules and scoring	Reduced risk of fraud Securing financial processes

Fraud detection: a major advance for finance departments thanks to AI

According to the ACFE (Association of Certified Fraud Examiners), companies lose an average of 5% of their annual revenue due to fraud. This can amount to tens or even hundreds of millions of pounds per year.

AI-based anomaly and fraud detection relies on a combination of machine learning, advanced statistics and, in some cases, business rules. In an environment such as SAP S/4HANA Finance or SAP Analytics Cloud (SAC), this translates into integrated algorithms capable of identifying unusual behaviour in massive financial flows.

This is based on:

Real-time analysis of massive amounts of data

AI ingests millions of financial transactions (payments, invoices, accounting entries). It detects deviations from normal patterns: unusual amounts, duplicates, unusual transactions.
If, for example, a supplier sends 10 identical invoices on the same day, the system will automatically flag this.

Supervised learning

Thanks to machine learning, AI is trained on historical fraud data. It learns to recognise the typical characteristics of fraud. When a new transaction has a similar profile, it is flagged as suspicious.

Unsupervised learning (anomaly detection)

AI can analyse data without a predefined model. It builds a statistical reference for "normality". Any transaction that deviates from this pattern is considered an anomaly. For example, if a customer who always pays within 30 days suddenly pays within 120 days, an anomaly will be detected.

Risk scoring detection

Each transaction is assigned a risk score (low/medium/high). The factors taken into account include the amount, the customer's history, the frequency of payments, the geographical location and even market conditions. This allows financial controllers to focus on the most risky transactions, i.e. those with a high score.

Relationship and network analysis

AI does not only look at individual transactions. It also analyses the relationships between actors. If a supplier is registered at the same address as an employee, it is able to identify this and send an alert.

Proactive detection via alerts and scenarios

With SAP Fraud Management or SAP Analytics Cloud (SAC), AI generates real-time alerts when an anomaly is detected. It can also simulate scenarios to anticipate potential fraud patterns.

This is a huge step forward, enabling financial services to reduce losses, secure processes and strengthen compliance while saving considerable time. SAP reports a 2%* reduction in fraud-related losses.
*Source: SAP

The integration of Artificial Intelligence into SAP heralds a new era for finance departments. As we have seen, there are many concrete use cases. Beyond saving time and reducing errors, AI enables the finance function to become a strategic player in the company, capable of anticipating and driving performance.

Leveraging SAP AI through your S/4HANA ERP or SAP Analytics Cloud is much more than just a technology project: it is a driver of competitiveness and innovation.

To find out more, contact our dedicated ERP Finance team.

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How SAP AI is revolutionising corporate finance processes

First of all, what is SAP AI?