In the era of digital transformation, the role of finance departments is evolving rapidly. Traditional approaches to financial management are giving way to solutions powered by modern technologies. It is becoming increasingly clear how artificial intelligence is changing finance – not only by automating repetitive tasks, but by supporting strategic decision-making, data analysis, and real-time forecasting.
In this article, based on a presentation by Piotr Modrzewski during a webinar co-hosted with Grant Thornton Poland, we take a closer look at how artificial intelligence is reshaping the operations of finance functions, the benefits it brings, and what the future holds for AI-powered finance.
Artificial Intelligence in finance – use case examples
1. OCR and Intelligent Document Processing
One of the oldest, yet most advanced and widely adopted applications of artificial intelligence in finance departments is OCR (Optical Character Recognition) and intelligent document processing. These technologies enable the automatic reading, classification, and analysis of financial documents – such as invoices, contracts, or receipts – without the need for manual data entry.
A standout example on the Polish market is Wolt, an international logistics and delivery platform. Operating across multiple countries and collaborating with a wide range of partners and suppliers, Wolt faced the challenge of processing thousands of invoices originating from various sources, in different formats and languages.
The solution came in the form of Rossum, a platform that enabled full automation of invoice processing, significantly improving Accounts Payable operations, accelerating payment workflows, and reducing language-related barriers.
Rossum Platform (1)
In the Polish context, it’s worth highlighting the role of the National e-Invoicing System (KSeF) (2), which will soon become mandatory for most businesses. This system introduces the standardisation of e-invoices and is expected to partially replace commercial OCR solutions, especially for domestic invoices. Interestingly, KSeF itself incorporates automation and AI technologies, streamlining the management of financial documentation.
Additionally, global technologies such as Agilent Technologies’ OCR, as well as platforms like Our Intelligence and RPA (Robotic Process Automation), support intelligent document processing. They combine content recognition with automatic data validation and seamless transfer to ERP systems, enabling finance teams to focus on analysis rather than manual data entry.
Benefits of Artificial Intelligence in financial services:
- Reduced operational costs – through the elimination of paper-based workflows and manual processing
- Faster response times – as documents are processed immediately upon receipt
- Minimised human error – thanks to automated data extraction and verification
- Improved compliance and auditability – with all records stored digitally
It’s worth noting that AI-powered financial solutions are available both as part of major platforms like SAP and as lightweight, low-code tools tailored to small and medium-sized enterprises. The key is selecting a solution that aligns with the scale of operations, document structure, and the financial team’s needs. This enables ongoing analysis of financial data, significantly accelerating reporting processes and improving their accuracy.
2. Robotic Process Automation (RPA) in financial and accounting operations
RPA (Robotic Process Automation) is one of the key tools driving financial transformation. It enables the automation of repetitive, rule-based tasks typically carried out by teams in accounting, controlling, and finance departments. As a result, professionals can redirect their focus towards more strategic activities that add greater value to the organisation.
One practical example of artificial intelligence in finance is the automation of cash flow forecasting. In this scenario, a bot collects data from various sources, ERP systems, accounting software, and even Excel spreadsheets, then aggregates and processes it. These AI-powered solutions are gaining popularity because they eliminate the need to manually switch between multiple applications, which is one of the most frequently cited pain points in large organisations.
Example: Automated travel expense reconciliation
A large service-based company with many mobile employees faced challenges in efficiently reconciling business travel and staff expenses. The process was time-consuming and required manual verification of documents, currency conversions, and compliance checks against company policy.
RPA solution – Automatic integration of HR systems (e.g. ADP/SAP SuccessFactors) with the Concur application. Bots extract reported expenses, verify documents, perform conversions, and check compliance. The entire reimbursement process is completed without human involvement, right up to the final approval stage. RPA can also support the automatic generation of financial and management reports for executive use.
Benefits of RPA implementation – process automation:
- Increased efficiency – bots work 24/7 without breaks, speeding up task execution
- Fewer errors – automation reduces the risk of human mistakes in repetitive tasks
- Better system integration – RPA allows multiple data sources to be consolidated in one environment
- Time and cost savings – implementation delivers measurable operational savings
3. Financial forecasting with Machine Learning (ML)
In an era of market uncertainty and rapid economic shifts, traditional financial forecasting methods are proving inadequate. As a result, more and more organisations are turning to machine learning (ML) – a technology that enables the creation of advanced predictive models that analyse historical data, learn patterns, and forecast future financial behaviours with high precision.
ML operates on algorithms that learn from data-analysing its structure, relationships, and variability. In a financial context, this may include data on revenues and expenses, customer and product information, market dynamics, as well as macroeconomic indicators, seasonality, and industry trends.
These ML algorithms process the information to create predictive models capable of estimating, for example, next quarter’s margins, expected changes in cash flow based on market conditions, or early warnings of potential liquidity issues.
Key stages of implementing ML in forecasting:
- Data collection – having access to high-quality financial data is essential
- Model training – the algorithm learns from historical data to identify patterns
- Testing and validation – ensuring the accuracy and reliability of forecasts
- Deployment and adaptation – models are continuously updated with new data
- Interactive use of outputs – enabling users to simulate various financial scenarios
A notable case study comes from the retail sector, involving Levi Strauss & Co., which struggled to forecast revenue accurately in an unstable market environment. By adopting the Wipro platform (3), the company developed a customised model that forecasted market trends, margins, and revenues using both internal and external data. The result was a significant improvement in forecasting accuracy, even under volatile conditions.
Benefits of ML in financial forecasting:
- Higher accuracy – ML models consider a wider range of factors and complex relationships, leading to more reliable predictions
- Speed and efficiency – forecasts can be generated automatically, saving time and enabling more frequent updates
- Adaptability – algorithms continuously learn and adjust to changing market conditions
- What-if scenarios – quick modelling of multiple forecast scenarios based on different business assumptions
Importantly, modern ML models can also integrate external market data, such as inflation rates, currency exchange trends, consumer sentiment, or trade policy changes. This gives businesses the ability to assess not only their internal performance but also build a more comprehensive view of external risks and opportunities.
4. Chatbots and Virtual Assistants: enhancing financial communication and process management
Solutions such as AI-powered chatbots and virtual assistants are becoming an increasingly important part of digital support for finance departments.
Thanks to the rapid advancement of generative artificial intelligence and Large Language Models (LLMs), these tools have undergone a significant transformation – evolving from basic rule-based bots into sophisticated assistants that support not only communication, but also substantive accounting and audit tasks.
Scope of chatbot applications – Artificial Intelligence in customer support
In finance departments, data security is of paramount importance. As a result, many companies opt for building dedicated, closed chatbot environments. These solutions allow the use of AI while maintaining full control over information flow and ensuring compliance with data protection regulations.
In the B2C space, voice and text-based assistants introduced by banks and financial institutions have gained popularity. For example, Nest Bank’s assistant (4), built using OpenAI’s GPT technology, handles customer inquiries related to products and transactions—greatly reducing pressure on call centres.
In corporate environments, artificial intelligence in finance is being used to support auditors by providing access to regulatory knowledge, reporting standards, and data interpretation.
Chatbots and AI assistants are used for:
- Responding to client and employee queries
- Assisting in the generation and editing of reports
- Translating complex financial data into simple language
- Integrating with ERP and accounting systems
How Artificial Intelligence works in practice – platforms and solutions
- Intuit QuickBooks Assistant (5)
In the accounting services sector, a noteworthy example is the QuickBooks Assistant, integrated into the widely used accounting platform. This tool supports both professional accountants and end-users in automating daily tasks-from invoice generation and receivables tracking to data reconciliation. Its intuitive interface means no advanced technical knowledge is required, allowing users to respond swiftly to real-time financial needs.
- Workday Assistant (6)
An increasing number of organisations are also using tools like Workday Assistant, which supports finance and HR departments in their day-to-day operations. It enables users to interact with data via natural language, whether reporting expenses, analysing figures, or generating reports and forecasts. Its presence significantly shortens access to critical information while reducing the operational burden on teams.
- Deloitte – DARTbot (7)
A similar tool is DARTbot, developed by Deloitte as an internal audit assistant. It supports audit teams in data analysis and report preparation by drawing on the organisation’s internal knowledge base and methodologies. As a result, auditors can obtain answers to complex queries more quickly and create consistent, high-quality documentation. This is a practical example of using large language models in a regulated environment, where quality, compliance, and timing are key.
- GT Polska – Audit Department Assistant
This solution has been designed to support auditors by accelerating data processing, identifying irregularities, and generating audit documentation with a high level of accuracy and compliance.
The system is based on a dedicated Large Language Model (LLM) trained on internal documents, audit procedures, and technical materials. As a result, the assistant can respond to detailed queries by paraphrasing source content and providing direct citations from relevant sections.
It not only eliminates the need for manual information retrieval but also significantly improves the quality and speed of audit report preparation. This is a prime example of intelligent operational support-automating processes while maintaining full expert control over the audit’s substantive content.
Benefits of implementation
- Time savings – 24/7 availability and rapid responses to queries
- Access to expert knowledge – chatbots can draw on internal knowledge bases and documentation
- Support for report and analysis generation – automation of recurring tasks
- Increased team productivity – less time spent on routine operations
5. Detecting financial fraud and anomalies
As the volume of financial data in organisations grows, so too does the risk of errors, abuse, and fraud. In this landscape, artificial intelligence is playing an increasingly critical role, leveraging advanced data analytics to identify financial anomalies in near real-time. This capability is particularly valuable in high-volume transaction environments such as banking, insurance, and global manufacturing.
How does anomaly detection with AI work?
AI algorithms are trained on historical transaction data. They analyse patterns and behaviours, then flag deviations that may indicate errors, fraud, or policy violations. Typical anomalies include duplicate payments, incorrect amounts, or transactions processed at unusual hours.
Once detected, the system generates alerts and corrective suggestions, enabling teams to respond swiftly and mitigate potential losses.
The global home appliance manufacturer – Electrolux – implemented an AI solution for monitoring and analysing employee expense claims. Previously, the process relied on manual review, which was time-consuming and inefficient. With AI, the company achieved:
- Time and cost savings
- Greater accuracy in anomaly detection
- Reduction in fraudulent claims
Industry spplications – financial services
Financial institutions like Mastercard and Citibank, as well as insurance providers, have long utilised AI to identify suspicious transactions and prevent fraud. For example, a system can monitor customer payments and instantly flag unusual activity, such as a sudden surge in transactions from an unexpected location or changes in spending habits.
In the insurance sector, AI is also used to analyse claims documentation, such as accident scene photos or claim history – to verify compliance with policy terms and detect potential fraud.
Organisational benefits
- Faster detection and response to threats
- Reduced financial loss risk
- Improved internal control quality
- Scalable, automated audit processes
- Increased transparency in financial operations
6. Automated transaction and account reconciliation
Many large financial organisations face the ongoing challenge of manually matching inflows, expenses, and transactions, especially when data originates from various sources such as ERP systems, CRMs, payment gateways, or sales applications.
Modern AI-driven systems can integrate this information, consolidate data using algorithms, and automatically identify and reconcile transactions without manual intervention. Finance teams can then focus solely on exceptional cases that require professional judgement or deeper investigation.
In large corporations, such as hotel chains or e-commerce platforms, where financial flows are distributed across multiple systems, these solutions provide full financial visibility and enable real-time identification of discrepancies.
A good example is the HighRadius platform, which automates large-scale payment and transaction reconciliation by aggregating data from multiple sources and applying predictive models to detect mismatches. The result is substantial time savings, improved accuracy, and stronger process control.
High Radius (8)
7. Generative Artificial Intelligence: new opportunities for finance teams
Generative AI marks the latest chapter in digital transformation. Unlike traditional algorithms that merely analyse or forecast data, generative AI can create new content, such as reports, summaries, chart interpretations, or strategic recommendations, based on input data.
One example is the “Juliusz” model (9), an assistant developed to support finance professionals in producing narrative content for reporting. It enables the automatic generation of chart descriptions, interpretation of numerical data, commentary for financial presentations, and data-driven recommendations.
These tools are emerging as intelligent collaborators, capable of translating complex information into clear messages, enhancing team creativity, and significantly reducing the time required to prepare reports.
Although Microsoft 365 Copilot is primarily designed as a general office assistant, an increasing number of organisations are implementing its features within finance departments—integrating tools like Excel, SharePoint, Outlook, and CRM systems.
At the same time, simpler low-code solutions such as Power Platform combined with Copilot enable rapid development of tailored financial assistants. This makes it an appealing option for SMEs looking for cost-effective and adaptable AI support in their financial operations.
The future of AI in finance – emerging trends
Hybrid Intelligence – merging human expertise with machine learning
One of the most significant upcoming trends is the rise of hybrid intelligence, a model in which human expertise is seamlessly combined with the capabilities of artificial intelligence. Rather than replacing finance professionals, AI acts as an enabler-supporting tasks like data analysis, report generation, formulating recommendations, and knowledge retrieval. This shift allows financial teams to focus on strategic and decision-making roles, while repetitive tasks are handled by machines.
This collaborative model also fosters the development of interdisciplinary teams, where AI technologies function as support tools rather than a threat to human roles.
Conversational Interfaces – Chat as the New Financial Control Centre
Another growing trend is the adoption of conversational interfaces, enabling users to interact with financial systems using natural language. Employees no longer need to master database structures, spreadsheet navigation, or SQL. Instead, the system can automatically produce answers, visualisations, and summaries on demand.
Advanced forecasting – smarter models and scenario planning
With rapid advances in machine learning and generative AI, organisations can now build highly accurate and adaptable financial forecasting models. These models go beyond historical data analysis, learning from real-time market trends, integrating external variables, and simulating multiple business scenarios.
Autonomous finance – AI-driven decision making
The most advanced (and still emerging) trend is autonomous finance, where AI independently handles routine financial operations, such as budget reallocations, payment approvals, report generation, and resource allocation.
Some global enterprises are already piloting this approach, particularly in environments with well-structured data and standardised processes. In the near future, we may see AI not only providing recommendations but also executing approved actions automatically, aligned with company policies and governance frameworks.
How to prepare for AI adoption in finance
Effective implementation of artificial intelligence in finance requires a structured and thoughtful approach. The first step is to assess the organisation’s digital maturity –specifically, reviewing existing systems and processes to determine their readiness for AI integration.
Next, it’s essential to define a clear strategy and prioritise areas with the highest potential return on investment. Data preparation is a critical stage, this includes consolidating and cleaning financial data sources to create a solid foundation for machine learning algorithms.
Before proceeding with full-scale deployment, organisations should begin with pilot initiatives. Testing solutions in a limited environment helps to minimise risk, validate assumptions, and build internal confidence before scaling AI more broadly across financial operations.
Developing AI capabilities must go hand-in-hand with upskilling finance teams—through targeted training programmes and fostering a data-driven culture. This ensures long-term transformation and smoother adoption of new technologies.
Benefits of AI in finance – driving financial efficiency
Implementing artificial intelligence in finance brings advantages across multiple levels, from day-to-day operational cost savings and improved analytical accuracy to strategic support in executive decision-making.
These benefits can be structured by increasing value, creating a clear hierarchy of AI’s impact on the financial organisation:
Greater accuracy – At the most basic level, AI supports finance functions by automating routine, repetitive tasks such as data entry, transaction matching, and compliance checks. This enables:
- Fewer human errors
- Improved data quality control
- Consistency and auditability of processes
Increased Efficiency – AI significantly boosts the productivity of finance teams by:
- Reducing the time required for analysis and reporting
- Automating workflows such as expense reconciliation and invoice processing
- Enabling 24/7 operations through bots and digital assistants
Deeper Insights – On a higher level, AI enables advanced data interpretation and highly accurate forecasting. Machine learning models analyse historical trends, internal data and external signals to:
- Forecast cash flow, margins, and market demand
- Detect anomalies and risks more rapidly
- Generate interactive dashboards and dynamic reports
Strategic Value – At its most advanced stage, AI becomes a strategic partner, enhancing business decision-making. AI provides not only data, but also meaningful insights, recommendations, and narratives that:
- Facilitate accurate executive decision-making
- Support scenario planning and long-term strategy
- Enhance organisational competitiveness
Artificial intelligence is transforming finance departments at every level—from automating routine processes and increasing control, to delivering deep insights and strategic guidance. Organisations that successfully combine financial expertise with AI capabilities gain not just efficiency, but also resilience in the face of market volatility.
As the technology matures, the role of finance becomes increasingly strategic. For finance leaders, the moment of decision is now: remain within traditional models, or embrace AI’s potential to redefine financial management for the future.
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