AI for finance teams: How to future-proof your financial processes

Artificial intelligence (AI) has fundamentally transformed how businesses operate — and the pace of change is accelerating. Companies across every industry are moving beyond exploring AI’s potential into deploying it across core financial processes.

By Qvalia  |  Updated June 2026  |  18 min read

We are in the era of generative AI, powered by transformer-based neural networks and attention mechanisms that allow AI systems to process vast amounts of data, identify complex patterns, and carry out specialized tasks with high accuracy. Today’s frontier models produce text, code, images, audio, and video — and reason through multi-step problems in ways that were out of reach just a few years ago.

The next significant frontier — already well underway — is agentic AI: autonomous systems that don’t just respond to prompts but plan, take action, and execute complex workflows with minimal human intervention. Near-term competitive advantage comes from mastering what’s available now.

What remains constant through each AI evolution: those with clean, structured, and accessible data will extract value first. Data management is not the boring prerequisite to AI — it is the foundation on which AI advantage is built.

AI algorithms are only as useful as the data they’re trained on. Organizations with readily available, high-quality data are positioned to adopt the latest AI tools — and to benefit from future advances as they emerge. Data is becoming one of businesses’ most valuable assets, and data management is how you ensure that value is fully realized.

In this guide, we explore why future-proofed data management is the foundation for benefiting from current and future AI systems. Getting excited about AI is easy; what matters is implementing the right processes now so you’re ready to put those systems to work. In short: getting AI-ready.

How did we get from the early days of computing to an era where AI can handle a wide range of tasks in finance and beyond? While it can be tempting to view AI as a recent innovation, we’re seeing the evolution of decades of research and development.

The field of AI study began in earnest after World War II. Scientists and visionaries such as Alan Turing and John McCarthy saw the potential of using machines to automate tasks, analyze data, and make informed predictions. Early interest in AI drove significant advances in computing, and while AI proved more challenging than initially anticipated, incremental progress steadily revealed what was possible.

Every era of AI is marked by an evolution in how machines learn and process data. In finance, that evolution has focused on handling structured data to train algorithms for specific tasks — a natural fit, since financial processes continuously produce exactly this type of data.

AI models and techniques increasingly impact the financial sector, offering sophisticated tools for analyzing transactional data, master data, accounting records, and much more. Here are the key technologies applied in finance today.

Machine learning (ML) models. Supervised learning models like regression analysis predict future trends based on past data. Unsupervised learning models like clustering segment data into meaningful groups for customer analysis and risk profiling.

Deep learning models. Neural networks — including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) — are powerful for complex pattern recognition, making them suitable for fraud detection, customer behavior analysis, and predictive analytics.

Large language models and reasoning models. LLMs process and generate human-like text, enabling financial document analysis, automated advisory, and contract review. Reasoning models — trained for multi-step logical thinking — are suited to audit evidence analysis, tax position assessment, and complex scenario modeling where accuracy matters more than speed.

Agentic AI systems. AI systems designed to autonomously execute sequences of actions, use tools, and interact with external systems. In financial operations, agentic systems can manage end-to-end workflows — from invoice receipt through exception handling to payment — with human oversight at defined checkpoints.

Natural language processing (NLP). Used for analyzing financial documents, reports, and communications to extract insights, perform sentiment analysis, or automate customer service interactions.

Decision trees and random forests. Useful for classification and regression tasks such as credit scoring, risk assessment, and customer segmentation.

Time series analysis models. Models like ARIMA forecast financial metrics and analyze temporal patterns in financial markets and cash flows.

Reinforcement learning. Applied in algorithmic trading to develop strategies that maximize returns based on historical and real-time data.

Anomaly detection models. Identify outliers or unusual patterns in financial transactions that could indicate fraud or data errors.

Graph-based models. Effective in detecting complex relationships and networks — for example, uncovering fraudulent activity through analysis of transaction networks.

The choice of model depends on the specific financial application, data availability, and desired accuracy. Real-world financial AI systems typically combine multiple approaches.

Financial services businesses are actively exploring how AI can add value to their operations. But focusing on the latest algorithm will fall short if the data it works with is low quality, unstructured, or inconsistent. Algorithms are empty shells — they require quality data to become useful.

The focus shouldn’t be only on technology but on how to prepare data and make it accessible for current and future applications — and how to make it work in collaboration with business partners. Data captured and processed correctly today can serve almost any future need.

Normalized data involves organizing and standardizing both structured data (spreadsheets, databases) and unstructured data (emails, PDFs) into a consistent format that enhances usability and analysis. Structured data is necessary for current and near-term AI algorithms. Transforming unstructured data into structured formats remains a priority for any organization that wants to benefit from AI tools.

Examples of unstructured data in finance:

Examples of structured data in finance:

Developing and maintaining a data refinement process — extracting valuable data from unstructured formats and converting it to structured data — is vital for any AI-ready organization.

Even the best AI algorithm is nothing more than an empty shell without quality data behind it. Processes continuously generate data, and current and future AI algorithms will learn from this data to carry out their tasks accurately and efficiently.

It is not just any data that matters: the quality, quantity, and diversity of data determine the effectiveness of AI in enhancing operations. Comprehensive data management practices emphasize ensuring that data is exchanged between business partners as electronic business messages. When needed, cleaning or converting unstructured data to structured formats is essential. Breaking down siloed data is equally crucial — you cannot afford to lose quality or continuity when moving data between systems and processes.

Data must be viewed as a strategic asset — as valuable as human resources or physical equipment. Companies that treat data this way create a compounding competitive advantage as AI continues to evolve.

The strategic value isn’t only in training new algorithms; it’s also in enabling continuous training for increasingly accurate models. Consider how Google applied long-term data thinking from its earliest days, storing search and map data that wasn’t immediately valuable because they knew it would be later. Your current data management strategy should embody this same perspective: today’s data serves tomorrow’s technological advances.

Your business data is valuable internally — but also externally, to your business partners. The data exchanged between business partners is crucial for maintaining streams of structured, high-quality data. We are seeing the emergence of ecosystems focused on sharing and utilizing data between businesses and governments: the infrastructure for digital commerce.

Traditionally, businesses used VAN (Value Added Network) services to send and receive business messages such as e-invoices and e-orders using EDI standards. New technologies — built for standardization and broad connectivity — have significantly simplified access for companies of all sizes.

Peppol (Pan European Public Procurement Online) is an initiative aimed at standardizing digital communication between companies and governments. It is a significant opportunity for businesses to begin gathering structured data for AI algorithms — and, increasingly, a regulatory requirement.

Peppol is an example of a business network: an infrastructure for structured data and transaction-related processes. Other networks and solutions, like point-to-point Electronic Data Interchange (EDI) and Value Added Networks (VAN), take a similar approach to securely sharing data between businesses and governments.

Adopting Peppol for invoicing and document sharing grants access to a rich source of structured data. Data generated from Peppol-related processes shares the same formatting and structure across businesses and governments — helping create a robust data chain that drives AI algorithms now and in the future.

External demands for structured data from governments, suppliers, and customers are already a driving force behind data management investment. Government mandates are increasing and won’t slow down. Regions and enterprises are also requiring data classification under frameworks such as Continuous Transaction Controls (CTC). Exchanging data in established standards such as Peppol positions your company as a valuable, trusted link in the emerging data chain.

The EU Artificial Intelligence Act — in force since August 2024 and progressively applicable through 2025–2026 — is the world’s first comprehensive AI regulation. It has direct implications for financial services companies deploying AI, and any AI readiness strategy must account for it.

The Act classifies AI applications into risk tiers. Several applications common in financial services fall under the high-risk category, including:

What high-risk classification means in practice: AI systems in these categories must undergo conformity assessments, maintain detailed technical documentation, implement human oversight mechanisms, register in the EU AI database, and meet transparency requirements before deployment.

This is not a barrier to AI adoption — it is a compliance framework that rewards organizations with mature data governance. Companies that have already invested in structured data, audit trails, and clear data lineage will find EU AI Act compliance significantly easier than those starting from scratch.

A data-first perspective to building a resilient business is crucial to future-proofing data management. Today’s data can serve tomorrow’s technological advances — but only if you capture and structure it correctly from the start.

Real-time data continuously fed to AI algorithms enables up-to-date insights and predictions. This requires a shift from batch-oriented to continuous processes to support a feedback loop for AI algorithms. Real-time processes allow the business to manage and analyze transactions as they occur, enabling risk mitigation, predictions, and ad hoc changes.

Consider a punchout session as an example of how digitization and standardized data transform a process:

Data standardization is a set of processes and tools that enables your teams to capture and process data consistently across the organization — because the same conventions for labeling and formatting are followed throughout. Standards also inform metadata — the data about your data — describing where it came from, when it was modified, and how it relates to other records. This metadata becomes essential when training AI models or auditing AI decisions.

Peppol BIS — the format standard used within the Peppol network — is an excellent example of data standardization at scale. The United Nations Standard Products and Services Code (UNSPSC) offers a widespread taxonomy for standardizing products and services data, providing a framework for categorization that enhances operational efficiency and enables cross-organization analysis.

A growing problem facing businesses is data sprawl — data scattered and siloed across private and public clouds, SaaS applications, and edge devices, often with different formatting, storage, and access controls. Data sprawl significantly hinders any AI use case, even simple analytics.

Solving this requires comprehensive data governance — an overarching process of managing data accuracy, availability, and security across the organization. A data governance program dictates how data should be managed throughout its lifecycle: capture, storage, usage, and disposal. Strong governance also establishes the audit trails and documentation required for EU AI Act compliance.

Siloed processes create siloed data, and both hinder effective AI deployment. Integrating different IT systems and SaaS tools prevents data sprawl and gives AI tools and employees visibility across the entire data estate. Cross-departmental collaboration increases both the quantity and quality of business data.

But collaboration isn’t only internal: partners, customers, and business networks like Peppol create an entirely new way for B2B transactions, communication, and document exchange — and the structured data that emerges from these transactions prepares your company to benefit quickly from new AI developments.

Initiating an AI project in finance requires a cross-functional team to ensure the project aligns with business goals, is technically feasible, and addresses legal and compliance requirements — including the EU AI Act. Key roles typically include:

Order-to-cash (O2C) and purchase-to-pay (P2P) are among the most suitable processes for AI implementation. They involve multiple steps, cross-departmental collaboration, and often rely on manual tasks that hinder efficiency. While automation has already reduced some repetitive work, AI — and increasingly agentic AI — holds immense potential to transform these workflows further.

Dynamic credit assessment. AI combines data from a range of sources — market trends, payment history, transaction patterns — to conduct dynamic credit assessments. Self-learning capabilities allow these assessments to become more accurate over time, helping companies reduce credit risks associated with incoming orders.

AI accounting. AI-driven accounting leverages vast datasets — transaction records, regulatory updates, spending patterns — to enhance accuracy and streamline financial processes. By automating error-prone tasks such as reconciliations, invoice processing, and tax calculations, AI reduces manual oversight while continuously improving through machine learning.

Decision support and analytics. AI-driven data categorization transforms raw financial data into actionable insights by intelligently classifying and organizing it from diverse sources such as invoices, transactions, and account entries. By recognizing patterns and correlations, AI enables deeper analytics and surfaces trends that would otherwise be missed.

Learning workflows. Unlike rule-based automation, AI continuously learns from new data, recognizing patterns and improving responses over time. Agentic AI systems can now manage exception-heavy workflows — supplier disputes, approval routing, customer credit queries — autonomously and with increasing accuracy.

Continuous cash flow forecasting. AI’s ability to integrate a wide range of data points — from current interest rates and currency fluctuations to invoice aging and customer payment behavior — enables continuously updated cash flow forecasts rather than periodic snapshots tied primarily to sales reports.

Fraud detection and prevention. Fraud in the O2C process can be extremely costly: goods may ship before a fraudulent transaction is identified. AI continuously analyzes internal systems and external fraud data to identify anomalous patterns and flag transactions for review before fulfillment.

Behavioral credit management. AI can analyze communication patterns and sentiment to gain deeper insights into customer payment behavior, informing credit approval decisions and payment agreements based on established criteria.

By 2026, the AI landscape has advanced beyond what many analysts predicted just a few years ago. Several frontier models now demonstrate broad reasoning capabilities across domains, prompting leading AI labs to claim AGI milestones — though definitions remain contested and the field continues to debate what “general intelligence” truly means. The practical debate has shifted from whether AGI will arrive to what comes after it, and how to align increasingly powerful systems with business objectives.

For financial operations, the most transformative development is not a philosophical milestone but a practical one: agentic AI. AI systems that autonomously execute multi-step workflows — processing invoices end-to-end, managing supplier exceptions, running cash flow scenarios — are being deployed at scale in leading finance functions today. This is where AI moves from productivity tool to operational infrastructure.

Multimodal capability — processing and generating text, images, data, audio, and video within a single model — is now a baseline feature of all major frontier AI systems. For finance teams, this means a single AI workflow can process a scanned contract, extract key terms, cross-reference against a database, and flag discrepancies — tasks that previously required multiple specialized systems. The question is no longer whether your AI can handle multiple data types but whether your data infrastructure can supply them cleanly.

A distinct class of AI models trained for multi-step logical reasoning is now widely deployed and particularly relevant for finance. These models work through problems systematically before answering, making them well-suited to audit evidence analysis, tax position assessment, complex contract review, and scenario-based financial modeling — tasks where a confident but wrong answer is worse than a slower, correct one.

Through each shift in AI capability, one fact holds: the single most important predictor of how quickly your organization will benefit from new AI systems is the quality and accessibility of your data. Future-proofed data management — structured, standardized, governed, and exchanged through networks like Peppol — ensures your company is positioned to feed high-quality data into whatever AI system comes next.