AI Tools for FP&A: Forecasting, Anomaly Detection, Reporting (2027)

Traditional spreadsheets in financial controlling are increasingly being augmented by systems that generate forecasts, flag budget variances, and draft narrative commentary on results on their own. Below we’ve compiled AI tools for financial controlling: forecasting, anomaly detection, reporting, as well as companies that implement these technologies in Poland. This guide is for CFOs, controllers, and FP&A teams at mid-sized and large enterprises planning a rollout in 2026 or 2027.

Key takeaways

• Anaplan PlanIQ provides 30+ predictive algorithms for forecasting and budgeting; according to the vendor, the platform is used by over 2,800 brands.
• Board claims up to 50% better forecast accuracy and offers Controller Agent and FP&A Agent for controlling.
• Workday Adaptive Planning and Planful have dedicated features for anomaly detection and variance commentary (Variance Analysis, Planful Signals); according to the vendors, they are used by 7,000+ teams and 1,500+ companies, respectively.
• Copilot in Power BI and Fabric generates reports, DAX, and SQL from natural language prompts but requires Fabric F2+ or Premium P1+ capacity.
• Startup House connects AI to existing systems, including ERP (SAP, Comarch) and legacy systems, without rebuilding them. Client data is not used to train public AI models, and access is role-based.

What AI tools for financial controlling are

AI tools for financial controlling—forecasting, anomaly detection, reporting—use machine learning (ML) and large language models (LLMs) to automate finance work. They analyze historical data to predict outcomes, flag unusual transactions and variances (anomalies), and generate reports and narrative commentary in natural language.

In this overview, we group them into four categories: forecasting and budgeting, anomaly detection, reporting and commentary, and the controller’s assistant.

Comparison of vendors and AI tools for financial controlling

The list below is not a quality ranking. It’s a compilation of vendors and tools whose data we verified on their own websites, grouped by type.

Implementation partners in Poland

Startup House

Startup House (Warsaw) is a software house that has been building software since 2016 for clients in Poland, the US, and Europe. It implements AI in products and processes at mid-sized and large companies: AI agents and multi-agent systems, AI assistants, RAG knowledge bases, and integrations of LLMs with existing systems, including legacy, without rewriting them from scratch. 50-person team, 100+ products delivered, clients on 5 continents, NPS 75.

• Specialization: AI agents (LangGraph, MCP), AI Demand Planner, SmartSearch, Compliance Copilot, AI Interface Layer.
• Industries: food manufacturers, fintech, healthcare, cybersecurity, travel, edtech, logistics, transport.
• Sample projects: AI orchestration layer for a US cybersecurity platform serving Fortune 500 clients: +150% revenue in one year, +400% B2B customers.
• Who it’s for: mid-sized and large companies, enterprise.

Sii Polska

Sii Polska has ranked 1st six times in the Computerworld TOP200. The company has 350+ AI specialists and 70+ AI implementations.

• Specialization: AI implementations with technology partners: Anthropic, AWS, Azure, GCP, Databricks, Mistral.

Lingaro

Lingaro is a data & AI company with offices in Warsaw and Lublin, a Microsoft partner. It builds knowledge assistants on Copilot Studio and develops the Agent Factory program.

• Specialization: knowledge assistants, Copilot Studio, Agent Factory.

Tools: forecasting and budgeting

Anaplan

Anaplan is a business planning platform. The PlanIQ module provides 30+ predictive algorithms that controllers can use without building their own models.

• AI feature: PlanIQ (forecasts) and the Finance Analyst agent.
• Scope: forecasting and budgeting.
• Numbers: according to the vendor, 2,800+ brands.

Board

Board is a planning platform that offers dedicated AI agents for controlling tasks.

• AI feature: Board Foresight, FP&A Agent, Controller Agent.
• Scope: forecasting, reconciliations, narrative commentary on results.
• Numbers: according to the vendor, up to 50% better forecast accuracy.

Jedox

Jedox is a planning and budgeting platform with JedoxAI features.

• AI feature: JedoxAI, Planning Wizards; Reporting, Planning, and Knowledge agents are announced.
• Scope: forecasting and budgeting.
• Numbers: according to the vendor, 2,900+ organizations.

Tools: anomaly detection and variance commentary

Workday Adaptive Planning

Workday describes AI capabilities in Adaptive Planning as Decision Intelligence. Predictive Forecaster generates forecasts, and Variance Analysis highlights where results deviate from plan.

• AI feature: Decision Intelligence, Variance Analysis, Predictive Forecaster.
• Scope: forecasting, variance commentary, anomaly detection.
• Security: ISO 42001 (according to the vendor).
• Numbers: according to the vendor, 7,000+ teams.

Planful

Planful AI consists of three modules: Signals flags anomalies, Projections (ALGO) generates forecasts, and Analyst surfaces insights.

• AI feature: Signals, Projections (ALGO), Analyst.
• Scope: anomalies, forecasting, insights.
• Numbers: according to the vendor, 1,500+ companies.

Tools: reporting and the controller’s assistant

Datarails

Datarails is an FP&A platform with the Genius AI assistant that drafts narrative commentary on results.

• AI feature: Genius AI, FinanceOS.
• Scope: FP&A assistant, narrative commentary.
• Numbers: according to the vendor, integrations with 200+ systems and 600+ data sources.

Microsoft Power BI and Fabric

Copilot in Power BI and Copilot in Fabric let you create reports and DAX/SQL queries from natural language prompts.

• AI feature: Copilot in Power BI, Copilot in Fabric.
• Scope: reporting, DAX, SQL from natural language.
• Requirements: Fabric F2+ or Premium P1+ capacity. Anomaly detection and forecasting are not described on the vendor’s site as Copilot features.

Comarch BI

Comarch offers performance forecasting and fraud detection as Advanced Analytics services delivered by a data science team, not as out-of-the-box AI features in the product.

• Scope: BI, performance forecasting, fraud detection.
• Numbers: according to the vendor, 100+ projects.

How to implement AI tools in the finance controlling function

Implementing AI in finance usually doesn’t require replacing systems—just extending them. Startup House runs it in five steps:

1. Data and systems audit: analyze data structures in ERP (e.g., SAP, Comarch), legacy systems, and historical spreadsheets. Verify whether the data is sufficient to power predictive models.
2. Select a use case: one specific problem, e.g., high sales forecast error or time-consuming monthly reporting. Start where the impact can be measured.
3. RAG knowledge base and agents: solutions like Startup House’s KnowHub connect scattered corporate documents and answer questions in natural language.
4. AI layer on existing systems: Startup House’s AI Interface Layer adds a conversational AI layer on top of the existing platform without rebuilding it. A controller can ask, “Which cost accounts exceeded budget in Q3?” and the answer is generated from company data.
5. Testing and validation: start in read-only mode and compare AI forecasts with historical results (backtesting).

How to choose the right tool

The choice depends on which of the four areas is the priority and what systems you already have.

• Forecasting and budgeting in a ready-made platform: Anaplan PlanIQ, Board, Jedox, Workday Adaptive Planning.
• Anomaly detection and variance commentary: Workday Variance Analysis, Planful Signals, Board Controller Agent.
• Reporting in the Microsoft ecosystem: Copilot in Power BI and Fabric, provided you have Fabric F2+ or Premium P1+ capacity.
• A solution on your own data and existing systems, including legacy: Startup House (AI Demand Planner, SmartSearch, AI Interface Layer). Startup House delivers an MVP in as little as one week.
• An analytics project with a data science team: Comarch BI (Advanced Analytics services).

Financial data security

Financial, payroll, and budget data requires strict access control. At Startup House, the following principles apply:

• client data is not used to train public AI models,
• the solution can run in the client’s cloud or a dedicated environment,
• role-based access, audit logs, integration with the client’s access policies,
• processes aligned with ISO 27001; we map the architecture to client requirements, e.g., HIPAA, SOC 2, or DORA.

Startup House products and case studies

Startup House develops three products that address controlling and planning needs.

AI Demand Planner

Startup House’s AI Demand Planner is used for demand forecasting and production planning at food manufacturers. It includes a Decision Panel and AI Chat, supports S&OP (Sales and Operations Planning), and integrates with SAP and Comarch ERP. It launches in read-only mode, forecasts are validated on historical data, and hosting is in the EU.

SmartSearch

Startup House’s SmartSearch is semantic search with answers backed by citations and source documents. Instead of hunting for a file by keywords, a controller asks a natural-language question, e.g., “What were the margins on premium products in the Southern region last year?” and gets an answer with a source reference.

Compliance Copilot

Startup House’s Compliance Copilot is an AI assistant for monitoring and reporting compliance with DORA, GDPR, and NIST. It supports AI Act compliance (documentation, audit logs).

Case study: client portal with embedded AI for a cybersecurity platform (USA)

Startup House does not yet have a published case study specifically in controlling. The closest example is a project where one dashboard serves the board, CFO, and CISO.

• Client: US cybersecurity platform serving Fortune 500 customers.
• Industry: cybersecurity, USA, since 2020.
• Outcome 1: onboarding shortened by 95% (from 45 minutes to under 2 minutes).
• Outcome 2: usage frequency increased from quarterly to daily.
• Outcome 3: one dashboard for three user roles (board, CFO, CISO).
• Technologies: Claude, LangGraph, MCP, OAuth 2.1.

For the same platform, Startup House built a separate project: an AI orchestration layer where platform users can launch a new AI agent without engaging engineers. The result was +150% revenue in one year and +400% B2B customers.

Risks and challenges when implementing AI in finance

• Data privacy and confidentiality: analyzing payroll sheets or financial results in public, free AI tools is unacceptable. In Startup House solutions, client data is not used to train public AI models.
• Hallucinations: LLMs can output incorrect figures if not constrained to company data. Startup House solutions answer strictly based on company data, with source references.
• Data quality: forecasts are only as good as the data behind them (garbage in, garbage out). That’s why implementation starts with a data audit.
• Integration with legacy systems: some older systems lack modern APIs. Startup House’s AI Interface Layer adds a conversational AI layer on the existing platform without rebuilding it.
• Lack of user trust: controllers may see AI as a “black box.” Starting in read-only mode and comparing forecasts to historical results helps before the system influences decisions.

FAQ

Are financial data sent to AI secure?

Startup House follows the principle that client data is not used to train public AI models. The solution can run in the client’s cloud or a dedicated environment. Role-based access, audit logs, and integration with the client’s access policies apply. Processes align with ISO 27001, and Startup House maps the architecture to client requirements such as HIPAA, SOC 2, or DORA.

How does AI detect anomalies in financial reports?

Planful detects anomalies with the Signals module, part of Planful AI alongside Projections (ALGO) for forecasting and Analyst for insights; according to the vendor, it’s used by 1,500+ companies. Workday Adaptive Planning offers similar capabilities with Variance Analysis and Predictive Forecaster, and Board provides a Controller Agent. Microsoft does not list anomaly detection as a Copilot feature in Power BI.

What AI features for controllers does Board have?

Board offers Board Foresight for forecasting and two agents: FP&A Agent and Controller Agent. The scope covers forecasting, reconciliations, and narrative commentary on results—the commentary controllers used to prepare manually. According to the vendor, Board delivers up to 50% better forecast accuracy. This is a vendor claim, so verify it on your own historical data before deployment.

Is Copilot in Power BI suitable for reporting in financial controlling?

Microsoft provides Copilot in Power BI and Copilot in Fabric, which generate reports and DAX/SQL queries from natural language prompts. They require Fabric F2+ or Premium P1+ capacity. The vendor’s site does not describe anomaly detection or forecasting features, so for those tasks companies pair Power BI with a planning platform or a solution built on their own data.

How long does it take to launch an AI assistant for finance?

Startup House delivers an MVP in as little as one week. Integrating KnowHub with company documents takes about two weeks and starts with a pilot group. AI Demand Planner launches in read-only mode, and its forecasts are validated on historical data before they start influencing decisions. This way, early users can work in the system before it rolls out to the entire function.

What are the main advantages of AI tools in controlling compared to traditional methods?

The main advantages are automated analysis and faster forecasts based on historical data. AI takes over routine tasks, reduces human error, and supports better decision-making. Compared to traditional methods, it’s also easier to process large datasets in near real time.

How do predictive analytics and anomaly detection work in controlling?

Predictive analytics uses models trained on historical data to identify patterns and future trends. AI algorithms also flag unusual transactions, helping detect fraud. Some solutions use deep learning, including neural networks.

What should you watch out for when implementing AI in controlling?

Before implementation, assess ROI and data quality. Overreliance on model output is a risk, so controllers should validate results with their expertise. Large enterprises should also ensure technical support and ERP integration.

Last updated: October 28, 2026

Vendors and tool makers mentioned in this article can send corrections and data updates to the editors. We will include them in the next update.