Forensic accounting has long been the discipline where numbers meet narratives: Uncovering the truth behind financial data, identifying fraud and supporting legal and regulatory proceedings. However, as we progress through 2025, that world looks very different.
Artificial intelligence, automation and advanced analytics are transforming how investigations are conducted and reshaping what it really means to be a forensic accountant. (At the end of this article, you can find a comprehensive list of the most commonly-used AI tools in forensic accounting).
AI is your colleague, not your competitor
There’s no escaping the advent of artificial intelligence. The global market for AI-powered forensic accounting solutions is expected to reach $10 billion by 2028.
AI tools can now process millions of transactions in seconds, detect anomalies across vast datasets and identify patterns that would have taken human teams weeks to uncover. According to a recent survey by Deloitte, AI can help forensic accountants detect fraud up to 70% faster than traditional methods.
However, while technology has automated parts of the data gathering and pattern recognition process, it hasn’t replaced the human judgment that defines forensic work.
The forensic accountants who thrive in this new era are those who know how to interpret AI outputs and not just operate the tools. They ask the right questions, challenge assumptions and turn data signals into credible, defensible insights.
Technology does the heavy lifting, but people still very much do the thinking.
Shifting skills: from accounting technician to ‘investigative strategist’
Where forensic accountants were once prized primarily for technical accuracy, today’s top performers are valued for strategic interpretation and ethical reasoning.
Emerging skill priorities include:
- Data analytics and visualization: translating complex datasets into clear evidence.
- Digital forensics fluency: understanding how to collaborate with cyber and eDiscovery teams.
- AI literacy: being able to question and validate algorithmic results.
- Narrative building: crafting persuasive reports and testimony that hold up under scrutiny.
- Ethical awareness: knowing when AI-driven conclusions need human oversight or context.
In short, the modern forensic accountant isn’t just an investigator. They are a trusted advisor guiding legal teams, regulators and boards through increasingly complex cases.
Hiring market demand: being human matters more
Recruitment data across the forensics, investigations and disputes sector tells a clear story: demand is rising for professionals who blend technical capability with investigative intuition.
Organisations aren’t just hiring for software proficiency. They are looking for people who can bridge the gap between data and decision-making.
For example, we’re seeing a spike in demand for:
- Forensic analytics specialists who can interpret machine-learning results in AML and fraud reviews.
- Hybrid investigators who combine accounting, technology, and legal awareness.
- Ethical leaders capable of managing AI-enabled investigations responsibly.
The ethical dimension: AI governance in forensics
As AI tools become central to investigations, forensic accountants are increasingly responsible for ensuring that these systems are used ethically and transparently.
That means asking critical questions like:
- Is the algorithm trained on unbiased data?
- Are we transparent with clients and regulators about how conclusions were reached?
- Could automated analysis lead to false positives or reputational harm?
In this sense, the forensic accountant’s role has expanded from ‘fact-finder’ to ‘ethical guardian’: a crucial evolution in an era when trust and accountability are under scrutiny.
Looking ahead: Investigators of the future
Over the course of the next five years, forensic teams will look more multidisciplinary than ever before. Accountants will work alongside data scientists, cybersecurity specialists and legal technologists; forming agile teams that can respond to complex, cross-border investigations.
For employers, the challenge will be attracting professionals who can bridge those worlds: people who are curious, adaptable and comfortable working at the intersection of finance, technology, and ethics.
For candidates, the opportunity is huge: those who can upskill in AI literacy, digital fluency and critical thinking will define the next generation of forensic leadership.
If you are looking to hire in forensic accounting or if you are looking for your next career move in forensics, investigations and disputes, please get in touch with Adam Nelson for a friendly discussion.
Appendix: AI tools commonly used in Forensic Accounting
1. Data Extraction and Preparation
- Relativity: Primarily an e-discovery platform, it's heavily used to process and manage large volumes of unstructured data (emails, documents, chat logs) for investigations.
- Alteryx: A powerful data blending and preparation tool. It allows forensic accountants to pull data from various sources (ERPs, databases, spreadsheets), clean it, and prepare it for analysis without extensive coding.
- Microsoft Power Query (within Excel/Power BI): A widely accessible tool for data transformation and cleaning, making it easier to standardize and prepare financial data for investigation.
2. Data Analysis and Anomaly Detection
- ACL (now Galvanize) / IDEA: These are the classic, industry-standard data analytics tools. They are scriptable and excel at testing 100% of a population for:
- Duplicate payments
- Transactions just below approval limits ("round-dollar" or "below-threshold" testing)
- Invalid vendor addresses or bank accounts
- Benford's Law analysis
- Tableau / Microsoft Power BI: These are powerful data visualization tools. Forensic accountants use them to create interactive dashboards that can:
- Spot temporal patterns (e.g., spikes in expenses on weekends).
- Identify geographic anomalies (e.g., purchases from a high-risk jurisdiction).
- Visualize relationships between entities and individuals using link charts.
- Specialized AI Platforms:
- MindBridge Ai Auditor: Uses machine learning to analyze entire general ledgers, automatically risk-scoring transactions and highlighting anomalies for further investigation.
- CaseWare IDEA's SmartAnalyzer: Incorporates AI and machine learning to automate the detection of outliers, duplicates, and potential fraud indicators.
Network and Relationship Mapping
- i2 IBM iBase & Analyst's Notebook: The gold standard for visual investigative analysis. Used to create link charts that map relationships between entities, phone numbers, addresses, and financial transactions, revealing complex fraud schemes.
- Maltego: A powerful open-source intelligence (OSINT) and link analysis tool for gathering information from public sources and visualizing relational data.
- Palantir Foundry: Used for large-scale, complex investigations (often by government agencies and large financial institutions) to integrate and analyze massive datasets, uncovering deep, non-obvious relationships.
Forensic Technology & E-Discovery Platforms
- Nuix: A powerful engine for processing, searching, and analyzing unstructured data. It can rapidly index and search through terabytes of emails, documents, and other files for keywords, patterns, and specific metadata.
- Relativity (again): Its robust review and analytics features, like Technology Assisted Review (TAR), use machine learning to categorize and prioritize documents, significantly speeding up the review process in litigation or investigations.
Process-Specific Automation
- Robotic Process Automation (RPA): Tools like UiPath and Automation Anywhere can be used to automate repetitive forensic tasks, such as:
- Logging into multiple systems to extract data on a scheduled basis.
- Reconciling bank statements or transaction logs.
- Populating standard investigation reports with data from analytics tools.
