Machine Learning in Forensics: Opportunities, Risks and Future Directions
Every message sent, every online payment, every log-in, every GPS check-in leaves a trace. These traces are the raw material of the digital economy and of modern forensic investigations. In a recent IDC forecast (2022-2026)[1], the datasphere is expected to more than double from ~101 zettabytes (ZB) in 2022 to over 221 ZB by 2026. That is the equivalent of 44.2 trillion HD movies worth of information, stored, transmitted and analysed.
For investigators, the growing volume of data presents both complexity and potential opportunities. Traditional techniques such as manual review, sampling and spreadsheet-based analysis are not well-suited to handle data at this scale. In contrast, machine learning (ML) methodologies are designed to operate effectively in high-volume environments, enabling the processing of large sets of structured and unstructured data to identify patterns and insights that are not readily accessible through conventional means.
What machine learning brings to forensics
Machine learning may sound abstract, but its value in forensic work is highly practical. At its core, ML helps answer three key questions investigators face every day:
Classification. ML can sort transactions, contracts, or communications into categories (routine, suspicious, high-risk) far faster than a human team could.
Regression and anomaly detection. ML can learn patterns (such as how fuel prices correlate with logistics costs) and flag anomalies. A handful of shipments priced far above the norm may be simple mistakes or they may signal fraud or collusion.
Grouping. ML can organise data into meaningful groups. Investigators might find clusters of emails pointing to coordinated action, or clusters of loan accounts that share hidden characteristics.
Core algorithms in forensic machine learning systems
Different forensic challenges demand different algorithmic approaches, each with its strengths and weaknesses. Understanding how these tools operate in practice helps explain why adoption is accelerating across compliance, fraud detection, and investigative services.
Decision trees and random forests
Decision trees emulate human decision-making by posing a sequence of binary (“yes” or “no”) questions that guide the path to a conclusion, for instance, “is this transaction unusually large?” or “was it made with a high-risk counterparty?”
Forensic use cases include risk scoring of transactions and journal entries, predicting defaults or insolvency risks, and compliance checks against anti-bribery or AML frameworks.
Random forests ensemble of multiple decision trees and enhance reliability by averaging out the biases of individual trees. For forensic professionals, they offer a compelling trade-off: robust analytical power while remaining interpretable enough to defend in legal or regulatory settings.
Neural networks (RNNs and transformers): text and speech analysis
Unlike decision trees, neural networks do not follow a clear set of human-readable rules. Instead, they process data through multiple hidden “layers,” automatically detecting complex patterns.
Recurrent Neural Networks (RNNs): Designed for sequential data such as emails, chat logs or phone transcripts. RNNs can track the flow of a conversation identifying escalation in tone or shifts in context across dozens of exchanges.
Transformers (the architecture behind large language models (LLMs): These models excel at processing vast amounts of text in parallel. In forensic practice, they can sort through millions of documents to surface the most relevant evidence, translate and analyse multilingual communications and summarise long trails of correspondence into coherent narratives.
GANs and Autoencoders: detecting deepfakes
Generative Adversarial Networks (GANs) and Autoencoders are algorithms that can both create and detect synthetic media.
GANs pit two networks against each other: one creates fake content (like a forged document or synthetic video), the other tries to detect it.
Autoencoders compress and then reconstruct data. Differences between the input and reconstructed output can reveal tampering or anomalies invisible to the human eye.
Forensic use cases include detecting altered images in financial records or audit trails, identifying manipulated video evidence in legal disputes, and exposing synthetic voice scams or AI-generated identity fraud.
For instance, scammers in Hong Kong recently pulled off a US$25 million theft by using deepfake technology to impersonate a CFO on a video call (CNN, 2024). This underscores the growing need for algorithms that can detect even minor signs of media manipulation.[2]
Industry trends and growth trajectories
The global artificial intelligence in fraud detection market is projected to grow from an estimated US$15.6 billion in 2025 to US$119.9 billion by 2034, driven by a robust compound annual growth rate (CAGR) of approximately 25.4%. This surge is underpinned by increasing investments from sectors such as banking, insurance, telecommunications, and e‑commerce seeking real‑time prevention of payment, identity, and money laundering fraud with greater accuracy and compliance oversight.[3]
This momentum reflects not only technological progress but also rising pressure from regulators and stakeholders for companies to prevent fraud, demonstrate compliance, and ensure accountability in increasingly complex global operations.
Real-world impact
Machine learning in forensics is actively transforming industries across both the private and public sectors. What was once a niche tool for data scientists has become a frontline instrument for fraud prevention, compliance monitoring and investigative work:
Banking and finance: ML has improved fraud detection accuracy by 35-40%, significantly reducing false positives common in rule-based systems. For instance, Mastercard employs generative AI to enhance fraud detection and reduce false declines. Their Decision Intelligence Pro platform analyses over a trillion data points in real time, improving fraud detection rates by up to 300% and decreasing false positives by more than 85%. This AI-driven approach enables banks to approve billions of transactions annually with greater accuracy and efficiency.[4]
Insurance: AI tools are increasingly used to detect fraudulent claims by analysing patterns such as repeated repairs, altered images, and inconsistencies across multiple data sources. This approach not only enhances detection accuracy but also streamlines the claims process, enabling insurers to focus on complex cases and improve overall efficiency.
Corporate compliance: ML scans vast document sets (contracts, invoices) to identify risks such as duplicate payments or conflicts of interest, reducing review time from months to days and mitigating regulatory risk.
Public sector and law enforcement ML integrates data (phone, finance, geolocation) to map criminal networks rapidly. Europol and Interpol use AI platforms to cross-analyse diverse data sources, enabling comprehensive investigations previously impossible.[5]
Key strengths, risks and limitations
By the end of 2025, experts predict that 95% of online transactions will be monitored by AI-powered fraud detection systems.[6] For forensic consultants and investigators, the use of machine learning offers both advantages and challenges - richer datasets and faster alerts, but also the need to assess the reliability and transparency of the underlying algorithms.
ML introduces consistency by applying uniform logic across large datasets, reducing the interpretive variability often seen in human-led reviews. This helps minimise subjectivity, especially in large teams where the same data might otherwise yield inconsistent conclusions.
It also enhances the ability to detect connections by linking identifiers such as phone numbers, IP addresses, or bank accounts across disparate data sources. These linkages can uncover hidden relationships such as a shell company tied to both a suspicious supplier and an internal stakeholder that would likely remain invisible in traditional spreadsheet-based reviews.
Modern language models further support global investigations by processing documents in multiple languages without requiring full human translation. This reduces costs, accelerates review timelines, and lowers the risk of losing key context or nuance in translation.
Rather than replacing investigators, ML augments their work providing both a broad view of entire datasets and the ability to zoom in on anomalies. This enables teams to shift from reactive investigation to proactive risk identification, addressing issues before they escalate into serious disputes or liabilities.
However, the story is not one of unqualified success. Forensic applications of ML face important challenges:
Data security: Cloud-based AI raises confidentiality concerns. Sensitive corporate data must often remain on-premises, driving demand for secure, locally deployed ML solutions.
Lack of standards: There is no global standard for the admissibility of AI-assisted forensic analysis. Courts and regulators are only beginning to grapple with these issues.
Data scarcity and bias: Real investigations often involve incomplete or manipulated datasets. Training an ML model on skewed data risks producing unreliable conclusions.
Explainability: Neural networks, especially deep models, are notoriously opaque. In forensic settings, especially in court, investigators must be able to explain how conclusions were reached.
Reproducibility: ML models may generate slightly different outputs on the same data, challenging the consistency required for legal evidence.
Trends shaping the future
Over the next five years, the adoption of machine learning in forensic investigations will be shaped by several converging trends including greater domain specialisation, stronger privacy controls, growing regulatory pressure for transparency and a more structured integration with human expertise.
One key development is the rise of domain-specific AI models. While general-purpose systems like GPT or Gemini perform well on broad tasks, they lack the precision required in forensic work. Models trained specifically on financial records, legal documents, whistleblower reports or compliance case histories are better suited to identifying fraud, tracing fund flows or interpreting regulatory risks. These models reduce false positives and generate outputs that are more context-aware and defensible in legal or regulatory proceedings.
Privacy and data control are also driving a shift toward on-premises AI systems. Cloud-based models offer scalability, but they also raise concerns under data protection regimes such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or financial secrecy laws. On-premise AI allows sensitive data to remain on local infrastructure, while federated learning enables model training across distributed datasets without transferring raw data.
A third priority is explainability. In forensic work, accuracy alone is not enough since investigators must be able to show why an AI model flagged a given transaction, document or communication. This is particularly important in legal and regulatory settings where AI outputs must be auditable. Explainable AI (XAI) aims to solve this by making model decisions interpretable and transparent.
Synthetic data is another enabling technology. Forensic teams often deal with confidential client data that cannot be used for model training. Synthetic datasets - realistic, anonymised data generated by AI - allow teams to develop and test models without exposing sensitive information. Markets & Markets projects that the synthetic data market will grow to US$2.1 billion by 2028, driven in part by demand in fraud prevention and compliance use cases[7].
Finally, AI is not replacing human investigators, it is amplifying their reach. AI excels at scaling document review, clustering large volumes of data and surfacing anomalies. Human experts provide the judgment, contextual understanding, and legal framing needed to interpret and act on AI findings.
Together, these trends point to a more secure, explainable and domain-aware approach to machine learning in forensic consulting - one that supports both operational efficiency and legal defensibility.
[1] https://view.ceros.com/idg/numbers-crunchers/p/1
[2] https://edition.cnn.com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intl-hnk
[3] https://dimensionmarketresearch.com/report/artificial-intelligence-in-fraud-detection-market/
[4] https://www.mastercard.com/news/ap/en/perspectives/en/2025/from-fighting-fraud-to-fueling-personalization-ai-at-scale-is-redefining-how-commerce-works-online/
[5] https://www.europol.europa.eu/cms/sites/default/files/documents/AI-and-policing.pdf
[6] https://www.sciencedirect.com/science/article/pii/S2773207X24001386
[7] https://www.marketsandmarkets.com/PressReleases/synthetic-data-generation.asp