As a fully qualified chartered accountant with over 30 years’ experience and a remote practice serving companies across Europe, I’ve seen how fraudsters shift tactics as quickly as technology evolves. This article pulls together practical, field-tested observations about the role technology now plays in detecting — and deterring — fraudulent activity, and how finance teams can adopt tools sensibly without losing professional scepticism. My aim is to provide clear, actionable perspective: what works, what to watch for, and how to marry automated systems with sound accounting practice.
Practical Tech Tools for Modern Fraud Detection
Technology for fraud detection now sits in several complementary layers: data ingestion and cleansing (ETL), automated extraction (OCR and document parsing), rule-based engines, machine learning anomaly detection, robotic process automation (RPA) for repetitive checks, and business intelligence/visualisation for investigator-led analysis. Each layer serves a different purpose — OCR and RPA reduce manual error and expose exceptions faster; rules and ML highlight patterns humans cannot see at scale; dashboards make those signals actionable for management and auditors. In my practice I choose combinations of these layers rather than a single silver-bullet product.
Concrete examples help illustrate the point. I once identified a supplier-payment scheme where subtle variations in invoice line descriptions hid inflated charges; a text-similarity routine combined with duplicate-payments rules flagged the set within hours, not weeks. In another client engagement, combining OCR with a simple business rule (invoice amount > approval threshold but missing approval ID) reduced missed exceptions by 70% in the first month. Visual dashboards then let us prioritise investigations by risk score rather than by volume, so senior staff focused on the highest-probability cases first.
Adoption is where many organisations falter. I advise starting with small, measurable proofs of concept that integrate with your ERP and maintain clear audit trails. Data quality is non-negotiable — garbage in, garbage out — so invest in cleansing and mapping before applying machine learning. Also be mindful of cross-border data protection (GDPR) when working with European entities: anonymise or pseudonymise where possible, and ensure vendors can document their compliance. Finally, never accept model output uncritically; automated flags should always be paired with a defined human review process.
A Chartered Accountant’s Take on Automated Controls
From my vantage point, automated controls are powerful but subordinate to professional judgement. Controls such as system-enforced segregation of duties, threshold-based approvals, and automated vendor screening materially reduce opportunity for fraud, but they can also create a false sense of security if exception workflows and escalation paths are weak. An effective control framework blends preventative automation with detective checks and a culture that encourages reporting and challenge, especially in remote or lean finance teams common across Europe.
Practically, I follow a three-step approach: assess risks specific to the client, design automated controls to address the highest-risk processes, and monitor/iterate. For example, if a company has many small vendors in multiple jurisdictions, I prioritise vendor master data controls (unique keys, verification rules), automated duplication checks, and mandatory attachment of supporting documentation via OCR. I also configure exception workflows so that any override requires a documented rationale and a secondary approver — the human step that prevents many automated-control workarounds from becoming routine.
Governance and explainability are critical when automating. Ensure there is an auditable trail for every automated decision, and that model parameters or rule-sets are version-controlled and reviewed periodically. Validate machine-learning models against known cases and maintain a simple scoreboard of false positives/negatives so the toolset improves over time. Finally, train staff: automation changes roles more than it removes them. Remote work across jurisdictions increases reliance on technology, so invest in clear policies, regular validation, and a point of contact for escalations — I make time each month to review control exceptions with clients and recommend the same cadence for my peers.
If you have concerns about financial integrity within your business or want a pragmatic review of how technology could strengthen your fraud controls, I offer confidential consultations tailored to companies across Europe. I combine deep accounting experience with hands-on knowledge of modern detection tools — contact me to arrange a focused risk assessment and an initial proof of concept that respects both compliance and your commercial constraints.