How Criminals Use AI to Commit Business Fraud
Deepfake video calls, cloned voices, AI-written BEC, and fabricated invoices: the offense, the Arup case, and the controls that still hold.
Independent research and analysis of how artificial intelligence is being used in business.
AI moves fast. Focus on what matters to business.
Efficiency Innovations covers new AI technology, business applications, AI agents and automation, enterprise systems, management implications, economic impact, and risk. Coverage examines each development from the perspective of practical business use.
A federal magistrate judge has treated a generative AI system making final responsiveness calls in document review as ordinary Technology Assisted Review, governed by the discovery rules that already exist. The order is narrow and binding on no one else. It may still be the most significant legal AI development of the year, because of what the court declined to do.
Deepfake video calls, cloned voices, AI-written BEC, and fabricated invoices: the offense, the Arup case, and the controls that still hold.
External deception and internal manipulation converge on the same function, and process-focused controls miss both.
The full AP stack in one section: automation economics, duplicate and vendor fraud detection, anomaly screening, and continuous controls.
Rules catch what someone anticipated. Pattern monitoring catches what the company has never done before, evaluated as every transaction moves.
Duration drives fraud losses, and population-scale monitoring is the highest-leverage control in the ACFE data. What AI catches and what it cannot.
A federal court has held consumer AI conversations are neither privileged nor work product, and hiring a lawyer afterward does not change that.
Using ChatGPT, Claude, and Gemini in a law practice without a large-firm platform, with a 30-day implementation model.
Pressure from competitors, vendors, and boards is evident but makes a poor basis for capital allocation. The questions that come before the purchase.
The three levels of business AI, and why the innovation occurs when AI changes the process rather than making an employee faster.
What agent technology can realistically do, where it remains immature, and which processes are candidates for evaluation.
Capabilities, plans, pricing structure, and the business functions where evaluation makes sense.
How the two leading enterprise AI offerings compare on capability, administration, security, and cost.
Where agent technology is producing results in payables, reconciliation, and the close, and where it isn't ready.
The product behind the branding, examined feature by feature for finance departments.
What AP automation covers, what it costs, and what an implementation actually involves.
Legal research, contract review, and drafting platforms, examined from the buyer side of the market.
Predictive maintenance, machine vision, scheduling, and knowledge assistants, assessed for practical plant value.
Integration paths for SAP, Oracle, Dynamics, and NetSuite, with the controls a business should require.
A technology company announces a new model, agent, product, or integration. Technology news explains what was announced. Efficiency Innovations examines what the announcement means for the businesses that might use it. Important developments are assessed according to their practical implications.
Payables, receivables, reconciliation, forecasting, reporting, fraud detection, and the financial close.
Process automation, document processing, scheduling, purchasing, and administrative work.
Machine vision, quality control, predictive maintenance, production planning, and industrial AI.
AI service agents, voice agents, support automation, ticket management, and knowledge systems.
CRM intelligence, lead qualification, proposal automation, forecasting, and customer analysis.
Onboarding, training, recruiting technology, employee support, and workforce planning.
Legal research, contract review and drafting, document analysis, e‑discovery, and AI use in law firms and corporate legal departments.
AI integration with SAP, Oracle, Dynamics, NetSuite, Sage, QuickBooks, and industry systems.
Inventory, demand planning, logistics, supplier analysis, and supply chain visibility.
End to end automation of business processes, with human oversight where it belongs.
Digital workers, supervised agents, and autonomous processes inside real business systems.
The first generation of business AI answered questions. The current transition is toward systems that participate in workflows: reading documents, updating records, initiating transactions, and completing multi step processes inside enterprise systems.
Coverage in this area distinguishes between what AI agents can realistically do today and what remains speculative or immature, with particular attention to human approval, system integration, and operational safeguards.
Strategy, competitive implications, investment priorities, and organizational readiness.
Financial impact, return on investment, cost structure, and finance department automation.
Process selection, productivity, implementation, and workforce effects.
Vendor evaluation, integration, security, data governance, and build versus buy decisions.
Practical adoption for small and mid sized companies without enterprise budgets.
Business use of AI raises practical questions about reliability, privacy, security, and oversight. Coverage addresses these issues directly, without either alarm or dismissal, so that adoption decisions rest on an accurate view of the risks.
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Efficiency Innovations is an independent publication examining how artificial intelligence and emerging technologies are being applied across modern businesses. Coverage includes AI products, business applications, AI agents, enterprise systems, management strategy, productivity, financial impact, implementation, risk, and governance.
The purpose of the publication is to provide practical analysis of what rapidly changing technologies mean for business. More about the publication and its editor is on the About page.