A&M’s Digital Twin service operationalizes corporate compliance programs and measurably demonstrates compliance effectiveness in line with regulators' expectations. Our end-to-end regulatory and compliance intelligence service gives chief investigators, legal and compliance professionals the ability to drive better business transparency with increased agility, in turn, driving better business performance, cost savings and integrity.
Anti-bribery and anti-corruption Digital Twin addresses the legal and compliance risks in companies' financial data bringing better transparency into:
- Sanctions and trade compliance
- Fraud, waste and abuse
- Third-party risks and improper payments
- Revenue recognition and customer/distributor risks
- Conflicts of interests and segregation of duties
- Employee travel and entertainment
Learn More about A&M's Digital Twin here.
Contact Vincent Walden and Jeremy Tilsner to Learn More
Australia's Results Decline in the Latest Corruption Perceptions Index
February 24, 2026
Australia’s drop in the latest Corruption Perceptions Index highlights growing integrity and governance risks amid a broader global decline. What does this mean for organizations, and how can leaders use CPI insights to strengthen risk management and compliance?
Deepfakes and the Shifting Burden of Scrutiny for Litigators
February 19, 2026
The increasing sophistication of AI has made it easy to manipulate evidence, and taking steps to triage evidence with
procedural diligence and technological support has become the litigator's burden.
Delay Analysis: A Mix of Science, Art, and Common Sense
February 17, 2026
Delay analysis supports the tribunal process by setting out the sequence of events and identifying the causes and extent of the delay. Director Moiz Khan explores how delay analysis, when applied as a mix of science, art and common sense, forms reliable expert evidence.
The Rise of AI Agents: The Next Big Thing in eDiscovery
February 16, 2026
Gary Foster, MD examines the rise of AI agents, the latest evolution of AI. Unlike traditional machine learning or generative AI models that perform single, isolated tasks - AI agents represent a paradigm shift: they can reason, plan, and act autonomously across workflows.
