Separating objects from value: A high-tech approach to fraud prevention

Learn how the 3P framework can help fraud examiners detect and prevent financial statement fraud.

As fraud examiners know all too well, financial statement fraud is one of the costliest and most complex forms of fraud. Indeed, as data from the Association of Certified Fraud Examiners’ (ACFE) Occupational Fraud 2024: A Report to the Nations shows, despite being the least common type of fraud (5% of cases), organizations lost a median $766,000 from it compared to asset misappropriation, which occurred in 89% of cases analyzed in the report, and caused a median loss of $120,000.

Fraud examiners also know well the complex nature of financial statement fraud and the intricate schemes that perpetrators conduct. According to The Anti-Fraud Collaboration’s analysis of 531 U.S. Securities and Exchange Commission (SEC) accounting and auditing enforcement releases from 2014 to 2019, the most common forms of financial statement fraud involve value manipulation, including improper revenue recognition (43%), reserves manipulation (24%–28%) and inventory misstatement (11%–12%).

In today’s digitally transformed business landscape, fraudsters can manipulate financial reports with increasingly sophisticated methods. Traditional detection methods — typically relying on retrospective analysis, sampling and manual audits — are often inadequate against these modern fraud schemes. By the time auditors detect inconsistencies, the damage has been done.

To effectively harness these technological advancements, fraud examiners need an approach that addresses the full spectrum of potential fraud schemes — the 3P Model.

However, emerging technologies, such as artificial intelligence (AI), object tracking and blockchain, are creating new paradigms for fraud prevention that address vulnerabilities before fraudsters can exploit them. We’re now able to use object tracking (like DHL and UPS use to track packages) for financial reporting and not just outdated double-entry bookkeeping (or machine readable XBRL data). These older methods use a mix of historical costs, fair value and other incompatible measurements.

To effectively harness these technological advancements, fraud examiners need an approach that addresses the full spectrum of potential fraud schemes — the 3P Model. This model separates objects from value in business reporting by organizing processes into three primary categories: products and services, people, and physical infrastructure. The separation of objects from value through the 3P Model creates multiple verification points that make fraud more difficult to execute but much easier to detect. By integrating AI, object tracking and blockchain technologies into this framework, organizations can build financial reporting systems resistant to manipulation.

The 3P Model

As shown in the chart below, the 3P Model creates a structured flow from object inputs (supply chain, resources, governance and electronic invoices) through core processing of the three Ps to object outputs (customers, markets and value creation). This separation makes manipulation more difficult because fraudsters must falsify data across multiple independent verification systems.

This model integrates cutting-edge technologies with evolving international reporting standards — including International Financial Reporting Standards (IFRS) 15 Revenue from Contracts with Customers, IFRS 18 Presentation and Disclosure in Financial Statements, and International Sustainability Standards Board (ISSB) — guidelines to create more transparent, accurate financial statements. (See “Glossary of terms” for a detailed explanation of different terms used in this column.)

Products and services

The first pillar of the 3P Model focuses on tracking products and services as they move through the supply chain.

How it works:

• Radio frequency identification (RFID) tags, barcodes and Internet of Things (IoT) devices monitor products from origin to delivery.
• Digital twins (virtual replicas) of physical products enable real-time verification.
• AI systems flag discrepancies between reported and actual inventory.

Red flags addressed:

• Unexplained inventory shrinkage.
• Premature revenue recognition.
• Channel stuffing.
• Fictitious sales.
• Bill and hold schemes.
• Phantom inventory.

Fraud prevention impact: Organizations implementing RFID-based supply chain tracking have experienced an overall reduction in inventory shrinkage and an improvement in financial reporting accuracy related to inventory valuation. More importantly for fraud examiners, these systems create real-time alerts when physical reality doesn’t match financial reporting — a classic indicator of potential fraud.

People

The second pillar focuses on the human side of business operations by monitoring employee data and contributions.

How it works:

• Advanced analytics monitor patterns in employee activities.
• AI systems identify anomalies in transaction approvals or system access.
• Pattern recognition algorithms detect unusual timing or approval sequences.

Red flags addressed:

• Unusual access patterns to financial systems.
• Excessive approvals outside normal business hours.
• Unusual changes to vendor master files.
• Ghost employees.
• Inflated expense reimbursements.
• Segregation of duties violations.

Fraud prevention impact: These systems excel at detecting expense report fraud, payroll schemes and unauthorized system access often before the fraud can fully materialize. By establishing baseline behavior patterns, AI algorithms can identify anomalies invisible to traditional audit procedures, such as approvals being processed while an employee is supposedly on vacation or a sudden change in expense report patterns.

Investigative considerations: When patterns trigger alerts, fraud examiners should carefully document the digital evidence. Modern AI systems maintain comprehensive audit logs that can be invaluable during investigations. However, you must consider privacy regulations when implementing monitoring systems.

Physical infrastructure

The third pillar addresses documenting and tracking the physical assets that form the backbone of many businesses.

How it works:

• IoT sensors provide continuous monitoring of physical assets.
• Drones and geospatial technologies create accurate digital representations.
• Geographic Information Systems (GIS) provide spatial context for asset monitoring.

Red flags addressed:

• Asset misappropriation.
• Fictitious or overvalued assets.
• Improper capitalization of expenses.
• Inflated insurance claims.
• Unreported asset impairment.
• Ghost assets remaining on the books after disposal.

Fraud prevention impact: Comprehensive monitoring creates an audit trail of asset existence, condition and usage that could make many traditional asset-based fraud schemes nearly impossible to execute. When a financial report claims ownership of 20 delivery trucks but IoT sensors and geographic data show only 18 in operation, investigators have a starting point for inquiry.

 

Cutting-edge technologies for fraud detection

The 3P Model leverages several key technologies, including advanced neural networks (computational models that consist of interconnected nodes organized in layers that process and transmit information), blockchain and IoT devices. Neural networks are often used in surveillance systems, sports analytics following players or balls, autonomous vehicles tracking pedestrians and other cars, and augmented reality overlaying digital content on moving objects. They excel at object tracking because they can handle complex scenarios, such as changing lighting, partial occlusion and objects that change appearance.

Advanced neural networks such as YOLOv8 provide real-time object detection with remarkable accuracy. These systems can verify physical inventory against reported values, creating an independent verification mechanism that reduces opportunities for misrepresentation. In testing, YOLOv8’s neural network-based object detection achieved 94.7% accuracy in identifying discrepancies between reported inventory and physical assets, significantly outperforming traditional audit procedures.

Blockchain technology ensures secure transaction recording through immutable ledgers that prevent retroactive manipulation of financial data. Smart contracts can automate compliance checks and verification processes, reducing human intervention points where fraud often occurs.

IoT devices (sensors, actuators, appliances or other hardware that connect wirelessly to a network and can transmit data) facilitate continuous monitoring across physical infrastructure and supply chains. These sensors provide real-time verification of asset status, creating an audit trail that supports reported financial data.

Implementing the 3P Model

To implement the 3P Model, you’ll use enterprise resource planning (ERP) systems, some of which have extensive object-tracking capabilities.

Physical objects:

• Materials and products through inventory management.
• Equipment and assets.
• Serial numbers and batch numbers for individual item tracking.
• Handling units and logistics units in warehouse management.

Business objects:

• Documents (purchase orders, sales orders, invoices).
• Financial transactions and accounting documents.
• Workflow items and approval processes.
• Master data changes and modifications.

System objects:

• User activities and access logs.
• Configuration changes.
• Data modifications with change documents.
• System transactions and database changes.

Key ERP tracking features:

• Change documents: Automatically track modifications to master data and transactional data.
• Audit trail: Comprehensively log user activities and system changes.
• Serial number management: Track individual items throughout their life cycle.
• Batch management: Monitor groups of materials with common characteristics.
• Warehouse management: Track the movements and locations of goods in real time.
• Asset tracking: Monitor fixed assets from acquisition to disposal.

Technical capabilities:

• Real-time visibility into object status and location.
• Historical tracking and reporting.
• Integration with RFID, barcodes and IoT devices.
• Automated alerts and notifications for object movements.
• Comprehensive reporting and analytics on tracked objects.

Audit trail integrity:

• Immutable logs: Audit trail entries can’t be modified or deleted once they’re created.
• Digital signatures: Critical audit trail data is cryptographically protected.
• Time stamping: Secure time stamping prevents backdating of entries.

Configuration requirements:

• Many audit trail features require specific activation and configuration.
• Some capabilities may need additional licensing, such as Governance, Risk and Compliance solutions.
• Regular maintenance is required for optimal performance.

ESG fraud detection

As sustainability reporting becomes increasingly mandatory for companies worldwide and tied to financial incentives, fraud examiners must prepare for financial deception related to environmental, social and governance (ESG) reporting. The 3P framework can help address this emerging risk area by detecting “greenwashing” (the practice of making misleading environmental claims) and other forms of ESG misrepresentation. By creating verifiable data trails that connect reported sustainability metrics to actual operational data, the 3P approach makes ESG fraud significantly more detectable.

Common ESG fraud schemes:

• Falsifying carbon emissions data.
• Overstating renewable energy usage.
• Misrepresenting supply chain ethical standards.
• Inflating diversity metrics.
• Claiming nonexistent sustainability certifications.
• Manipulating environmental impact assessments.

New regulatory focus

With the ISSB now requiring comprehensive frameworks for sustainability-related financial information, fraud examiners face new challenges in verifying compliance. The 3P Model aligns with these requirements by creating auditable connections between sustainability claims and operational reality.

Looking ahead: E-invoicing and fraud prevention technology

The implementation of mandatory e-invoicing beginning this year (in some countries) represents a major advancement in fraud prevention capabilities. This system, integrated with AI tools, enables real-time validation and immediate data sharing with tax authorities, substantially reducing opportunities for invoice fraud, tax evasion and fictitious vendor schemes.

E-invoicing systems integrate perfectly into the 3P Model as both an input and output element, creating additional verification points throughout the business process. (See 3P Model chart above.) You can prepare your organization with these steps:

1. Invest in technical expertise: Hire or train staff in AI and data analytics.
2. Create cross-functional teams: Combine financial, IT and operational expertise.
3. Implement in phases: Start with high-risk areas and expand systematically.
4. Update fraud risk assessments: Incorporate new data streams and detection capabilities.
5. Develop response protocols: Create clear action plans that indicate when AI systems flag potential issues.

A new era for fraud prevention

For fraud examiners, AI, object tracking and blockchain technologies aren’t just new tools but a fundamental paradigm shift from periodic, sample-based detection to continuous, comprehensive prevention. As international standards evolve to incorporate sustainability reporting, the 3P Model is a flexible framework that can adapt to new requirements while strengthening core fraud prevention capabilities.

The result isn’t just accurate financial statements; it’s a fundamental rebalancing of the risk-reward equation that makes fraud less attractive to potential perpetrators. By increasing the technical sophistication required to commit fraud while simultaneously improving detection capabilities, the 3P Model helps create financial systems where trust is built on verification rather than assumption.

Kurt Ramin, CFE, is a financial reporting expert specializing in emerging technologies and international reporting standards. He consults with organizations on implementing advanced fraud prevention systems and has contributed to the development of international reporting frameworks. Contact him at kurtramin@yahoo.de.

Klara Weiand, Ph.D., is a partner in Deloitte‘s Risk, Regulatory & Forensic practice. She advises companies on optimizing investigations and litigations through the use of innovative technologies and advanced analytical techniques. Contact her at kweiand@deloitte.de.

Tim Danne is a senior consultant in Deloitte‘s Risk, Regulatory & Forensic practice. He supports clients in investigating potential misconduct and rule violations. Contact him at tdanne@deloitte.de.

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Glossary of terms

Object tracking: The systematic identification, monitoring and documentation of physical and digital entities throughout their life cycle within an organization. This includes tangible assets (equipment, inventory), intangible assets (intellectual property, digital assets) and dynamic resources (human capital, customer relationships).

Value: The worth, utility or importance that something has to someone or in a particular situation. It represents the benefit, significance or desirability that an individual, group or market assigns to an object, service, idea or concept.

Value coordinator: Serves as the organizational facilitator who ensures that value creation initiatives are properly planned, executed and measured across all stakeholder groups. Sometimes called a chief value officer, who must ensure that all relevant aspects of value creation and destruction are accounted for and communicated.

Double-entry bookkeeping: An accounting system in which each transaction affects at least two accounts, maintaining the fundamental equation that assets equal liabilities plus equity.

Financial reporting: The structured communication of financial information about an organization to internal and external stakeholders. This encompasses financial statements, management reports, regulatory filings and other formalized information disclosures about financial position, performance and cash flows.

Enterprise Resource Planning (ERP) systems: Integrated software platforms that organize and manage business processes across functional areas, typically including modules for finance, human resources, supply chain and operations.

3P Model: A proposed framework for restructuring financial statement reporting around three fundamental object categories: products (goods and services), people (human resources), and physical infrastructure (facilities and equipment).

Direct method of cash flow reporting: A cash flow reporting approach that directly discloses operating cash receipts and payments, rather than deriving them through adjustments to net income.

Neural network: A computational model inspired by the structure and function of biological neural networks in the brain. It consists of interconnected nodes (called "neurons" or "units") organized in layers that process and transmit information.

Blockchain technology: A distributed digital ledger technology that maintains a continuously growing list of records (called blocks) that are linked and secured using cryptographic principles. Each block contains a cryptographic hash of the previous block, a time stamp and transaction data, creating an immutable chain of records that can’t be altered retroactively without changing all subsequent blocks.

E-invoicing: An invoice issued, transmitted and received in a structured electronic format that enables it to be automatically and electronically processed. In some countries e-invoicing is mandatory, with copies of the invoices forwarded to the tax authorities.

Sustainability reporting: The disclosure, whether voluntary, solicited or required, of nonfinancial-performance information to outsiders, dealing with qualitative and quantitative information concerning environmental, social, economic and governance issues.

Privacy regulation: Legislation that governs how organizations collect, process, store and share personal data to protect individuals’ privacy rights and ensure data security compliance.

XBRL (eXtensible Business Reporting Language): A standardized format for financial reporting that enables automated processing of business information through standardized taxonomies and digital tags.