Document Type : Research Paper
Authors
1 Islamic Azad University, South Tehran Branch, Tehran, Iran
2 Islamic Azad University , South Tehran Branch,
3 Islamic Azad University, South Tehran Branch
Abstract
1. Introduction
Economic sanctions are coercive measures imposed by states to restrict international activities of target nations, offering a lower-risk alternative to military conflict (Cordesman et al., 2011). Iran exemplifies this, facing escalating sanctions that incentivize tax evasion through Related-Party Transactions (RPTs). Under sanctions, firms exploit legal gaps and accrual accounting to manipulate profits (Abeysekera, 2003; Arabi et al., 2018), transforming Iran’s financial market into a complex network (Soleimani et al., 2014). Traditional analytical methods fail against such complexity, while metaheuristic models excel. Graph mining uniquely uncovers hidden dimensions in sanctioned markets by analyzing network structures and variable relationships (Hu et al., 2022), especially where information asymmetry impedes tax authorities (Iacovacci & Lacasa, 2019; Yang & Xu, 2024).RPTs occur in nested networks with non-linear relationships (e.g., shared boards, cross-ownership) (Ruan et al., 2019). Sanctions amplify complexity through layered tactics like free trade zones (FTZs) and multi-layer transfer pricing (e.g., sequential sales at non-arm’s length prices) (Chan et al., 2016; Tian et al., 2016). Non-disclosure of ~68% key RPT information (e.g., pricing logic) exacerbates tax avoidance (Barokah, 2013), enabling profit shifting to foreign affiliates and eroding tax bases (Yang & Xu, 2024).
Although RPTs can be economically justified (Gordon et al., 2004a), they risk abuse for private gain (Djankov et al., 2008; Barokah, 2013). In Iran, firms use subsidiaries in FTZs (e.g., Kish, Chabahar) and transfer pricing under Article 132-T of Iran’s Direct Taxation Law to shift profits: e.g., selling goods below market to affiliates, which then export at global prices, registering profits offshore. Weak oversight and fragmented databases hinder monitoring, but Iran’s Taxpayers’ Integrated System (TIS) provides foundational data for analysis.This study proposes a novel framework combining graph mining (to detect high-risk FTZ firms) and Type-3 Sheffer-like Type-4 fuzzy logic (to model tax data uncertainty) optimized by the Jaguar metaheuristic algorithm. It identifies suspicious groups exhibiting structural (e.g., nested ownership) and behavioral (e.g., abnormal pricing) tax evasion patterns, aligning with Iran’s Comprehensive Tax Plan for risk-based audits.
Research Questions:
1. Do economic sanctions increase RPT-based tax evasion?
2. How can advanced data analytics identify and model these hidden patterns?
2. Theoretical Framework
2.1. Related-Party Transactions (RPTs)
Per Iranian Accounting Standard 12 (Audit Organization, 2020), RPTs involve entities with control/influence over financial decisions. Key groups include:
1. Parent/subsidiary entities under shared control.
2. Key management personnel and relatives.
3. Entities with significant economic/management ties.
Two theoretical perspectives exist:
- Agency Theory:RPTs enable opportunism by insiders (Jensen & Meckling, 1976), e.g., underpriced asset sales (Cheung et al., 2006).
- Efficiency View: RPTs reduce transaction costs (Gordon et al., 2004a) but require disclosure to mitigate information asymmetry (Kohlbeck & Mayhew, 2010).
Empirical evidence confirms RPTs facilitate tax avoidance via transfer pricing (Harris et al., 1993; Jian & Wong, 2010), especially in low-tax jurisdictions (Barker et al., 2016).
2.2. Sanctions’ Economic Impact
Sanctions restrict input access, raise production costs (Parsa et al., 2013), contract import-reliant sectors (Caetano et al., 2023), and reduce total factor productivity (Nosratabadi, 2023). They incentivize shifting activities to the informal economy, causing technical inefficiency (Markus, 2024).
3. Methodology
3.1. Data & Variables
- Dependent Variable: Tax evasion, measured by the tax gap (difference between declared and final tax) per OECD standards (Slemrod & Weber, 2012).
- Independent Variable: RPT volume (Iranian Accounting Standard 12).
- Moderator: Sanctions index (PCA-derived from 10 macroeconomic variables, Table 1).
Data: 16,756 RPTs from 1,780 Iranian firms (2016–2020), including:
523 firms in FTZs (zero tax rate under Article 132-T).
1,257 non-FTZ firms with shared boards.
Financial data (net sales, COGS, operating profit) sourced confidentially from Iran’s National Tax Administration (INTA).
3.2. Integrated Framework
1. Graph Mining:
Construct transaction networks (nodes = firms; edges = RPTs weighted by price deviation).
Identify high-risk clusters(e.g., firms in FTZs with below-market pricing).
2. PCA for Sanctions Index:
- Combine 10 macroeconomic variables (e.g., oil exports, currency volatility) into a unified index.
- 2 principal components explain 85% variance (Table 1, Chart 3).
3. Fuzzy Metaheuristic Optimization:
- Apply Type-3 Sheffer-like Type-4 fuzzy logic to model data uncertainty (e.g., transfer pricing discrepancies).
- Optimize via Jaguar algorithm (multi-objective: minimize prediction error [MSFE], maximize detection accuracy).
- Output: Dynamic risk index (transaction volume, price deviation, geographic concentration).
4. Results & Discussion
- Sanctions increased RPT-based tax evasion by 0.414% (vs. 0.389% pre-sanctions).
- The Jaguar model achieved 98.8% accuracy (error rate: 0.012), outperforming traditional methods (40% vs. 74.6% detection rate).
- Graph analysis revealed post-sanctions topological shifts: increased suspicious nodes/clusters (Chart 4).
- Key evasion patterns:
- Multi-layer transfer pricing (e.g., mother → FTZ subsidiary → export).
- Abnormal profitability in FTZ subsidiaries.
- Geographic concentration in low-tax areas.
5. Conclusion & Policy Implications
5.1. Key Findings
Sanctions intensify RPT-based tax evasion by incentivizing complex, hidden transaction networks. The integrated graph-fuzzy-jaguar framework proves superior to linear models in detecting evasion under data uncertainty.
5.2. Innovations
- First application of Type-3 fuzzy logic in taxation.
- Dynamic risk index for audit prioritization.
- Operational compatibility with INTA’s existing systems (e.g., TIS).
5,3. Recommendations
- To INTA:Integrating the model into a blockchain-based real-time monitoring platform and Develop an AI dashboard with risk-tiered visualization (green/yellow/red).
- Domestic Policy: Mandating disclosure of transfer pricing logic and topological RPT networks and establishing a National Networked Data Analysis Center.
- International Cooperation:Leveraging double-taxation agreements for cross-border data exchange.
- Future Research: Extending the model to multinational contexts and designing "tax resilience indices" for sanction-affected economies.
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