FinTech Integration and Tax Compliance: A Systematic Literature Review of Risk, Criminal Justice Challenges, and Due Process Implications

Abstract

Tax systems worldwide face a compliance gap that OECD data places at USD 100–240 billion annually in corporate avoidance alone, before accounting for the shadow economy and crypto-asset transactions. FinTech mandatory e-invoicing, real-time transaction matching, and machine-learning audit selection is narrowing the informational conditions that enable evasion, while simultaneously introducing governance risks: opaque algorithmic audit targeting, contested blockchain forensic evidence, and the surveillance potential of programmable money. This article presents a PRISMA 2020 systematic literature review of 59 peer-reviewed articles (Scopus, Web of Science, and ScienceDirect), complemented by IRAMUTEQ lexicometric analysis and an extension of the Allingham Sandmo compliance model to incorporate algorithmic detection probabilities, bomb-crater belief dynamics, and Zero-Knowledge Proof verification. Four thematic clusters emerge: tax compliance behaviour and FinTech adoption (19.92%), digital transformation and corporate performance (35.34%), bibliometric and emerging-technology research (16.54%), and cryptocurrency markets and regulatory challenges (28.20%). Across them, FinTech reduces evasion where institutional and technical conditions allow but generates distributional, evidentiary, and constitutional risks that existing legal frameworks have yet to resolve. In response, we propose the Techno-Legal Due Process Framework (TLDPF) three pillars (Techno-Proportionality, Cryptographic Burden of Proof, and Algorithmic Constitutionalism) grounded in EU/OECD constitutional doctrine as a normative design proposal awaiting empirical validation.

1. Introduction

There is a persistent gap in public finance between what governments are legally entitled to collect and what they actually receive. The OECD puts the annual cost of corporate tax avoidance at somewhere between USD 100 billion and USD 240 billion (OECD, 2023a). Add unreported self-employment income, the shadow economy, and the rapidly expanding universe of crypto-asset transactions, and the true figure is considerably larger. Governments have tried the standard remedies: more auditors, stiffer penalties, and information-exchange agreements between tax authorities. Each of these helped at the margin. None of them fundamentally changed the underlying dynamic, which is that tax authorities have always known far less about taxpayers’ financial situations than taxpayers themselves.

The emergence of Financial Technology (FinTech) has profoundly transformed financial intermediation, reshaping not only private financial markets but also public sector operations, including taxation systems. FinTech broadly refers to the application of digital technologies such as artificial intelligence (AI), blockchain, big data analytics, and digital platforms to deliver financial services in more efficient, inclusive, and innovative ways (Adeoye et al., 2024). While early research primarily focused on FinTech’s disruptive effects on banking and financial services, recent scholarship has increasingly turned its attention to the implications for government functions, particularly tax administration and compliance.

Tax compliance remains a central concern in public economics as governments worldwide face persistent challenges related to tax evasion, avoidance, and administrative inefficiencies. Classical economic theories, such as the deterrence model introduced by Allingham and Sandmo (1972), conceptualise compliance as a function of audit probability and penalty severity. However, subsequent empirical and behavioural research has demonstrated that compliance is also shaped by trust in institutions, perceived fairness, and social norms (Alm, 2019; Alm & Torgler, 2011). Despite these advances, traditional enforcement mechanisms continue to exhibit limitations, particularly in contexts characterised by high informality and limited administrative capacity.

In this evolving landscape, FinTech introduces new opportunities to enhance tax compliance through automation, real-time data processing, and improved transparency. Digital payment systems, for example, generate granular transaction data that can be leveraged by tax authorities to detect underreporting and broaden the tax base (Slemrod, 2019). Similarly, the integration of big data analytics and machine learning into tax administration enables more sophisticated risk assessment and audit selection processes, thereby increasing enforcement efficiency. Blockchain technology has also attracted considerable attention for its potential to create immutable transaction records, reducing opportunities for fraud and enhancing trust in tax systems (Dai & Vasarhelyi, 2017).

FinTech is beginning to change the underlying dynamic of tax enforcement. When invoices are mandatory and electronic, when business payments leave permanent digital traces, and when a firm’s transactions can be cross-checked automatically against bank records, customs declarations, and social insurance contributions, the information asymmetry that makes evasion possible starts to collapse. At the same time, FinTech also poses significant challenges for tax compliance. The rise of digital platforms, cryptocurrencies, and cross-border financial flows complicates the ability of tax authorities to monitor and regulate economic activity effectively (Marian, 2013). These developments have given rise to new forms of tax evasion and regulatory arbitrage, necessitating adaptive and technologically sophisticated responses from public institutions. Furthermore, the adoption of FinTech solutions in the public sector raises critical issues related to data privacy, cybersecurity, and algorithmic governance (Veale & Brass, 2019). The balance between leveraging data for compliance and protecting individual rights remains a central tension in contemporary tax administration.

Decentralised finance protocols pose particular challenges as they operate outside the territorial frameworks that international tax law has relied upon since the League of Nations model conventions (Marian, 2013; Ylönen et al., 2024). Machine-learning audit selection systems raise concerns about discrimination at scale and the absence of meaningful contestation mechanisms for affected taxpayers (Veale & Brass, 2019; Wirtz et al., 2019). Central bank digital currencies introduce further governance dilemmas, notably the risk that programmable tax-withholding mechanisms may exceed the boundaries of fiscal administration and encroach on financial privacy rights (Koparan, 2025; Srokosz et al., 2025).

The intersection of FinTech and tax compliance is thus inherently multidisciplinary, spanning public finance, information systems, law, and innovation studies (Owens et al., 2022). However, the existing literature remains fragmented, with contributions dispersed across different academic domains and lacking a cohesive analytical framework. This fragmentation underscores the need for a systematic and bibliometric review to synthesise current knowledge, identify intellectual structures, and highlight emerging research trajectories.

Moreover, the public sector context introduces unique institutional and governance dimensions that distinguish tax compliance from other applications of FinTech. Unlike private firms, tax authorities operate within complex legal frameworks and must balance efficiency with accountability, transparency, and equity. The effectiveness of FinTech-driven tax solutions therefore depends not only on technological capabilities but also on institutional quality and regulatory design. For instance, the implementation of AI-based compliance systems requires robust governance structures to mitigate risks associated with bias, opacity, and unintended consequences (Azenzoul et al., 2026a; Wirtz et al., 2019).

Another important dimension concerns the heterogeneity of FinTech adoption across countries. Advanced economies, characterised by high levels of digital infrastructure and administrative capacity, are better positioned to integrate FinTech into tax systems. In contrast, developing economies face structural constraints but may also benefit from leapfrogging opportunities, particularly through mobile financial services and digital identification systems. Empirical evidence suggests that digital financial inclusion can contribute to improved tax compliance by formalising economic activity and enhancing traceability (Beck et al., 2018). Machine-learning approaches that combine heterogeneous data sources to produce policy-relevant predictions have proven useful in other public-sector domains, from cross-country assessments of education quality (El Hatimi et al., 2024) to agronomic decision support (Moussaid et al., 2025), suggesting that similar architectures could be adapted to model tax-compliance outcomes across heterogeneous national contexts.

The existing literature lacks the integrative framework needed to navigate these questions. Economists have studied deterrence and compliance behaviour. Tax lawyers have grappled with the jurisdictional puzzles posed by crypto-assets. Information systems researchers have tracked FinTech adoption. Bibliometric analyses have confirmed that the field is growing rapidly while remaining internally fragmented (Aljawarneh et al., 2025; Murphy et al., 2024; Woldeamanuel & Kebede, 2025). What is missing is a synthesis that connects these threads and translates the empirical findings into a normative framework that legislators, courts, and tax administrations can use.

This paper attempts that synthesis. We conduct a systematic literature review of 59 peer-reviewed articles following PRISMA 2020 (Page et al., 2021), and apply IRAMUTEQ lexicometric analysis to map the discourse structure of the field (Camargo & Justo, 2013). We extend the classical Allingham–Sandmo model (Allingham & Sandmo, 1972) to incorporate the specific features of algorithmic enforcement that the standard formulation cannot capture. And we propose the Techno-Legal Due Process Framework, which addresses three problems that the existing literature identifies but does not resolve: the proportionality problem in digital compliance mandates, the evidentiary problem in blockchain-based prosecutions, and the constitutional problem in automated audit selection.

Against this background, the study pursues three objectives: (i) to synthesise the empirical evidence on how FinTech reshapes tax compliance and to map the intellectual structure of the field; (ii) to formalise the mechanisms this evidence implies by extending the Allingham–Sandmo model; and (iii) to translate the resulting diagnosis into a normative due-process framework. Accordingly, the review is organised around three research questions:

RQ1. How does FinTech-enabled enforcement reshape tax-compliance behaviour, and what behavioural and distributional risks (e.g., post-audit "bomb-crater" effects and infrastructure-based compliance burdens) does it generate?

RQ2. What is the evidentiary status of blockchain forensic analysis (taint scoring) in tax proceedings, and what admissibility standard should govern its use?

RQ3. What constitutional and due-process constraints should govern algorithmic audit selection and programmable-money (CBDC) tax mechanisms, and how can these constraints be embedded by design?

Each research question maps directly into one of the three pillars of the proposed framework.

The remainder of the article is structured as follows. Section 2 develops the mathematical foundations. Section 3 describes the review methodology. Section 4 presents the bibliometric and lexicometric findings. Section 5 works through the four thematic clusters in substantive depth. Section 6 introduces the TLDPF. Section 7 sets out the limitations and the agenda for future research, followed by Section 8, which concludes.

2. Theoretical and Mathematical Foundations

Before developing the formal analysis, we state the epistemic status of the equations that follow since they serve two distinct roles. Equations (2), (4) and (6) are formalisations of mechanisms already documented in the reviewed corpus-machine-learning risk-scoring; the bomb-crater effect identified by Mittone (2006) and Advani et al. (2023); and the taint-score forensics described by Spyra et al. (2025)-and are included to render those mechanisms precise rather than to introduce new empirical claims. By contrast, the capacity-adjusted penalty (Equations (7) and (8)), the Zero-Knowledge Proof construction (Definition 1 and Equation (5)), and the admissibility thresholds (Equation (9)) are normative design proposals. They are not empirically validated, and their parameters-for example, ${\alpha }_1$–${\alpha }_4$, $c_{min}$ and $\gamma$ in the capacity-adjusted penalty-would require calibration against administrative-capacity data before they could be estimated, legally adopted, or tested. We return to this validation requirement in Section 7.

2.1. The Classical Model and Why It Is Insufficient

The formal analysis of tax evasion begins with Allingham and Sandmo (1972), who drew on earlier work by Srinivasan (1973) to model the compliance decision as a standard problem in expected utility theory. A taxpayer with true income W chooses declared income $X\le W$ to solve

$$\underset{X\in [0,W]}{max}(1-p)U\left(W-tX\right)+pU\left(W-tX-\theta (W-X)\right),$$

(1)

where t is the tax rate, p is the audit probability, and $\theta$ is the penalty rate on undeclared income. The first-order condition simplifies to the requirement that $p\theta >t$ for full compliance. The policy message is clean: raise p or $\theta$ and the problem gets better.

Fifty years of research since then have accumulated a long list of qualifications. Tax morale, institutional trust, perceived fairness, and the social visibility of compliance all influence behaviour in ways the model does not capture (Alm, 2019; Slemrod, 2019). But for our purposes, the model’s most important limitation is structural rather than behavioural: it treats p as a fixed, homogeneous, and objectively known parameter. Under FinTech enforcement, none of those three assumptions holds.

2.2. Algorithmic Detection: When p Depends on Who You Are

When a tax authority deploys a machine-learning risk-scoring system, the audit probability is no longer a single number applied uniformly across the population. It is a function of each taxpayer’s observable digital footprint. For taxpayer i in period t, the system assigns

$${\widehat{p}}_{i,t}=\sigma \left({\mathit{\beta }}^{\top }{\mathbf{z}}_{i,t}\right),\sigma \left(x\right)={\displaystyle \frac{1}{1+e^{-x}}},$$

(2)

where ${\mathbf{z}}_{i,t}$ is the vector of observable signals and $\mathit{\beta }$ is the model’s coefficient vector. The compliance problem becomes:

$$\underset{X_{i,t}}{max}\left(1-{\widehat{p}}_{i,t}\right)U_i\left(W_i-t{X}_{i,t}\right)+{\widehat{p}}_{i,t}{U}_i\left(W_i-t{X}_{i,t}-\theta (W_i-X_{i,t})\right).$$

(3)

Several things follow from this formulation that the classical model cannot see. A taxpayer who understands that his risk score depends on observable signals has a rational incentive to manage those signals rather than to comply honestly (Goodhart, 1975). Because $\mathit{\beta }$ is typically not disclosed, he cannot compute his own ${\widehat{p}}_{i,t}$, which means the transparency link that due-process protections are supposed to provide is severed before enforcement begins (Veale & Brass, 2019). And as the next subsection shows, the dynamic trajectory of beliefs about ${\widehat{p}}_{i,t}$ after an audit produces perverse incentive effects that neither the regulator nor the taxpayer necessarily intends.

2.3. The Bomb-Crater Effect: A Formal Treatment

Luigi Mittone ran laboratory experiments in the early 2000s and found that taxpayers who had just been audited did not become more compliant in the following periods; they became less compliant (Mittone, 2006). The mechanism was the gambler’s fallacy. Having been “hit once,” they concluded that another audit in the near future was unlikely. Advani and his colleagues confirmed the pattern in real administrative data from the United Kingdom, finding a reduction of seven to ten percentage points in declared income in the two years following an audit (Advani et al., 2023).

We model this with a partial-adjustment belief dynamics equation. Let ${\tilde{p}}_{i,t}$ be taxpayer i’s subjective audit probability in period t, which may differ from the objective ${\widehat{p}}_{i,t}$. After an audit event $A_{i,t}\in \{0,1\}$, the taxpayer updates

$${\tilde{p}}_{i,t+1}={\tilde{p}}_{i,t}-\delta A_{i,t}+\lambda \left({\widehat{p}}_{i,t}-{\tilde{p}}_{i,t}\right),$$

(4)

where $\delta >0$ captures the downward revision after an audit and $\lambda \in (0,1)$ is the partial-adjustment coefficient toward the objective rate. When $\delta >\lambda$, a completed audit pushes subjective beliefs below objective risk for multiple subsequent periods. The enforcement implication is uncomfortable: ML models that target the same high-risk taxpayers repeatedly may generate exactly the belief distortion that feeds post-audit evasion, running directly counter to the compliance improvement they are designed to produce.

2.4. Zero-Knowledge Proofs: Compliance Without Disclosure

A third feature of the FinTech context that falls outside the classical model is the possibility of verifying compliance without observing income. A Zero-Knowledge Proof (ZKP) allows a prover to convince a verifier that a statement is true without revealing any information beyond the fact of its truth (Goldwasser et al., 1989). Formally,

Definition 1

(ZKP System). A protocol $(\mathcal{P},\mathcal{V})$ for language $\mathcal{L}$ is a ZKP system if it satisfies: (i) Completeness-for all $x\in \mathcal{L}$, the honest prover convinces the verifier with probability 1; (ii) Soundness-for all $x\notin \mathcal{L}$, any cheating prover succeeds with at most negligible probability; and (iii) Zero-Knowledge-the interaction transcript is simulable without the witness, leaking nothing about it beyond $x\in \mathcal{L}$.

In the tax context, the relevant language is

$${\mathcal{L}}_{\mathrm{tax}}=\left\{(t,W,X):X\ge X_{min}(W,t)\right\},$$

(5)

where $X_{min}$ is the minimum statutory declaration. A ZKP over ${\mathcal{L}}_{\mathrm{tax}}$ embedded in a CBDC smart contract allows the system to verify that a taxpayer has declared at least the required amount without knowing the declared figure itself-delivering compliance verification while preserving the financial privacy that Article 8 ECHR and GDPR Article 22 protect.

2.5. Blockchain Forensics: What Taint Scores Can and Cannot Prove

The growing use of on-chain analysis in tax fraud prosecutions requires a precise account of what forensic evidence of this kind can actually establish. We model the transaction history of a blockchain as a directed acyclic graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$, where $\mathcal{V}$ is the set of wallet addresses and $\mathcal{E}$ is the set of value-transfer edges. For a target wallet $v^{*}$, the taint score is

$$\tau \left(v^{*}\right)=\sum _{v\in \mathcal{S}}{\displaystyle \frac{\mathrm{flow}(v\to v^{*})}{\mathrm{totalinflow}\left(v^{*}\right)}},$$

(6)

where $\mathcal{S}$ is the set of wallets flagged as associated with illicit activity (Spyra et al., 2025). A high $\tau \left(v^{*}\right)$ tells us that funds passing through $v^{*}$ came from suspicious sources. It does not tell us that the person who controls $v^{*}$ intended anything unlawful or that the person who controls $v^{*}$ is the same person who initiated the transactions in question. Private key theft, malware-driven transfers, and the use of mixing services can all produce high taint scores without criminal intent. That gap between what the mathematics can show and what criminal law requires is exactly what the second pillar of the TLDPF is designed to close.

3. Materials and Methods

3.1. Research Design

The methodology pairs a systematic literature review with a quantitative lexicometric analysis. The systematic review, conducted under the PRISMA 2020 protocol (Page et al., 2021), ensures that the selection of source material is transparent and replicable. The completed PRISMA 2020 checklist is provided as Supplementary Material (Table S1). The lexicometric analysis, performed with IRAMUTEQ software (Camargo & Justo, 2013), extracts the intellectual structure the corpus from statistical patterns in the vocabulary of the texts, rather than from our own prior beliefs about what the literature says. Bringing these two methods together has become common practice in multidisciplinary research fields where the corpus spans academic communities that rarely cite each other (Tranfield et al., 2003; Zupic & Čater, 2015); it disciplines the interpretive process in a way that neither approach achieves alone.

3.2. Search Strategy

We searched Scopus, Web of Science, and ScienceDirect. The search combined two conceptual dimensions-FinTech technologies and tax compliance behaviour:

(“FinTech” OR “Financial Technology” OR “Digital Finance” OR “Mobile Money” OR “Digital Payments”) AND (“Tax compliance” OR “Tax evasion” OR “Tax avoidance” OR “Tax administration” OR “Taxpayer behavior”)

We included peer-reviewed English-language articles, conference papers, and book chapters published between 2015 and 2025, accessible in full text. Preprints, duplicates, and papers with only incidental FinTech relevance were excluded.

3.3. PRISMA 2020 Selection

The initial queries returned 3178 records: 2311 from Scopus, 124 from Web of Science, and 743 from ScienceDirect. After deduplication, title–abstract screening, and full-text eligibility assessment, the final corpus comprised 59 articles. Figure 1 shows the complete selection flow.

3.4. IRAMUTEQ Lexicometric Procedure

The 59 articles were compiled into a single plain-text corpus and imported into IRAMUTEQ 0.8 alpha 7 The software lemmatised the texts, reducing inflected forms to their dictionary roots, then segmented the corpus into elementary context units (UCEs) of approximately 40 words. The Descending Hierarchical Classification (DHC) algorithm partitioned the corpus iteratively, assigning words to clusters when the chi-square association statistic exceeded ${\chi }^2>3.84$ ($p<0.05$). Solutions retaining at least 75% of classified text segments are accepted as methodologically robust (Camargo & Justo, 2013). To ensure replicability and transparency, the corpus comprised the titles, abstracts, and keywords of the 59 selected articles. The generated clusters were labelled based on the dominant keywords and their chi-square statistics; the pertinence and thematic coherence of each cluster were subsequently validated by the research team. Correspondence Factorial Analysis (CFA) was then applied to the word-cluster matrix to produce a two-dimensional map of the discourse.

4. Results

4.1. Bibliometric Analysis

4.1.1. The Field Is New and Growing Fast

Between 2015 and 2018, research at this intersection barely existed as a coherent body of work. A handful of exploratory papers addressed electronic filing adoption or digital payment systems in isolation, but there was no sustained scholarly conversation about the governance implications of FinTech for tax administration. That started to change around 2019, when mandatory e-invoicing programmes launched in Italy, Greece, and several Asian economies began generating the kind of large-scale policy variation that empirical researchers could exploit. The post-2022 acceleration is more striking still. The year 2025 alone accounts for 27 of the 59 articles in our corpus-nearly half the entire sample (Figure 2). Three forces appear to be driving this: the post-pandemic push by governments to digitalise public services, the mainstreaming of AI in revenue administration, and the growing alarm among tax policy communities about the evasion opportunities opened up by crypto-assets (Appendino et al., 2023; Marian, 2013).

4.1.2. Who Publishes and How

Journal articles account for 92% of the corpus; conference papers and book chapters make up the rest (Figure 3). The methodological variety within those journal articles is genuinely wide: panel data econometrics sits alongside laboratory experiments in tax psychology, doctrinal legal analysis, and bibliometric mapping. This diversity is one of the field’s intellectual strengths and also its coordination problem: researchers in different disciplines ask different questions, use different definitions of FinTech, and publish in journals that rarely appear on each other’s reading lists (Gomber et al., 2018; Thakor, 2020).

4.1.3. Geographic Distribution

China and Romania lead with five articles each; followed by Italy, Jordan, Malaysia, the USA, and the UK with four each; and Greece, Indonesia, and Vietnam with three (Figure 4). The emerging economies in this list are there for substantive reasons. Jordan, Malaysia, Indonesia, and Vietnam all have significant informal sectors where FinTech-driven formalisation has the largest potential fiscal impact (Beck et al., 2018; Skandalis & Skandali, 2025a); the research reflects a genuine policy urgency in those contexts. China’s prominence is inseparable from Golden Tax III, which has become a reference model for digital tax administration globally (Su, 2025). Romania and the wider Eastern European cluster likely reflect EU e-invoicing mandates, which have produced the kind of country-level variation in compliance infrastructure that makes for clean comparative research (Demirguc-Kunt et al., 2022).

4.2. Textometry Analysis

We performed a textometry analysis to further analyse the scholarly discourse and segment it into a coherent thematic structure. IRAMUTEQ allows for an objective segmentation of the corpus based on statistical metrics that purge the analysis of subjective encoding. Specifically, the software reduces the text into “lemmas”, the dictionary form of each word and then performs analyses such as the Descending Hierarchical Classification and the Correspondence Factorial Analysis. These rest on statistical correlations between a word and the thematic clusters, using chi-square tests (${\chi }^2$) and p-values that determine whether a word is included in a given theme, as shown in

Table 1. Top 15 lemmas per cluster with ${\chi }^2$ and p-values. Source: authors (IRAMUTEQ).

Cluster 1 (19.92%)			Cluster 2 (35.34%)			Cluster 3 (16.54%)			Cluster 4 (28.20%)
Compliance and FinTech			Digital Transf. & Perf.			Bibliometric and Emerging			Crypto and Regulation
Word	${\mathit{\chi }}^{\mathbf{2}}$	$\mathit{p}$	Word	${\mathit{\chi }}^{\mathbf{2}}$	$\mathit{p}$	Word	${\mathit{\chi }}^{\mathbf{2}}$	$\mathit{p}$	Word	${\mathit{\chi }}^{\mathbf{2}}$	$\mathit{p}$
system	58.50	<0.0001	transformation	46.97	<0.0001	account	74.94	<0.0001	crypto	33.32	<0.0001
compliance	58.50	<0.0001	digital	31.11	<0.0001	literature	41.50	<0.0001	market	32.00	<0.0001
attitude	54.93	<0.0001	digitalization	23.34	<0.0001	research	37.14	<0.0001	asset	32.00	<0.0001
tax	42.45	<0.0001	firm	20.76	<0.0001	review	36.10	<0.0001	regulatory	24.15	<0.0001
adoption	36.23	<0.0001	performance	20.76	<0.0001	bibliometric	30.97	<0.0001	global	24.15	<0.0001
examine	21.77	<0.0001	show	16.65	<0.0001	publication	25.71	<0.0001	regulation	23.67	<0.0001
affect	19.54	<0.0001	enterprise	16.40	<0.0001	topic	25.30	<0.0001	cryptocurrencies	22.29	<0.0001
electronic	16.32	<0.0001	improve	15.22	<0.0001	oracle	20.49	<0.0001	capital	16.95	<0.0001
uncertainty	16.32	<0.0001	difference	15.09	0.0001	systematic	20.49	<0.0001	currency	16.95	<0.0001
behaviour	16.32	<0.0001	government	13.88	0.0002	blockchain	16.95	<0.0001	investment	16.30	<0.0001
association	16.32	<0.0001	list	13.16	0.0003	future	15.90	<0.0001	challenge	16.00	<0.0001
evasion	14.08	0.0002	increase	12.91	0.0003	ai	15.69	<0.0001	scenario	15.63	<0.0001
study	14.87	0.0001	result	12.88	0.0003	discuss	15.69	<0.0001	financial	15.38	<0.0001
cost	12.34	0.0004	msmes	11.69	0.0006	network	15.31	<0.0001	legal	14.33	0.0002
trust	11.90	0.0006	reduction	11.23	0.0008	web	15.31	<0.0001	launder	12.98	0.0003

. Following this analysis, IRAMUTEQ generated four thematic clusters, identified in Figure 5; we then named each theme and discussed it on the basis of the articles included in the analysis.

The DHC solution retained 82.3% of classified text segments, above the 75% robustness threshold (Camargo & Justo, 2013). Figure 5 shows the dendrogram; Table 1 lists the 15 highest-ranked lemmas per cluster with their ${\chi }^2$ statistics.

Table 2. Thematic cluster descriptions. Source: authors.

Cluster	Title	Description
1	Tax Compliance Behaviour and FinTech Adoption	Research examining how digital tools change what taxpayers actually do: e-filing adoption, compliance costs, deterrence dynamics, trust formation, and the unintended behavioural consequences of algorithmic audit targeting.
2	Digital Transformation and Corporate Performance	Studies of the organisational and macroeconomic effects of FinTech on firms: performance gains, reductions in tax avoidance, and the heterogeneous distribution of those gains across large corporations versus MSMEs in different infrastructure environments.
3	Bibliometric Research and Emerging Technologies	Literature mapping the field’s academic landscape alongside studies of next-generation instruments-blockchain accounting, AI auditing, oracle-based smart contracts-and their governance implications.
4	Cryptocurrency Markets and Regulatory Challenges	Analysis of the governance problems created by crypto-assets and DeFi protocols: money laundering, cross-border evasion, the contested evidentiary status of taint-score forensics, and the jurisdictional vacuum created by decentralised systems.

gives a brief description of each cluster.

4.3. Correspondence Factorial Analysis

Figure 6 projects the four clusters onto two orthogonal dimensions. The horizontal axis separates the behavioural literature (left: compliance, attitude, and trust) from the regulatory and governance literature (right: crypto, regulatory, and legal). The vertical axis distinguishes the academic mapping and emerging-technology literature (upper right) from the organisational and macroeconomic performance literature (lower left). All four clusters are cleanly separated, which confirms that the DHC partition is statistically sound and that the intellectual terrain of the field has genuinely distinct regions rather than a single continuous debate.

The CFA presented in Figure 6 maps the academic discourse surrounding FinTech’s contribution to tax compliance, categorising it across behavioural, organisational, and regulatory channels. The upper-left quadrant concentrates on the psychological and systemic drivers of taxpayer engagement, highlighted by terms such as “compliance”, “system”, “attitude”, “behaviour”, and “adoption”. The upper-right quadrant delineates the academic and methodological landscape of the field, featuring keywords like “account”, “research”, “literature”, “review”, and “bibliometric”. The lower-right quadrant shifts heavily toward the governance and regulatory challenges of decentralised finance, anchored by terms such as “crypto”, “market”, “asset”, and “cryptocurrencies”. Finally, the lower-left quadrant turns to the broader organisational impact of these technologies on tax ecosystems, characterised by words like “transformation”, “digital”, “performance”, and “efficiency”.

5. Discussion

5.1. Compliance by Design: The Limits of What Deterrence Can Explain

The Allingham–Sandmo framework, represented in Equation (1), treats the compliance decision as a wager: evade, and probably win; declare honestly, and forgo the gain. Enforcement policy tries to tilt the odds by raising the probability of audit p or sharpening the penalty $\theta$. The model is elegant, and in stable institutional contexts it produces useful predictions.

What it cannot describe is a situation where the informational conditions for evasion are gradually eroded. That is what FinTech appears to be doing in the most advanced jurisdictions. When Golden Tax III in China began feeding invoice data, bank statements, and customs records into a unified matching system, the compliance question for most firms stopped being “will they catch me?” and became “does my reported revenue match the figure the authority already holds?”. It is a structural change in what evasion even means (Kleven et al., 2011; Prichard et al., 2019; Spinelli et al., 2024). The Italian e-invoicing reform produced similar dynamics: revenue recovered faster in sectors with high prior cash intensity, exactly where the reform destroyed the conditions for unreported transactions (Al Ghunaimi et al., 2025; Kotsogiannis et al., 2025). In developing economies like Morocco, public sector digitalisation acts as a key driver of audit quality by reducing these structural information asymmetries (Mahouat et al., 2025a). Scholars have coined the phrase “compliance by design” for this architecture (Omarova, 2020), and we think it captures something important that the deterrence literature has not fully absorbed.

But Cluster 1 carries two findings that complicate any straightforward optimism about algorithmic enforcement. The first, which we formalise in Equation (4), is that audits do not simply produce compliance. Mittone (2006) found in laboratory settings that post-audit taxpayers become less compliant in subsequent periods, and Advani et al. (2023) confirmed this with UK administrative data: seven to ten percentage points less income declared in the two years after an audit. It is the gambler’s fallacy: the phenomenon makes people feel safer after an adverse event rather than cautious. For a machine-learning system that concentrates repeated audits on the same high-risk population, this means the model may be generating the very evasion it is designed to prevent (Kasper & Rablen, 2023).

The second finding concerns trust. Tax morale, the intrinsic motivation of a taxpayer to pay his share by viewing the state as a legitimate partner rather than an adversary, is a significant determinant of voluntary compliance, independent of deterrence calculations (Alm, 2019; Alm & Torgler, 2011; Slemrod, 2019). Research from Vietnam (Do et al., 2022), Indonesia (Kristiana et al., 2025), and Jordan (Pane & Simanjuntak, 2024) consistently shows that willingness to use digital tax systems is mediated by trust in the institution operating them. When an algorithm selects a taxpayer for audit without explanation, and when neither the taxpayer nor the appeals tribunal can inspect the model’s underlying logic and mechanisms, that trust corrodes. The effect is not necessarily immediate or dramatic, but it is cumulative: a compliance culture built on fear of detection rather than on genuine institutional legitimacy tends to be less stable and more prone to creative gaming of observable signals the algorithm uses as inputs (Bird & Zolt, 2008).

A third issue is that the cluster surfaces is distributional. Digital compliance mandates carry infrastructure costs that fall very differently on large corporations and small businesses. For a multinational with a dedicated tax technology team, connecting to a tax authority’s reporting API is intuitive. For a sole trader or a small manufacturer in a region with unreliable connectivity, the same obligation can represent months of work and capital expenditure the business may not have (Mahalle et al., 2021). Imposing uniform penalties for non-compliance, regardless of a firm’s actual technical capacity, is not neutral: it places the heaviest effective burden on the firms least equipped to bear it, with consequences for competitive dynamics and the viability of small business in heavily digitalised economies (Paleka & Vitezić, 2023; Souguir et al., 2025). Figure 7 illustrates how the compliance pathway differs for firms with high versus low digital capacity, and identifies the point where the proportionality safeguard proposed in Section 6 intervenes.

5.2. Decentralised Finance and the Collapse of Territorial Tax Jurisdiction

Tax law is built on the assumption that economic activity can be located somewhere. The right of a state to levy tax on a transaction rests on the physical or institutional presence of the taxpayer within its territory: corporate residence, permanent establishment, and the location of servers or management. These concepts have been the load-bearing structure of international tax law, and they remain the foundation of the OECD framework today (OECD, 2022; Rixen, 2013).

Decentralised blockchain protocols remove the ground under that structure. A decentralised exchange has no headquarters, no employees, no servers in any fixed location, and no legal personality. Its transactions are peer-to-peer, executed by code validated by anonymous nodes distributed across every continent, and settled in pseudonymous tokens. The traditional tax law question related to the location of the event has no answer for a DEX trade because in any meaningful sense the trade happened everywhere and nowhere simultaneously (Marian, 2013). Ylönen et al. (2024) traced how capital seeking tax anonymity has migrated from traditional offshore centres to blockchain protocols, which offer comparable opacity without the reputational cost of incorporation in a known tax haven. Their analysis of “organisational ring-fencing” and “capital swarming” describes structures already common in the digital asset industry, designed explicitly to exploit the gaps in territorial tax doctrine.

The technical response-blockchain forensics and taint-score analysis, formalised in Equation (6)-has produced real results. Spyra et al. (2025) document how transaction-graph analysis has been used to trace illicit flows through mixing services and cross-chain bridges, contributing to prosecutions in the United States and Europe. But the legal status of this evidence remains genuinely unsettled. Courts in different jurisdictions have reached contradictory conclusions about what a taint score establishes, what threshold justifies prosecution, and how chain-of-custody requirements apply to digital records that exist on a distributed ledger rather than in any single physical location (Marian, 2017; Sugimoto et al., 2020). Defence teams have challenged the methodology successfully enough times that prosecutors can no longer rely on forensic evidence alone to support a conviction.

The deeper doctrinal problem is that even perfect forensic attribution of a transaction to a wallet address does not resolve the criminal law question. Tax fraud requires both a wrongful act and criminal intent. When the “act” is the execution of a smart contract that triggers without any human intervention, and when private key theft or malware can produce transactions in someone’s name without their knowledge, translating blockchain evidence into criminal liability raises factual questions that existing doctrine has not yet answered (Finck, 2018; Nakamoto, 2008). Jurisdictions that have tried to apply traditional criminal law categories to these situations have produced inconsistent outcomes, and the resulting forum-shopping incentives distort where crypto-asset businesses choose to incorporate.

The investor behaviour evidence from Cluster 4 is instructive about what thoughtful regulation can achieve. Cong et al. (2023) showed that the introduction of mandatory crypto-asset reporting obligations in the United States measurably changed realisation timing: taxpayers adjusted their portfolio strategies around reporting deadlines in ways consistent with tax-loss harvesting. Markets that were explicitly designed to resist state oversight turned out to be responsive to well-designed information requirements after all. The lesson is not that regulation cannot work in decentralised environments; it is that it has to be designed for the actual architecture of those environments, not adapted from frameworks built for a world of identifiable intermediaries (OECD, 2023b; Werbach, 2018). The OECD’s Crypto-Asset Reporting Framework is a serious attempt in that direction, but it still operates through custodial intermediary logic that most DeFi protocols lack.

5.3. Digital Transformation, Firm Behaviour, and the Unequal Geography of FinTech’s Gains

The largest cluster in the corpus asks a more basic empirical question than the other three: does adopting FinTech actually make firms better governed? The evidence supports this positive relationship at the macro level; however, aggregate data tend to mask critical geographic and distributional nuances.

Among large, publicly listed firms in countries with mature digital infrastructure, the evidence is reasonably consistent. Corporate tax aggressiveness directly undermines economic governance, but the deployment of high-quality audit and corporate governance mechanisms helps mitigate these non-compliant behaviours (Mahouat et al., 2025b). Empirical evidence shows that digital transformation is associated with meaningful reductions in effective tax avoidance among Moroccan listed companies, working primarily through improved disclosure practices and stronger internal audit capacity (Azenzoul et al., 2025). Indeed, the robust functioning of these internal mechanisms in the Moroccan context is heavily influenced by external organisational factors (Gharrafi et al., 2024) and the necessary cooperation between internal and external audit bodies (Mahouat et al., 2024), which together help ensure that digital compliance tools are effectively monitored. Su (2025) finds analogous results from China’s Golden Tax III natural experiment. Sun et al. (2025) confirm in cross-national panel data that more advanced digital enforcement infrastructure correlates with lower aggressive avoidance, particularly for firms with high public visibility. The mechanism is what Equation (2) implies: when the information environment is transparent enough that discrepancies will be detected automatically, the accounting judgement space in which avoidance lives gets compressed (Bharadwaj et al., 2013).

For smaller and less digitally capable firms, the picture is different. Zheng et al. (2023) apply spatial econometrics to Chinese MSME data and find that the performance benefits of digitalisation decline sharply in regions with weak digital infrastructure; in other words, the firms that need the most help from FinTech-enabled institutional improvements benefit least from them. Skandalis and Skandali (2025b) narrow this finding to the compliance domain: VAT gap reductions following FinTech adoption are concentrated in firms that were already digitally capable; less capable firms show no measurable improvement. This is a version of the digital divide pattern that the inequality literature documents more broadly (Acemoglu & Restrepo, 2022): technological gains tend to flow to those already positioned to capture them, while adjustment costs fall disproportionately on those who are not.

The cybersecurity dimension of this cluster connects the performance literature to governance risk in a way that is easy to underestimate. Aggregating sensitive financial data in digital tax systems does not just improve enforcement; it creates a high-value target. A successful intrusion into a national tax authority’s infrastructure can compromise the financial records of millions of firms simultaneously, with consequences for institutional trust and credit markets that extend far beyond the tax system (Owens et al., 2022; Veale & Brass, 2019). The blockchain oracle literature reviewed in Cluster 3 is increasingly attentive to this risk and points towards zero-trust architectures and decentralised identity systems as partial technical responses, alongside regulatory sandboxes and mandatory incident reporting as institutional complements (Bellucci et al., 2022; Caldarelli, 2025).

CBDCs bring the threads of performance, cybersecurity, and constitutional risk together in a single policy question. Programmable state digital currencies with embedded tax-withholding rules could, in principle, eliminate certain forms of evasion entirely: VAT settled at the point of transaction, income tax withheld before income is paid, illicit flows blocked at the protocol level. Simulation results reviewed by Bespalova et al. (2025) suggest a possible 40% reduction in VAT fraud in cash-intensive economies with full CBDC adoption; we treat this as a prospective design estimate rather than an established empirical result. Koparan (2025); Srokosz et al. (2025) sketch legal architectures for such systems. But the same programmability that makes CBDCs powerful fiscal tools also makes them potentially powerful control tools, and the constitutional safeguards needed to prevent one from sliding into the other have barely been articulated in the literature (Allen et al., 2020). The ZKP mechanism formalised in Equation (5) offers a technical path through that dilemma, but only if it is built into CBDC design from the start.

5.4. The Machine in the Courtroom: Algorithmic Governance and the Rights of the Taxpayer

Before turning to the constitutional analysis, we note its jurisdictional scope. The doctrinal discussion in this section is grounded in the EU and OECD context-in particular Article 8 ECHR, GDPR Article 22, the EU AI Act, the Dutch SyRI ruling, and the OECD Crypto-Asset Reporting Framework. We do not claim that these principles apply uniformly across civil-law, common-law, and non-European tax systems; extension of the framework to those settings is a matter for future work (see Section 7).

The fourth tension that runs through all three previous clusters is the constitutional one, and in our view it is where the gap between what the technology makes possible and what legal doctrine currently supports is most acute.

For example, consider a small business owner who receives a notice that she owes back taxes and penalties. She is told that an automated system placed her in a high-risk category. She does not know what variables the model used, what weight they carried, or whether the model was validated on a representative sample of businesses like hers. She has thirty days to pay or appeal. If she appeals, she faces an administrative tribunal whose members likely have no technical background in machine learning. The system has made a consequential decision about her finances with no explanation, no transparency, and no meaningful route to challenge.

This scenario has already materialised. The Dutch SyRI system, which used algorithmic profiling to identify welfare and tax fraud, was struck down by the Hague District Court in 2020 because its methodology was opaque to the people it targeted, in violation of Article 8 ECHR and GDPR transparency requirements. The ruling has since been invoked in constitutional challenges in France, Austria, and Germany. The pattern suggests a growing judicial consensus: algorithmic enforcement is not categorically illegal, but a system whose logic cannot be explained to the person it affects fails the procedural requirements that constitutional democracies impose on state power (Veale & Brass, 2019; Wirtz et al., 2019; Zarsky, 2016).

Three distinct legal vulnerabilities run through the current generation of algorithmic tax systems. The opacity problem is the most widely discussed: when taxpayers cannot understand why they were selected, due process protections become formal rather than substantive. The discrimination problem is subtler but potentially more serious: a model trained on historical enforcement data will reproduce whatever biases were embedded in how enforcement decisions were made historically and do so at machine scale with no individual notification (Azenzoul et al., 2026b; Barocas & Selbst, 2016; Kotsogiannis et al., 2025). If self-employed people, ethnic minority entrepreneurs, or residents of particular postcodes were historically over-audited, the model will flag them more often-not because anyone chose to discriminate but because the training data encoded past practice and the model generalised from it. The EU AI Act’s classification of tax audit selection as a high-risk AI system is precisely an acknowledgement of this danger. The data minimisation problem is the third: financial surveillance that goes beyond what is strictly necessary for the stated enforcement purpose is in contrast with proportionality principles that are legally binding across most OECD jurisdictions (Veale & Brass, 2019).

5.5. Costs, Failure Modes, and Resistance: A Counterweight to the Optimistic Reading

The preceding sections document substantial benefits, but a balanced assessment must also weigh the costs and failure modes that the aggregate revenue figures obscure. Four are salient. First, implementation failures are common: large digital-tax programmes routinely overrun their timelines and budgets, suffer integration problems with legacy systems, and produce data-quality errors that generate spurious discrepancies and wrongful audit flags. The benefits documented for Golden Tax III or Italian e-invoicing are realised only after costly, multi-year institutional learning that not every administration can sustain.

Second, digital exclusion is a structural cost rather than a transitional one. As Section 5.3 shows, the firms and individuals least equipped to meet digital mandates-small traders, the elderly, businesses in low-connectivity regions, and the informally employed-bear the heaviest adjustment burden, and uniform mandates risk formalising exclusion rather than inclusion. Third, technology resistance is predictable where systems are imposed without trust: low voluntary adoption, workarounds, and the migration of activity into still-harder-to-observe channels can offset the intended compliance gains, an effect amplified when the enforcement apparatus is perceived as adversarial. Fourth, regulatory failures recur where rules lag the technology: regulatory arbitrage across jurisdictions, the limited reach of custodial-intermediary frameworks over genuinely decentralised protocols, and sandboxes that authorise innovation faster than they can supervise it. Taken together, these four failure modes mean that FinTech is not self-executing; its compliance dividend is conditional on institutional capacity, trust, and inclusive design, and the costs of getting these wrong fall disproportionately on the least powerful actors.

6. The Techno-Legal Due Process Framework

Reading the four clusters together against the mathematical foundations of Section 2 produces a clear diagnosis: FinTech is transforming tax enforcement faster than the legal frameworks designed to govern it can adapt. The result is a set of specific, concrete problems-disproportionate penalties on capacity-constrained firms, legally untested forensic evidence, and opaque algorithmic audit selection-that existing doctrine has not resolved. The TLDPF, illustrated in Figure 8, addresses each of them through a dedicated pillar.

To make the connection between the review findings and the framework explicit,

Table 3. Mapping from IRAMUTEQ clusters to governance problems and TLDPF pillars. Source: authors.

IRAMUTEQ Cluster	Governance Problem	TLDPF Pillar	Mechanism
C1-Compliance behaviour and FinTech adoption	Opacity and behavioural distortion (bomb-crater); regressive, infrastructure-based compliance burden	Pillar I (with Pillar III)	Capacity-adjusted penalty (Equtations (7) and (8)); explainability mandate
C2-Digital transformation and performance	Unequal distribution of gains; SME infrastructure inequity; cybersecurity exposure	Pillar I	Publicly disclosed capacity score and calibrated penalties (Equtations (7) and (8))
C3-Bibliometric and emerging technologies	Immature governance of AI, oracle and blockchain instruments; explainability gap	Pillar III	Explainability mandate; human-in-the-loop review; impact assessments
C4-Cryptocurrency markets and regulation	Collapse of territorial jurisdiction; contested forensic evidence; CBDC surveillance risk	Pillar II (with Pillar III)	Three-tier admissibility standard (Equtation (9)); ZKP privacy-preserving design (Equtation (5))

traces each governance problem from the IRAMUTEQ cluster in which it surfaces, through the pillar that responds to it, to the mechanism by which the pillar operates. The mapping shows that the framework is derived from the empirical structure of the corpus rather than imposed upon it: the proportionality and behavioural problems concentrated in Cluster 1 (with the performance and inequality evidence of Cluster 2) motivate Pillar I; the evidentiary and jurisdictional problems concentrated in Cluster 4 motivate Pillar II; and the algorithmic-governance and emerging-technology problems running through Clusters 1, 3 and 4 motivate Pillar III. The three research questions stated in Section 1 correspond one-to-one to these three pillars.

The findings of the systematic literature review allow us to structure the mechanisms through which FinTech adoption disrupts tax compliance. The adoption of these technologies redefines tax behaviour by reducing tax avoidance and reinforcing operational efficiency. However, significant legal concerns are raised related to technical capacities, audit trails, and the constitutionality of algorithmic decision-making. In response to these concerns, we propose the TLDPF, which aims to bridge the gap between the advantages of FinTech in strengthening tax-fraud prediction and the requirement to respect tax-law obligations.

The first pillar of the TLDPF is the principle of technological proportionality. When the tax code obligates, for example, the integration of specific systems such as electronic invoicing platforms, and an SME fails to comply, the penalty should be proportional to its infrastructural capacity; if a company cannot fully invest in the latest software because of budget constraints, the penalty should take this inadequacy into account.

The second pillar addresses the chain of evidence and the auditability of transactions through the mandated use of blockchain ecosystems to ensure the immutability of transactions. By acknowledging the legal value of blockchain as a decentralised notary-ensuring authenticity and traceability through chronological, tamper-proof records-we bridge the gap between the speed of innovation and the slower pace of the law.

The third pillar concerns the constitutionality of algorithmic decision-making in tax law. It addresses the risk of stripping people of their right to privacy when tax-collection mechanisms are embedded into the smart contracts of digital currencies in ways that make tax evasion impossible. Such mechanisms strengthen the coercive power of the state but can erode citizens’ trust in the social contract. The framework therefore proposes privacy-by-design mechanisms such as Zero-Knowledge Proofs (ZKPs), which establish the legality of an operation under tax law without revealing details such as its amount or the parties involved.

6.1. Pillar I-Techno-Proportionality

The proportionality principle is foundational in administrative law: state measures must not exceed what is necessary to achieve their legitimate objective. Applied to digital tax mandates, this means that the penalty for failing to comply with an e-invoicing requirement should take into account whether the taxpayer had the technical means to comply in the first place.

We operationalise this through a capacity-adjusted penalty. Each firm i is assigned a compliance capacity score $c_i\in [0,1]$ derived from a publicly disclosed matrix:

$$c_i={\alpha }_{1}ln\left(s_i\right)+{\alpha }_2{d}_s+{\alpha }_3{g}_i+{\alpha }_4{k}_i+{\epsilon }_i,$$

(7)

where $s_i$ is firm size, $d_s$ is sector digital readiness, $g_i$ is the geographic infrastructure index, and $k_i$ is existing IT investment. The statutory penalty $\Phi$ is then adjusted as

$${\Phi }_i=\Phi ·f\left(c_i\right),f\left(c\right)=min\left(1,{\left(c/c_{min}\right)}^{\gamma }\right),$$

(8)

where $c_{min}$ is the capacity threshold below which penalties are reduced and $\gamma >0$ controls the shape of the reduction schedule. At $c_i=1$, the full penalty applies. For $c_i<c_{min}$, a significant reduction applies, protecting digitally constrained firms from what would otherwise be a structural tax on infrastructure poverty. As noted in Section 2, the parameters of this schedule are design choices that would require calibration against administrative-capacity data before adoption. By construction, this adjusted penalty schedule satisfies the proportionality principle precisely when $f\left(c_i\right)$ is non-decreasing in $c_i$, $f\left(1\right)=1$, and the capacity-score methodology is publicly disclosed before the compliance obligation takes effect-conditions that the formulation in Equations (7) and (8) is designed to satisfy.

6.2. Pillar II-The Cryptographic Burden of Proof

Taint-score evidence (Equation (6)) is being used in tax fraud prosecutions without a settled legal framework for evaluating it. Prosecutors and defence teams are arguing about the same questions in every case: What does a high score actually prove? What threshold is enough? How should courts handle the attribution gap between a wallet address and a legal person? The second pillar proposes a three-tier admissibility standard that resolves these questions in advance rather than leaving them to ad hoc litigation.

The taint score $\tau \left(v^{*}\right)$ measures the provenance contamination of funds, not the probability that the wallet-holder is culpable; consistent with Section 2.5 and Section 5.2, it cannot by itself establish intent or control. Culpability is therefore established by Tier 2 (attribution through convergent off-chain evidence), for which the taint score is a necessary but not a sufficient condition. Second, the numerical values in Equation (9) are admissibility floors on the evidentiary weight of the forensic exhibit-thresholds below which a chain-analysis result is too weak to serve as primary proof in the relevant proceeding-and are not to be read as probabilities of guilt mapped onto the standard of proof.

Tier 1-Authenticity.The prosecutor must demonstrate that the blockchain record’s Merkle proof is valid and the hash chain is intact, confirming that the record has not been altered since it was included in the ledger. Most major protocols satisfy this by design; the requirement is to give it explicit statutory status.

Tier 2-Attribution. Linking wallet address $v^{*}$ to a named legal person requires convergent off-chain evidence: KYC records, exchange data, IP logs, corroborating financial documents. Attribution cannot rest on chain analysis alone. Private key theft, malware, and mixing services can all produce high taint scores for people who had nothing to do with the underlying transactions.

Tier 3-Evidentiary weight. The taint score $\tau \left(v^{*}\right)$ must clear an admissibility floor ${\tau }_{\mathrm{legal}}$ appropriate to the proceeding before the forensic exhibit can serve as primary evidence:

$$\tau \left(v^{*}\right)>{\tau }_{\mathrm{legal}},{\tau }_{\mathrm{legal}}=\left\{\begin{array}{cc}0.51\hfill & (\mathrm{civil}\mathrm{proceedings})\hfill \\ 0.90\hfill & (\mathrm{criminal}\mathrm{proceedings})\hfill \end{array}\right.$$

(9)

A chain-analysis result whose taint score falls below the applicable floor is inadmissible as primary proof, preventing enforcement from resting on statistical association alone. The floor governs the admissibility of the forensic component only; whether liability is established still depends on the convergent off-chain evidence required by Tier 2.

6.3. Pillar III-Algorithmic Constitutionalism

The third pillar starts from the premise that algorithmic efficiency and constitutional rights are not in conflict by nature-they are in conflict because the systems currently in use were not designed with constitutional constraints in mind. Building those constraints in from the start is both legally necessary and technically feasible.

Three requirements follow. An explainability mandaterequires that any ML-driven audit selection system produce, on request, a human-readable account of why a specific taxpayer was flagged, at a level of specificity sufficient to enable meaningful contestation. This is already implied by GDPR Article 22 and the EU AI Act’s high-risk system provisions, but operational guidance specific to tax contexts is still largely absent from national implementation frameworks (Doshi-Velez & Kim, 2017; Goodman & Flaxman, 2017). Interpretable architectures that pair transformer-based models with lightweight large language models to generate human-readable justifications have already been demonstrated in other regulatory and quality-assessment contexts (Jrondi et al., 2025), and offer a plausible template for the tax-audit explainability mandate proposed here.

A privacy-preserving design requirement mandates that CBDC compliance-verification mechanisms use ZKP protocols satisfying Definition 1-verifying conformity with ${\mathcal{L}}_{\mathrm{tax}}$ in Equation (5) without transmitting the underlying transaction data. Research teams at several central banks have already demonstrated this is technically feasible in prototype implementations (Ben Mekhlouf et al., 2025, 2026). The privacy cost of compliance verification can in principle be reduced to near zero if the architecture is right.

A human-in-the-loop requirement mandates that automated audit selection decisions be reviewed and approved by a qualified human official before becoming enforcement actions. Combined with mandatory algorithmic impact assessments and published bias metrics, this requirement reintroduces human accountability at precisely the point where the SyRI ruling-and the constitutional challenges that followed it in France, Austria, and Germany-found it was missing (Diakopoulos, 2016; Lepri et al., 2018).

However, despite the insights that the framework may introduce, its full implementation entails several costs that administrations and authorities must bear. The first pillar, for instance, requires the capacity to maintain continuous assessment processes, thereby incurring governance and data-analysis costs. The second pillar implies intensive technical training for prosecutors so that they can effectively evaluate cryptographic evidence. Lastly, the third pillar presupposes costs related to integrating explainable models, deploying privacy-preserving cryptography, and maintaining human-in-the-loop mechanisms to mitigate bias. These expenses must be budgeted and carefully factored in if the framework is to deliver its intended benefits.

7. Limitations and Future Research

Several limitations should temper the conclusions drawn above, and each points toward a direction for future work.

First, the final corpus is small: 59 articles. While appropriate for a focused, emerging field and consistent with the PRISMA selection, it constrains the generalisability of the lexicometric structure, and the cluster proportions should be read as indicative rather than definitive. Second, the database scope was limited to Scopus, Web of Science, and ScienceDirect; this excludes potentially relevant material indexed in SSRN, HeinOnline, Google Scholar, and the policy and legal literatures of the OECD, IMF, World Bank, and dedicated legal databases. Because the constitutional and evidentiary arguments draw heavily on legal sources, this exclusion is a genuine constraint, and a future review incorporating legal databases would strengthen the normative side of the analysis. Third, the corpus is concentrated in time: 27 of the 59 articles were published in 2025, so the findings may over-represent the concerns of a single, very recent moment and under-represent slower-moving doctrinal scholarship. Fourth, the English-only restriction omits relevant work in other languages, particularly given the prominence of non-Anglophone jurisdictions (China, Italy, Romania, Indonesia, and Vietnam) in the corpus. Fifth, the corpus shows geographic and author concentration that may shape which problems appear salient.

A sixth limitation is methodological. The mathematical models proposed here-especially the capacity-adjusted penalty (Equations (7) and (8)), the ZKP construction, and the admissibility thresholds (Equation (9))-are normative design proposals, not empirically validated instruments. Their parameters have not been estimated against data, and the TLDPF as a whole has not been tested in any jurisdiction. Seventh, there is a potential mismatch between what the lexicometric mapping can support and the reach of the legal-normative conclusions: the clusters describe the structure of the academic literature, whereas the framework makes prescriptive claims that go beyond what a discourse map can establish. Finally, as noted in Section 5.4, the legal analysis is bounded by the EU/OECD context, so cross-jurisdictional comparability is limited.

8. Conclusions

The study highlights the contribution of FinTech to tax compliance through a comprehensive systematic review of the literature, demonstrating that the impact is transmitted through different channels. Our analysis (bibliometric and topic modelling) reveals that scholars focus on the multidimensional impact, with artificial intelligence, blockchain and big data emerging as the cutting-edge technologies that drive this transformation.

FinTech reduces evasion, in the specific sense that digital enforcement infrastructure lowers evasion where the institutional conditions support it. China, Greece, Italy, and Morocco all provide evidence of this, through different mechanisms and in different contexts, but pointing in the same direction. The mechanism is not primarily deterrence in the Allingham–Sandmo sense; it is the structural narrowing of the informational conditions that make undisclosed income invisible. That is a genuinely important finding for public finance, and for governments considering mandatory digitalisation programmes it provides reasonable empirical support.

But the systematic review also documents several serious risks that the aggregate revenue figures do not reveal. Bomb-crater belief dynamics, formalised in Equation (4), show that algorithmic auditing can reduce compliance among the taxpayers it targets most intensively. The distributional burden of digital compliance mandates falls regressively on firms with weak digital infrastructure. The territorial foundations of international tax law do not apply to decentralised blockchain protocols, and the forensic tools available to prosecutors are ahead of the evidentiary doctrine that would give those tools legal weight. And the constitutional protections against opaque state power-due process, non-discrimination, proportionality, privacy-are being tested by algorithmic enforcement systems that were designed for efficiency, not for accountability.

The TLDPF responds to each of these problems in turn. Techno-Proportionality addresses the regressive burden through a capacity-adjusted penalty formula. The Cryptographic Burden of Proof gives courts a workable evidentiary standard for blockchain forensics. Algorithmic Constitutionalism requires explainability, privacy-preserving design, and human review at the moment of enforcement-the three conditions that the SyRI ruling implicitly demanded and that no major jurisdiction has yet operationalised in its tax administration legislation.

For policymakers, the framework implies four specific actions. The OECD should extend the Crypto-Asset Reporting Framework to cover DeFi protocols directly, rather than relying on custodial intermediary logic that those protocols deliberately lack. National tax administrations should conduct formal capacity assessments before imposing new digital compliance obligations, using the results to parameterise the penalty schedule in Equation (8). Jurisdictions deploying algorithmic audit selection should commission independent algorithmic impact assessments with published bias metrics before deployment, not after. And central banks developing CBDC architectures with tax-compliance features should adopt ZKP-based verification as a constitutional baseline.

Finally, it is important to acknowledge the limitations of this study, most notably the overall lack of empirical evidence detailing the exact day-to-day interactions between FinTech and tax compliance. While digital transformation and behavioural compliance are well documented, critical areas such as DeFi regulation, SME infrastructure equity, blockchain forensics, and CBDC constitutional design remain heavily underexplored. To address these gaps, future research should gather direct insights from key stakeholders-taxpayers, tax inspectors, and technology providers-to understand how these systems work in practice. There is also a strong need for macroeconomic studies using high-level data to measure how technology adoption actually affects the tax gap. Moving forward, the field urgently requires empirical work that isolates causal effects through natural variation in FinTech adoption, alongside formal legal analyses that connect algorithmic tools directly to constitutional doctrine. Ultimately, breaking down the academic fragmentation observed in this review will require genuine interdisciplinary collaboration, bringing cryptographers, legal scholars, and public finance economists to the same table.