Affiliated with: Department of Accounting, Federal University Dutsin-Ma, Katsina State, Nigeria
pISSN: 3115-6606; eISSN: 3156-2809
ORIGINAL RESEARCH ARTICLE
Rihanat Onize Amin, Ibrahim Yakasai Aminu and Josephine Chinelo Ene
Department of Accounting and Finance, Baze University, Abuja, Nigeria
Corresponding Author: Rihanat Onize Amin 
rihanat.amin@bazeuniversity.edu.ng
This study examines how data analytics and asset tracing affect asset recovery and prosecution success within Nigeria’s EFCC. The study was necessitated by the limited empirical evidence on how specific forensic accounting techniques contribute to post-investigation enforcement outcomes, particularly asset recovery and prosecution success. Anchored on Deterrence Theory, the study adopted a cross-sectional survey design. The study collected primary data using a structured five-point Likert-scale questionnaire administered to relevant EFCC personnel. Of the 157 questionnaires administered, 152 were retrieved, while 150 valid responses were retained for analysis. We used Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4 to assess the measurement and structural models. The findings reveal that data analytics had a significant positive effect on asset recovery (β = 0.531, p < 0.001) but did not significantly influence prosecution success (β = 0.169, p = 0.138). Asset tracing had significant positive effects on both asset recovery (β = 0.170, p = 0.039) and prosecution success (β = 0.266, p = 0.020). Data analytics therefore emerged as the stronger predictor of asset recovery, while asset tracing was the only technique with a significant direct effect on prosecution success. The study concludes that forensic accounting techniques contribute differently to economic and legal enforcement outcomes and recommends strengthening analytical and asset-tracing capabilities while integrating them with appropriate evidential, legal and prosecutorial processes.
Keywords: Asset recovery; Forensic accounting; Prosecution success
Effective financial crime enforcement goes beyond establishing that an offense occurred. Financial investigations may identify suspicious transactions, reconstruct financial movements and locate assets associated with criminal activity. Yet these achievements do not necessarily lead to the recovery of illicit proceeds or the successful prosecution of offenders. International asset-recovery frameworks therefore emphasize the progression from financial investigation and asset tracing to restraint, confiscation, and recovery of criminal property, showing that effective enforcement involves interconnected investigative, legal, and institutional processes (Financial Action Task Force [FATF], 2023, 2025).
This distinction matters in financially motivated crime because offenders may retain substantial economic benefits even after authorities identify the underlying misconduct. Asset recovery seeks to deprive offenders of illicit proceeds, while successful prosecution establishes legal consequences for financial misconduct. Together, these outcomes provide a broader basis for evaluating financial crime enforcement beyond detection and investigation alone (FATF, 2023).
In Nigeria, the Economic and Financial Crimes Commission (EFCC) is statutorily empowered to investigate economic and financial crimes and undertake functions related to identifying and tracing proceeds associated with such offenses (Federal Republic of Nigeria, 2004). Performing these responsibilities in an increasingly complex financial environment requires the ability to reconstruct transactions, identify relationships among accounts and entities, and follow the movement and ownership of assets associated with suspected financial misconduct. Data analytics and asset tracing are particularly relevant to these functions. Data analytics enables investigators to identify anomalies, relationships, and transaction patterns in financial information, while asset tracing helps identify, locate, and track assets or financial interests linked to suspected criminal activity (Akinbowale et al., 2020; FATF, 2025; Kapo et al., 2024).
Existing research generally supports forensic accounting's contribution to financial crime detection, investigation, and prevention (Akinbowale et al., 2020; Olaniyan et al., 2021; Ogbaini et al., 2024). However, far less empirical attention has focused on whether specific forensic accounting techniques contribute to downstream enforcement outcomes such as asset recovery and prosecution success. This distinction is important because identifying suspicious financial relationships or locating criminal assets does not automatically secure their recovery. Recovery may additionally require restraint, confiscation or forfeiture procedures, institutional and cross-border cooperation, and compliance with applicable legal requirements (FATF, 2023, 2025). Similarly, financial intelligence generated through forensic investigation may strengthen a case without independently determining prosecution success, which may also depend on evidentiary requirements, prosecutorial capacity, procedural processes and judicial factors.
The institutional setting also represents a gap. Much of the Nigerian empirical evidence on forensic accounting originates from commercial banks, public-sector organizations, ministries, departments and agencies rather than specialized financial crime enforcement institutions (Olaniyan et al., 2021; Ogbaini et al., 2024). The EFCC operates in a distinctive enforcement environment in which financial investigation is directly connected to asset tracing, recovery, and prosecution-related activities. Consequently, evidence from conventional organizational settings offers limited insight into whether data analytics and asset tracing independently contribute to final economic and legal enforcement outcomes within the Commission.
The study is anchored on Deterrence Theory, particularly Becker's (1968) economic analysis of crime. From this perspective, offending decisions are influenced by expected benefits relative to the probability and consequences of enforcement. Asset recovery may strengthen deterrence by reducing the economic benefits retained from financial crime, while successful prosecution contributes to the credibility of legal sanctions. Forensic accounting techniques may therefore support deterrence where their investigative capabilities contribute to tangible economic and legal consequences.
Against this background, this study examines the effects of data analytics and asset tracing on asset recovery and prosecution success within Nigeria's Economic and Financial Crimes Commission. By disaggregating the two forensic techniques and the two post-investigation enforcement outcomes, the study provides technique-specific and institution-specific evidence on whether forensic accounting capabilities extend beyond financial investigation to support the recovery of criminal proceeds and successful prosecution of financial crime cases.
Forensic accounting supports financial crime enforcement by combining accounting expertise with analytical and investigative procedures that reconstruct transactions, identify financial relationships, and trace the movement of suspected criminal proceeds. This study focuses on two techniques, data analytics and asset tracing, because of their particular relevance to post-investigation enforcement outcomes.
Data analytics involves systematically examining financial and related information to identify anomalies, patterns, relationships, and transaction flows that may warrant further investigation. Techniques such as transaction-pattern analysis, anomaly detection, data matching, and examination of relationships across datasets can help investigators reconstruct financial movements and identify connections among individuals, entities, accounts, and assets (Akinbowale et al., 2020; Kapo et al., 2024). For asset recovery, these analytical capabilities may provide leads concerning the destination and conversion of suspected criminal proceeds. For prosecution, they may strengthen a case's evidential foundation by clarifying transaction sequences and financial relationships. However, analytical findings must ultimately satisfy applicable legal and evidentiary requirements before they can contribute to final enforcement outcomes.
Asset tracing focuses on identifying, locating, and establishing connections between assets and individuals, entities, or transactions associated with suspected financial misconduct. It follows the financial trail created through the acquisition, transfer, conversion or concealment of property and other economic benefits derived from unlawful activity (Financial Action Task Force [FATF], 2023, 2025). Asset tracing is therefore closely related to asset recovery but is not synonymous with it. Tracing identifies and locates suspected criminal property, whereas recovery may additionally require restraint, seizure, confiscation, forfeiture, realization or return through applicable legal and institutional processes (FATF, 2025). Asset tracing may also support prosecution by establishing financial connections between suspected offenders, transactions and property, although its contribution depends on the quality and legal usability of the evidence generated.
The two enforcement outcomes examined are asset recovery and prosecution success. Asset recovery concerns the extent to which proceeds and assets associated with financial crime are successfully identified, secured and recovered through relevant investigative and legal processes. It is particularly important because depriving offenders of illicit proceeds removes economic benefits that might otherwise remain despite detection or prosecution. Prosecution success, by contrast, concerns the extent to which financial crime investigations and the evidence they generate contribute to effective legal proceedings and the successful disposition of cases against alleged offenders. Although forensic accounting may strengthen the evidential foundation of prosecution, final outcomes can also be influenced by evidentiary standards, prosecutorial capacity, procedural requirements and judicial processes. Accordingly, asset recovery and prosecution success represent distinct economic and legal outcomes through which the effectiveness of financial crime enforcement may be assessed.
The study is anchored on Deterrence Theory, particularly Becker's (1968) economic model of crime. The theory proposes that the expected benefits of unlawful behaviour relative to the probability and consequences of enforcement influence offending decisions. Its relevance to financially motivated crime is particularly strong because the anticipated economic benefit constitutes an important incentive for offending.
Asset recovery strengthens the economic dimension of deterrence by reducing the illicit benefits offenders are able to retain, while successful prosecution strengthens the credibility of legal consequences. Data analytics and asset tracing may contribute to these outcomes by improving enforcement institutions' ability to reconstruct financial transactions, identify relationships among funds and assets, and locate proceeds associated with suspected financial crime. However, Deterrence Theory does not imply that forensic techniques independently determine asset recovery or prosecution outcomes. Rather, it provides a framework for examining whether these capabilities contribute to the economic and legal consequences through which financial crime may be discouraged.
Empirical research generally supports the usefulness of forensic accounting in financial crime investigation, although evidence is considerably stronger for detection and prevention than for asset recovery and prosecution. Akinbowale et al. (2020) demonstrated the relevance of technologically supported forensic accounting procedures in combating economic crime, while Kapo et al. (2024) reported that data analytics strengthens the capacity to identify irregularities, patterns, and relationships within complex financial information. Gabrielli et al. (2024) similarly indicated that big-data analytics can deepen forensic examination and reveal relationships that may remain concealed under conventional procedures. These studies establish the investigative value of analytical capabilities but provide limited evidence that such capabilities necessarily translate into recovered assets or successful prosecution.
Evidence concerning asset recovery illustrates the importance of this distinction. Kolawole (2026), examining forensic accounting investigations and the recovery of looted funds in Nigeria, reported variation in asset-recovery outcomes and highlighted the relevance of institutional coordination, policy conditions and enforcement processes. Similarly, Sproat et al. (2024) identified legal and institutional constraints affecting non-conviction-based asset recovery in Nigeria. Their findings indicate that identifying or tracing criminal property represents only part of the recovery process because final recovery also depends on the legal and institutional mechanisms through which traced property is restrained, confiscated, and recovered.
Studies relating forensic accounting to prosecution likewise suggest that investigative capability can strengthen legal proceedings without independently determining their outcomes. Sorunke (2018) found that forensic accounting investigation techniques can improve the quality of evidence available for corruption investigation and prosecution, while Eiya and Otalor (2013) emphasized forensic accounting's role in strengthening the evidential foundation of financial crime cases. More recent Nigerian evidence also indicates that forensic accounting evidence can contribute to fraud prosecution.
Based on the theoretical arguments and empirical evidence, the following null hypotheses are formulated:
H01: Data analytics has no significant effect on asset recovery and prosecution success within the Economic and Financial Crimes Commission.
H02: Asset tracing has no significant effect on asset recovery and prosecution success within the Economic and Financial Crimes Commission.
For empirical testing, the effect associated with each hypothesis is evaluated separately for asset recovery and prosecution success.
The conceptual framework proposes that data analytics and asset tracing independently influence two financial crime enforcement outcomes: asset recovery and prosecution success (Figure 1). The framework therefore specifies four structural relationships: DA → AR, DA → PS, AT → AR and AT → PS. This specification allows comparison of the effects of the two forensic accounting techniques across economic and legal enforcement outcomes.
Figure 1: Framework of the Study
The study adopted a cross-sectional survey design to examine the effects of data analytics and asset tracing on asset recovery and prosecution success within Nigeria's Economic and Financial Crimes Commission (EFCC). Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4 (Ringle et al., 2024). Respondents were drawn from relevant functional areas of the Commission, including Investigation, Forensic Accounting, Intelligence, Audit and Legal/Prosecution.
Primary data were collected using a structured questionnaire measuring Data Analytics (DA) and Asset Tracing (AT) as the independent constructs and Asset Recovery (AR) and Prosecution Success (PS) as the dependent constructs. Responses were measured on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The instrument underwent face and content validation before administration.
We administered 157 questionnaires and retrieved 152. After screening, two inadequately completed questionnaires were excluded, leaving 150 valid responses for analysis, representing 95.5% of the questionnaires administered. Participation was voluntary, and respondents were informed that information provided would be treated confidentially and used strictly for academic purposes.
Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4. The measurement model was assessed using indicator loadings, Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE). Indicator loadings of approximately 0.70 or above were considered satisfactory; Cronbach's alpha and CR values of at least 0.70 established internal consistency; and AVE values of 0.50 or above established convergent validity (Hair et al., 2021). Discriminant validity was assessed using the Fornell–Larcker criterion (Fornell & Larcker, 1981) and the Heterotrait-Monotrait ratio (HTMT) (Henseler et al., 2015).
The structural model examined the independent effects of data analytics and asset tracing on asset recovery and prosecution success and was specified as follows:
Model 1: Asset Recovery
AR = β₀ + β₁DA + β₂AT + ε
Model 2: Prosecution Success
PS = β₀ + β₁DA + β₂AT + ε
Where AR = Asset Recovery; PS = Prosecution Success; DA = Data Analytics; AT = Asset Tracing; β₀ = intercept; β₁–β₂ = structural path coefficients; and ε = error term.
Bootstrapping was employed to determine the statistical significance of the structural relationships using path coefficients (β), t-statistics, and p-values at the 5% significance level. Predictive relevance was assessed using Q². Accordingly, the study evaluated four structural relationships: DA → AR, DA → PS, AT → AR, and AT → PS.
This section presents and discusses the study's empirical findings. It covers the preliminary data screening and respondent profile, descriptive statistics, measurement model assessment, discriminant validity, structural model and hypothesis testing, and discussion of the findings.
A total of 157 questionnaires were administered, of which 152 were retrieved. After data screening, two inadequately completed questionnaires were excluded, leaving 150 valid responses for analysis. Table 1 presents the demographic and work characteristics of the respondents.
Table 1: Distribution of Respondents by Demographic and Work Characteristics
| Variable | Category | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Male | 118 | 78.7 |
| Gender | Female | 32 | 21.3 |
| Qualification | PhD | 49 | 32.7 |
| Qualification | Others | 38 | 25.3 |
| Qualification | BSc/HND | 34 | 22.7 |
| Qualification | MSc/MBA | 29 | 19.3 |
| Years of Experience | 16 years and above | 43 | 28.7 |
| Years of Experience | 1–5 years | 39 | 26 |
| Years of Experience | 11–15 years | 36 | 24 |
| Years of Experience | 6–10 years | 32 | 21.3 |
| Department | Audit Unit | 37 | 24.7 |
| Department | Investigation Unit | 30 | 20 |
| Department | Forensic Accounting Unit | 29 | 19.3 |
| Department | Intelligence Unit | 28 | 18.7 |
| Department | Legal/Prosecution Unit | 26 | 17.3 |
Source: Author’s Computation (2026)
The respondents represented diverse demographic and professional backgrounds. Male respondents constituted 78.7% of the sample, while 21.3% were female. Respondents were drawn from Audit, Investigation, Forensic Accounting, Intelligence, and Legal/Prosecution functions, providing representation across areas relevant to the study’s forensic accounting and enforcement outcomes.
Table 2 presents the descriptive statistics for the study constructs. The results indicate generally favorable perceptions across the four constructs, with all mean scores exceeding the scale midpoint of 3.00. Asset Tracing recorded the highest mean (M = 4.065, SD = 1.048), while Asset Recovery recorded the lowest mean (M = 3.980, SD = 1.056). Overall, respondents tended to agree with the statements measuring the forensic accounting techniques and financial crime enforcement outcomes.
Descriptive statistics were computed directly from the 150 observations in the dataset. Each construct score was calculated as the mean of its questionnaire items, consistent with the five-point Likert response format reported in the methodology. A mean above the scale midpoint of 3.00 indicates an overall tendency towards agreement.
Table 2: Construct-Level Descriptive Statistics
| Construct | No. of Items | N | Mean | SD | Minimum | Maximum |
|---|---|---|---|---|---|---|
| Asset Recovery | 4 | 150 | 3.98 | 1.056 | 1.25 | 5 |
| Prosecution Success | 4 | 150 | 4.04 | 1.056 | 1 | 5 |
| Data Analytics | 5 | 150 | 4.018 | 0.912 | 1.5 | 5 |
| Asset Tracing | 5 | 150 | 4.065 | 1.048 | 1 | 5 |
Source: Author’s Computation (2026)
The measurement model was assessed to establish the reliability and convergent validity of the study constructs. Figure 2 presents the PLS-SEM measurement model, while Table 3 reports the indicator loadings, Cronbach’s alpha, composite reliability, and average variance extracted (AVE).
Figure 2: Measurement Model Outcome, Source: Author’s Computation (2026)
Table 3: Measurement Model Results
| Construct | Items | Loadings | Cronbach’s alpha | CR | AVE |
|---|---|---|---|---|---|
| Asset Recovery | AR1 | 0.951 | 0.952 | 0.965 | 0.873 |
| AR2 | 0.947 | ||||
| AR3 | 0.909 | ||||
| AR4 | 0.93 | ||||
| Asset Tracing | AT1 | 0.971 | 0.979 | 0.984 | 0.924 |
| AT2 | 0.951 | ||||
| AT3 | 0.96 | ||||
| AT4 | 0.975 | ||||
| AT5 | 0.95 | ||||
| Data Analytics | DA1 | 0.934 | 0.949 | 0.961 | 0.83 |
| DA2 | 0.929 | ||||
| DA3 | 0.902 | ||||
| DA4 | 0.895 | ||||
| DA5 | 0.895 | ||||
| Prosecution Success | PS1 | 0.966 | 0.951 | 0.964 | 0.871 |
| PS2 | 0.973 | ||||
| PS3 | 0.895 | ||||
| PS4 | 0.897 |
Source: Author’s Computation (2026)
All indicator loadings ranged from 0.895 to 0.975, exceeding the 0.70 benchmark specified in the methodology. Cronbach’s alpha values ranged from 0.949 to 0.979, while composite reliability values ranged from 0.961 to 0.984, indicating satisfactory internal consistency. The AVE values ranged from 0.830 to 0.924, exceeding the recommended threshold of 0.50 and confirming convergent validity across the constructs.
Discriminant validity was assessed using the Fornell–Larcker criterion and the heterotrait-monotrait ratio (HTMT). The results are presented in Tables 4 and 5.
Table 4: Fornell–Larcker Discriminant Validity Criterion
| Construct | AR | AT | DA | PS |
|---|---|---|---|---|
| Asset Recovery | 0.934 | |||
| Asset Tracing | 0.341 | 0.961 | ||
| Data Analytics | 0.586 | 0.318 | 0.911 | |
| Prosecution Success | 0.208 | 0.317 | 0.248 | 0.934 |
Source: Author’s Computation (2026)
Table 5: HTMT Results
| Constructs | AR | AT | DA | PS |
|---|---|---|---|---|
| Asset Recovery | ||||
| Asset Tracing | 0.353 | |||
| Data Analytics | 0.614 | 0.33 | ||
| Prosecution Success | 0.216 | 0.323 | 0.257 |
Source: Author’s Computation (2026)
The Fornell–Larcker results show that the square root of the AVE for each construct exceeded its correlations with the other constructs, indicating satisfactory discriminant validity. Similarly, all HTMT values were below the 0.85 threshold, with the highest value at 0.614. These results confirm that the constructs were empirically distinct.
After confirming the reliability and validity of the measurement model, the structural model was assessed to determine the effects of Data Analytics and Asset Tracing on Asset Recovery and Prosecution Success. Figure 3 presents the structural model, while Table 6 reports the hypothesis-testing results.
Figure 3: Outcome of Structural Model, Source: Author’s Computation (2026)
Table 6: Structural Model and Hypothesis Testing Results
| Path | β | SE | t-Statistic | p-value | Inference |
|---|---|---|---|---|---|
| Data Analytics → Asset Recovery | 0.531 | 0.088 | 6.053 | <0.001 | Significant |
| Data Analytics → Prosecution Success | 0.169 | 0.11 | 1.483 | 0.138 | Not significant |
| Asset Tracing → Asset Recovery | 0.17 | 0.083 | 2.063 | 0.039 | Significant |
| Asset Tracing → Prosecution Success | 0.266 | 0.114 | 2.323 | 0.020 | Significant |
Source: Author’s Computation (2026)
The results show that Data Analytics had a significant positive effect on Asset Recovery (β = 0.531, p < 0.001) but did not have a significant effect on Prosecution Success (β = 0.169, p = 0.138). Asset Tracing had significant positive effects on both Asset Recovery (β = 0.170, p = 0.039) and Prosecution Success (β = 0.266, p = 0.020). Data Analytics therefore emerged as the stronger predictor of Asset Recovery, while Asset Tracing was the only technique with a significant direct effect on Prosecution Success.
Predictive relevance was also established, with Q² values of 0.308 for Asset Recovery and 0.101 for Prosecution Success. Both values were greater than zero, indicating predictive relevance for the two endogenous constructs, with comparatively stronger predictive relevance for Asset Recovery.
The findings show that Data Analytics had a significant positive effect on Asset Recovery. This suggests that using analytical tools to identify unusual transactions, financial patterns, and relationships enhances the EFCC’s capacity to identify and support the recovery of proceeds of financial crime. This finding is consistent with Akinbowale et al. (2020) and Kapo et al. (2024), who emphasized the importance of data-driven forensic techniques in strengthening financial crime control. The comparatively stronger effect of Data Analytics on Asset Recovery also highlights the value of analytical capability in identifying financial flows and assets connected to illicit activities.
However, Data Analytics did not have a significant effect on Prosecution Success. This suggests that although analytical techniques may generate useful financial intelligence and identify suspicious patterns, these outputs may not translate into successful prosecution on their own. Prosecution outcomes also depend on the admissibility and quality of evidence, legal procedures, and the effective presentation of forensic findings. The results therefore indicate that appropriate evidential and prosecutorial processes must complement analytical capability.
Asset Tracing had a significant positive effect on Asset Recovery, indicating that the ability to identify, locate and establish links between assets and suspected illicit activities contributes to recovery outcomes. This finding supports Sproat et al. (2024), who emphasized the importance of tracing mechanisms within the asset recovery process. Nevertheless, asset tracing should be distinguished from recovery itself, as the eventual confiscation or recovery of identified assets may also depend on legal and institutional procedures.
Asset Tracing also had a significant positive effect on Prosecution Success. By establishing connections among assets, transactions and persons of interest, asset tracing can strengthen the evidential basis required for financial crime prosecution. This finding is consistent with earlier evidence on the contribution of forensic accounting techniques to financial crime investigation and prosecution (Eiya & Otalor, 2013; Sorunke, 2018).
Overall, the findings provide qualified support for Deterrence Theory. Effective asset recovery can reduce the economic benefits associated with financial crime, while successful prosecution strengthens the credibility of legal sanctions. However, the non-significant effect of Data Analytics on Prosecution Success demonstrates that technological and analytical capabilities do not automatically produce legal outcomes. Their effectiveness depends on their integration with evidential, institutional, and prosecutorial processes.
This study examined the effects of Data Analytics and Asset Tracing on Asset Recovery and Prosecution Success within Nigeria’s Economic and Financial Crimes Commission. The findings revealed that Data Analytics had a significant positive effect on Asset Recovery but did not significantly influence Prosecution Success. Asset Tracing, however, had significant positive effects on both Asset Recovery and Prosecution Success. The findings therefore demonstrate that the contribution of forensic accounting techniques differs across financial crime enforcement outcomes.
The study concludes that Data Analytics is particularly important for strengthening asset recovery, while Asset Tracing has broader relevance across both recovery and prosecution outcomes. Although forensic accounting techniques can strengthen financial crime enforcement, their effectiveness, particularly in relation to prosecution, depends on their integration with appropriate evidential, legal and institutional processes.
Based on the findings, the study recommends that the EFCC should strengthen its Data Analytics capabilities through continued investment in analytical infrastructure, integrated financial data systems and specialized staff training to enhance the identification and recovery of proceeds of financial crime.
Asset Tracing capabilities should also be strengthened and integrated across investigation, legal and asset recovery functions to facilitate the identification, restraint and eventual recovery of illicit assets while improving the evidential links required for successful prosecution.
Finally, greater coordination should be established between forensic analysts, investigators and prosecutorial teams from the early stages of financial crime cases. Such integration would help ensure that analytical and asset-tracing outputs are developed in forms that support both recovery proceedings and prosecution requirements.
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