# Introduction inancial crime is a non-violent but intentional crime committed for illicit monetary or other unlawful gain from individuals, corporations, government bodies and financial institution (IMF, 2001;Ladan, 2005). It constitutes a very serious threat that manifests itself in virtually all aspects of national life. It is widely spread and involves Internet-based cheque issuance, cash deposit, wire transfer and Automated Teller Machine (ATM) withdrawals performed by institutions, government and individuals on a daily basis. Notable financial crimes include theft, scams, embezzlement, identity theft, money laundering, bribery, smuggling, forgery, counterfeiting and tax evasion (Ibrahim et al., 2015). Financial crimes are characterized by deceit, concealment or violation of trust and can be committed with every form of dynamism, subtleness and camouflage (Agus et al., 2010a). Considerably, financial crime may lead to colossal loss of money as well as undermining the integrity of financial institutions and markets. It may also subvert national growth and development (Spencher and Pickett, 2002;McDowell and Novis, 2001;Okoye and Gbegi, 2013;Ejiofor et al., 2007). Financial crimes may lessen the ability of a country to attract foreign investment and subvert the growth and development of local manufacturing industries (IMF, 2001;Spencher and Pickett, 2002;Yusu, 2009). Financial crime may manifest inform of corruption, fraud and theft. Corruption is any illegal act such as kickbacks, embezzlement and extortion and another misuse of entrusted funds and power for private gain or improper and unlawful enrichment (Gottschalk, 2010;Ksenia, 2008). Fraud is a despicable act with the aim of achieving a personal gain or creating a loss for another through concealment of an illegal act and it is a significant and growing threat to several organizations (Golden et al., 2006;Edelherz, 1977). Most prominent financial frauds are the conversion of public money into personal use, granting of unauthorized loans or overdraft, fraudulent transfer or withdrawals, misrepresentation of quality and quantity during procurement and pyramid trading schemes. Others are posting of fictitious credits, cheque counterfeiting or forging, payroll padding (ghost workers), contract over billings and over-invoicing among others (Okeyi and Gbegi, 2013). Theft of cash, intellect, art or identity is said to take place if it is carried out unlawfully and out of all proportions. Several strategies and measures for combating financial crimes include the use of technology and establishment of agencies and commissions. Technological tools offer a more holistic view of data and highlight potential areas of risk to organizations thereby reducing the incidence of fraud (Deloitte, 2014). Big data and text mining, machine learning and forensic accounting are some of the existing technologies for combating financial crimes (Adegbeie and Fakile, 2012; Shai and Shai, 2014; Agus et al., 2010b;Raghavendra et al., 2011;Kitten, 2016). Impact of criminal personality, opportunity structures, corporate identity, values on crime, and business ethics had been identified as causes of financial crimes (Bussman, 2003). These causes could be attributed to bio-genetic factors which include genetic mutation and heredity (Horton, 1939), psychological factors comprising of personality disorders and sociological factors that include learning environment (Sutherland, 1939). The fundamental techniques for combating financial crime still require a good understanding of its causes and dynamics as all technical and scientific proof have yielded unsatisfactory results (Ayoola eat al., 2015). Existing techniques for presenting the understanding of the causes and dynamics of financial crimes include some baseline and dimension-reduction tools such as Missing Values Ratio (MVR), Low Variance Filter (LVF) and High Correlation Filter (HCF). Others are Random Forests Ensemble Trees (RFET), Backward Feature Elimination (BFE), Forward Feature Construction (FFC), Principal Component Analysis (PCA) (Silipo, 2015) and Factor Analysis (FA). Factor analysis is a method for investigating whether some variables of interest N 1 , N 2 , . . ., N m , are linearly related to a smaller number of unobservable factors F 1 , F 2 , . . ., F k. It is used to identify dimensions underlying response (outcome) for a set of variables such that the observed values for the variables are determined as manifest variables. Some standardized variables are generally used with the correlation matrix modelled such that its dimensions correspond to the factors. Several of manifest variables can be used but more appropriate if they have more than a few distinct values and an approximate bellshaped distribution. Factor analysis based on principal components uses weights and scores to produce factor loadings and scores. These attributes informed its choice for the analysis of the financial security-related issues in Nigeria. The main objective of the study is to take a holistic view of the conceptualization of the main issues that relate to financial crime and provides data that serve the basis for the determination of their impact in Nigeria. Also, the study also provides data that is relevant for drawing conclusion based on a comparison between results from current and some related works. # II. # Related Works An exploration of the statistical methods for fighting financial crime by financial institutions is carried out in (Agus et al., 2010a). Issues on the growing losses of revenue by governments, financial institution and individuals to the various forms of financial crime as well as the review of some statistical techniques for investigative studies of financial crimes were also discussed. The research formulated the necessary steps for account opening, described some visualization, description, analysis and computational tools for data on high volume transactions as well as a machine learning algorithm for detecting financial crime. An investigative study on the impact of economic and financial crime on the Nigerian economy is presented in (Yusus, 2009). A review of the conceptual legal framework as well as the nature, scope and effects of economic and financial crimes under the Nigerian law, was presented. The authors concluded that ICT infrastructure is the main tool that financial criminals rely on in carrying out their unlawful acts. The authors in (Okoye and Gbegi, 2013) evaluated the effect of fraud and related financial crime on the Nigerian economy. The research placed a premium on how the Internet, electronic money transfer (wire transfer) and other related platforms contributed to the current spate of financial crime. Regression-based analysis on available financial crime data revealed that financial crimes portend dwindling Gross Domestic Product (GDP) and shrinking economy. In (Iwasokun et al., 2012), an investigation on the effect of financial crime on the society was presented. A platform for determining the correlation between crimes was also presented based on PCA-based analysis of financial crime data from a Criminal Investigation Department. The authors in [3] examined the effect of financial crime and corruption on manufacturing firms and organizations. The Two Way-ANOVA-based analysis of financial crime data obtained from primary and secondary sources revealed significant and negative implications of financial crime on the manufacturing firms as manifested through drained revenue, operational instability and low level of interest from foreign and domestic investors. # III. # Research Methodology The research methodology is conceptualized in Figure 1 showing data survey, factor analysis by principal components and results phases. Data survey involved a survey of public and private agencies such as banks, insurance company, educational institutions that are involved in different forms of financial activities both offline and online. The selection was based on stratified sampling and respondents were randomly chosen with equal probability. Data preparation involved determination of all relevant variables for inclusion in the analysis, determination of the number of observations sufficient to provide reliable estimations of the correlations between the indices, estimation of valid measure of associations among selected variables and the arrangement of the surveyed observations as a set of data vectors y 1 , y 2 , ? , y d with each denoting a grouped observation of V variables. Data normalization is used to transform the surveyed data to a formatted form. Data with ratings were restructured to a notionally common scale before averaging. # b) PCA-Based Factor Analysis The variables for PCA-based factor analysis of the inducement factors of financial crime are related to one another for the j th respondents, and it is represented as follows (Iwasokun et al., 2012;Gulumbe et al., 2012): Z j represents the j th respondent (the principal component of j th data), ? jk represents the assessment of the j th indices by the k th respondent (the elements of the j th eigenvector ? j for the correlation matrix). The principal components analysis of the survey involved descriptive statistic, correlation matrix, Bartlett's and Kaiser-Mayer Olkin (KMO) tests, component extraction and other statistics of relevance. Descriptive statistics ensured standardization of the measurements used in the normalized data and covered the assigning of the data set variables to zero means, unit variances and standard deviation. The sample correlation coefficient is calculated as follows: ?? ?? ,?? is the variance between two columns in the data matrix, ?? ?? ?????? ?? ?? are the standard deviations of columns x and y respectively. For multivariate analysis, the correlation matrix is analogous to the covariance matrix with correlations in place of covariances. Barlett's test of sphericity ? is used to confirm the adequacy of a sample population by testing the null hypothesis that the variables in the population correlation matrix are uncorrelated. The observed significance level of .0000 is used to reject this hypothesis. The test is based on the formula (Donal, 1993): |??| is the determinant of the correlation matrix, n is the number of observation and p is the number of variables. The KMO test ? for the hypothesis that the variables are uncorrelated in the population is based on the formula: r ij is the correlation coefficient in the correlation matrix; a ij is the partial correlation coefficient and i,j represent the rows and column sizes respectively. A near-zero partial correlation, A guarantees effective factorization and it is obtained from the correlation matrix R as follows: Results Interpretation and discussion var?Z j ? = ? j ? j1 2 + ? j2 2 + ? + ? jk 2 = 1(1) (2) ?? ??,?? = ?? ?? ,?? ?? ?? ?? ??(3) ?= ? ? ?? ???? 2 ????? ?? ? ? ?? ???? 2 ????? ?? + ? ? ?? ???? 2 ????? ?? ? = ? ?[?? ? 1 ? 2?? + 5 6 ] ? ????|??|(4) (5) 8 Year 2019 ( ) E Factor Analysis-Based Investigation into Financial Crime Related Issues in Nigeria The Communality of the squared component loadings for component i is computed as follows: p is the number of variables, ? ip is the value in A for row i, column p. The communalities narrate how well the model performs for each variable while the total communality gives an overall assessment. The eigenvalues of R is calculated as follows: I p is a ?? × ?? identity matrix with eigenvalues ? 1 ? ? ? 2 ? ? ? ? ? p ? and the eigenvector V is calculated as follows: D is the p x p diagonal matrix of eigenvalues of R. From p variables, the principal components (unrotated factors) are extracted based on the criterion presented as follows (Kaiser, 1960): The criterion only extracted a principal component with an eigenvalue greater than ? ? . The unrotated factors are subjected to orthogonal transformation using varimax, equimax, quartimax and promax and the best result was taken. Orthogonal transformation is used to obtain meaningful representation of variables and component mapping along the principal axis. Rotation by varimax is based on the assumption that the interpretability of a factor can be measured by the variance of the squares of its factor loadings. Quartimax rotation involves the minimization of the number of factors needed to explain each variable while equamax rotation is a compromise that attempts to simplify both components and variables. Promax is an oblique rotation that allows factors to be correlated and because it is often more easily calculated than any direct oblimin rotation, it is more useful for large datasets. The determination of component scores is based on a linear equation of the weighted standard scores of each respondent on the variables as follows: ?? ??,?? represents the contribution of b th respondent to c th component, ?? ??,?? is the component score coefficient of a th variable for c th component, f is the number of the extracted components, ?? ??,?? represents the standard score of b th respondent for a th variable and x is the respondents' population. ?? ??,?? is estimated as follows: X represents the allowable minimum score, which in this case is 1, S b represents the raw score for b th index, T b and u b represent the mean and standard deviation respectively, of the raw scores of b th index by the sampled respondents. IV. # Results and Interpretation The result from the analysis is interpreted to determine the correlation and relationship between indices. The Questionnaire shown in Appendix 1 was designed using the indices for the analysis of financial security-related issues. Each of these indices was offered loosed linguistic description and range of values as shown in Table 1. The first part of the Questionnaire provided vital information about each respondent while the second part presented five columns for the respondent to rank each of the sixteen indices based on the scale presented in Table 1. The Questionnaire was administered to Thirty States in the six geo-political zones and the Federal Capital Territory (FCT) in Nigeria and the summary of the survey is presented in Table 2. A total of Sixteen Thousand Five Hundred and Thirty-Eight (16538) copies of the Questionnaire were administered through direct and online contacts. In the direct contact, the researcher visited the surveyed states or engaged the services of third parties while indirect contact involved hosting the Questionnaire on Google forms for online assessment. In both cases, Fourteen Thousand Five Hundred and Forty-Six (14546) respondents returned duly completed Questionnaires. Where necessary, the responses were verified and validated through follow-up meetings and personal interviews with the respondents All the 14546 responses were subjected to factor analysis by principal components using SPSS. The analysis of the respondents' knowledge of financial crime, times fallen victim of financial crime and the distribution of crimes are presented in Tables 3, 4 and 5 respectively. The descriptive statistics shown in Table 6 presents the means and standard deviation of the rating of the indices for the analysis of the financial crime related issues by the respondents. The mean and standard deviation of the rating on 'National Policy on Financial operations and Security' are 3.47(69.0%) and 1.245 respectively while the mean and standard deviation of the rating on 'Legislative, Regulatory and Institutional Framework on Financial operations' are 3.25(65.0%) and 1.172 respectively. These values reveal that on the average, the respondents agreed that the 'National Policy on Financial Operations and Security' and 'Legislative, Regulatory and Institutional Framework on Financial Operations' are strong financial crime related issues. The interpretation is based on the matrix of weight attached to the linguistic values presented in Table 1. Similarly, standard deviations represent the statistical measure of dispersion from the mean for the response values for 'National Policy on Financial Operations and Security' and for 'Legislative, Regulatory and Institutional Framework on Financial Operations' respectively. The communalities of the indices for financial crime related issues are presented in Table 7 showing that communalities of the 'National Policy on Financial operations and Security' and 'Legislative, Regulatory and Institutional Framework on Financial operations' are 0.719 and 0.731 respectively. These imply that 71.9% of the variance in 'National Policy on Financial operations and Security' can be explained by the extracted factors while the remaining 28.1% is attributed to extraneous factors. Similarly, 73.1% of the variance in 'Legislative, Regulatory and Institutional Framework on Financial operations' can be explained by the extracted factors while the remaining 26.9% is credited to extraneous factors. The analysis of the correlation matrix presented in Table 8 shows that the highest correlation of 0.711 exists between 'National Policy on Financial Operations and Security' and 'Legislative, Regulatory and Institutional Framework on Financial Operations'. The next highest correlation of 0.710 exists between 'Legislative, Regulatory and Institutional Framework on Financial Security' and 'Legislative, Regulatory and Institutional Framework on Financial Operations'. The implication of the former is that 'National Policy on Financial Operations and Security' is most likely to share the same factor with 'Legislative, Regulatory and Institutional Framework on Financial Operations'. Similarly, in the later, 'Legislative, Regulatory and Institutional Framework on Financial Security' and 'Legislative, Regulatory and Institutional Framework on Financial operations' will likely share the same factor. A = ? 1 R × ? v ii × v jj (6) c i = ? i1 2 + ? i2 2 + ? + ? ip 2 = ? ? ip 2 p i=1 (7) ?R ? ?I p ? = 0 (8) ?? = ???? ?1 (9) ? ? = 1 p ? p j=1(10) The Least correlation of 0.376 exists between 'Capacity Building/ IT Staff Development' and 'National Policy on Financial Operations and Security'. This means that 'Capacity Building/ IT Staff Development' and 'National Policy on Financial Operations and Security' are not likely to share the same factor. The Barlett's test of sphericity produces a ?? 2 of 7493.525 with a significance level of 0.000 which indicates that the sample population is adequate while the Kaiser-Mayer Olkin (KMO) test produced a measure of 0.950, which further confirmed the adequacy of the sample population. The result of Kaiser Criterion based initial component extractions is presented in Table 9. The orthogonal transformation of the initial component extractions by varimax, promax, equamax and quartimax were carried out and the result obtained from the rotation by varimax, which is presented in Table 10, appeared most realistic and meaningful for interpretation among all others. Table 10 reveals four factors with their corresponding loadings. The result highlighted government-approved policies and regulation as the most critical issues on financial crimes. This view was corroborated by the authors in (Galina, 2014;Sofia de Oliveira et al., 2016) who mentioned that the state of national financial security depends solely on governance efficiency as well as policies and regulations. Other financial crimes related issues highlighted are response and management strategies, capacity building and public awareness and litigation measures. These also agreed with the opinions presented in (Galina, 2014 Given that the standard scores by the b th respondent in the sixteen variables under consideration are W b,1 , W b,2 , W b,3 . . ., W b,16, the financial crimes related issues based on the view of each respondent, in the areas of policies and regulations, responses and management, capacity building and awareness and litigation denoted by M 1 M 2, M 3, and M 4 are modeled as follows: The standard scores by ten randomly selected respondents for each of the sixteen variables under consideration are presented in Table 12. Table 13 shows the calculated percentage contributions of each of the ten sampled respondents to each of the four factors. It is revealed that sampled respondent described with identity Res2 has the highest contribution of 5.76 (17.47%) The eigenvalues and percentage variance for each of the four issues is shown in Table 14. It is revealed that the four extracted issues contributed 71.02% to financial crime related issues in Nigeria. Component 1 described as 'Policies and Regulations' contributes 53.37% out of 71.02%. This implies that government policies and regulations are very germane issues of financial security and must not be taken with levity. Components 2, 3 and 4 contribute 7.43%, 5.13% and 5.10% respectively. These imply that government must also focus on raising the awareness of its people to the need for safe and secure financial system as well as ensuring strong litigation measures against financial related crimes. It is also important that adequate attention be given to building strong capacity for all relevant groups and agencies as well as putting in place facilities for timely response and management of threats to safe financial system. The remaining 28.98% is considered as the expected influences of some extraneous components that are important but their related indices were not considered. V. # Conclusion Nigerian economy has suffered greatly in recent years dues to the rising cases of financial crimes in several agencies of public and private sectors. Financial criminals have used the Internet to commit all manners of frauds, embezzlement, tax invasion, money laundering and other despicable financial acts. Nigeria's international image has also suffered due to the involvement of some of her citizens in cases of financial crimes at local and international levels. In view of these, factor analysis by principal components has been used for the analysis of financial crimes related issues in Nigeria. Four issues were extracted with their respective related indices. The initial component matrix generated was subjected to orthogonal transformation to ensure reasonable factorization. The obtained factor score coefficient matrix provided the basis for the determination of the degree of reasonability of the assessment of every respondent. The obtained eigenvalue of each issue gave its percentage impact on the current spate of financial crimes in Nigeria. The percentage contribution was less than 100, which is a pointer to some significant extraneous (latent) factors whose indices were not considered in the research instrument. The results of the factor analysis placed a high premium on government policies and regulations, responses and management, capacity building as well as awareness and litigation as the major issues for safe and secure financial system in Nigeria. These corroborated the positions held by the authors in [30][31][32][33] who opined that good governance should be provided at all levels for economic and social security, promoting selflessness and patriotism. The authors also agreed on the need for adequate countermeasure and litigation systems as necessary strategies for curbing the menace of financial crime. Findings from the research also established that systemic ways of ensuring that citizens adopt technical know-how for national development rather than committing crimes should be introduced and enforced by the Nigerian government. Year 20196)E( 1Linguistic RepresentationExcellentVery GoodGoodAveragePoorRange of Values4.01-5.03.01-4.02.01-3.01.01-2.00.0-1.0 ð??"ð??"?? =1(11)Year 20199?? ??,?? = ?? +?? ?? ??? ?? ?? ??(12))E(Factor Analysis-Based Investigation into Financial Crime Related Issues in Nigeria 2Serial No.StateNo. of LG SurveyedTotal Questionnaire AdministeredTotal Questionnaire ReturnedTotal Questionnaire not Returned1Abia5300263372Adamawa6425415103Akwa-Ibom852452224Anambra5275254215Bauchi75894871026Benue7652623297Delta10524451738Cross River11687671169Ebonyi61651283710Edo87856879811Ekiti757045711312Enugu86225289413Imo752242010214Jigawa342025916115Kaduna3186181516Kano118948563817Kebbi3202202018Kogi64144011319Kwara45515104120Lagos20102689613021Nasarawa6239239022Ogun765845220623Niger8659659024Ondo181524132519925Osun83542589626Oyo1274246827427Plateau62311973428Rivers6402401129Sokoto31891751430Taraba5580574631FCT362758740Total1581128995001789 3ValuesFrequencyPercent% CumulativePoor9886.86.8Average310221.328.1Good480333.061.5Very Good386326.387.7Excellent179012.3100.0Total14546100.0 4RangeFrequency PercentValid PercentCumulative Percent01165580.11-513729.447.447.46-104473.115.462.811-151641.15.768.516-203572.512.480.9> 205513.319.1100.0Total14546100.0 5Classes of Financial CrimeNumber of Occurrences%Advance fee fraud117712.72Forgery (Fake Cheque)8349.02Money Theft Through ATM8509.18Kickbacks and Extortion7307.89Embezzlement7758.37Corruption and Bribery101410.95Fraud8659.34Money Laundering5365.8Identity theft4624.99Counterfeit Money7007.57Financial Grooming6406.92Insider Trading3724.03Phishing2973.22Total9252100 6VariablesNMeanStd. DeviationNational Policy on Financial operations and Security 13413.471.245Legislative, Regulatory and Institutional Framework on Financial operations13503.251.172Legislative, Regulatory and Institutional Framework on Financial Security13473.271.184Implementation of Conventional Security in Financial Institution13383.211.177Implementation of Financial Security Policy13403.131.219Financial Crime Case Assessment13383.151.210Prosecution of Financial Criminals13443.051.223Proficiency of litigators on Financial Crime Cases13233.131.223Public Awareness on Financial Security13443.171.196IT Literacy of Conventional Security Personnel13463.131.150Availability of IT Security Facility at Financial Centres 12593.051.213Capacity Building/ IT Staff Development12683.041.197Rapid Response to Financial Emergency by Security Agencies13463.111.270Development and Usability of Financial Crime Database System13553.071.267Collaboration Between Financial Agencies13413.091.221Availability of Independent/Private Financial Security organization13433.031.235 7VariablesInitial ExtractionNational Policy on Financial operations and Security1.0000.719Legislature's, Framework on Financial operations Regulatory andInstitutional1.0000.731Legislature's, Regulatory and Institutional Framework on Financial Security1.0000.718Implementation of Conventional Security in Financial Institution1.0000.648Implementation of Financial Security Policy1.0000.664Public Awareness on Financial Crime/Security1.0000.698Development and Usability of Financial Crime Database System1.0000.795IT Literacy of Conventional Security Personnel1.0000.69Capacity Building/ IT Staff Development1.0000.761Collaboration Between Financial Agencies1.0000.756Availability of Independent/Private Financial Security organization1.0000.68Availability of IT Security Facility at Financial Centres1.0000.791Proficiency of litigators on Financial Crime Cases1.0000.788Financial Crime Case Assessment1.0000.623Rapid Response to Financial Emergency by Security Agencies1.0000.621Prosecution of Financial Criminals1.0000.681 8NpFos 1.000 .711 .656 .573 .542.558.441 .421 .432 .383 .376 .451 .458 .470 .420AsFrO .711 1.000 .710 .594 .556.570.500 .460 .449 .493 .439 .441 .515 .493 .489 .445AsFrS.656 .710 1.000 .604 .565.572.522 .523 .466 .476 .466 .446 .484 .488 .478 .421CsAss .573 .594 .604 1.000 .632.585.496 .472 .464 .464 .462 .431 .500 .497 .518 .451FsAss.542 .556 .565 .632 1.000 .635.442 .419 .431 .485 .448 .483 .489 .453 .489 .468FCCcAs .558 .570 .572 .585 .635 1.000 .565 .481 .455 .456 .471 .438 .471 .463 .481 .476ProFC .473 .500 .522 .496 .442.565 1.000 .631 .502 .488 .457 .416 .512 .498 .491 .471LigPr.441 .460 .523 .472 .419.481.631 1.000 .605 .519 .440 .453 .481 .517 .499 .455PubAr.421 .449 .466 .464 .431.455.502 .605 1.000 .593 .482 .460 .458 .489 .474 .454ItLit.432 .493 .476 .464 .485.456.488 .519 .593 1.000 .612 .558 .515 .498 .506 .471FCSEc .383 .439 .466 .462 .448.471.457 .440 .482 .612 1.000 .668 .531 .451 .475 .522CapSt.376 .441 .446 .431 .483.438.416 .453 .460 .558 .668 1.000 .525 .471 .501 .492RapRe .451 .515 .484 .500 .489.471.512 .481 .458 .515 .531 .525 1.000 .643 .540 .527FCDbs .458 .493 .488 .497 .453.463.498 .517 .489 .498 .451 .471 .643 1.000 .696 .590Collb.470 .489 .478 .518 .489.481.491 .499 .474 .506 .475 .501 .540 .696 1.000 .626InOrg.420 .445 .421 .451 .468.476.471 .455 .454 .471 .522 .492 .527 .590 .626 1.000 9Variables1Component 2 34Legislative, Regulatory and Institutional Framework on Financial Security0.762Legislature's, Regulatory and Institutional Framework on Financial operations0.761Implementation of Conventional Security in Financial Institution0.750Collaboration Between Financial Agencies0.748Development and Usability of Financial Crime Database System0.746-0.421Financial Crime Case Assessment0.744Rapid Response to Financial Emergency by Security Agencies0.740Implementation of Financial Security Policy0.732IT Literacy of Conventional Security Personnel0.732Prosecution of Financial Criminals0.724National Policy on Financial operations and Security0.717-0.450Proficiency of litigators on Financial Crime Cases0.717-0.469Availability of IT Security Facility at Financial Centres0.708Availability of Independent/Private Financial Security organization0.708Public Awareness on Financial Crime/Security0.699Capacity Building/ IT Staff Development0.695 10Variables12Component34National Policy on Financial operations and Security0.788Legislature's, Regulatory and Institutional Framework on Financial operations0.773Legislature's, Regulatory and Institutional Framework on Financial Security0.744Implementation of Conventional Security in Financial Institution0.688Implementation of Financial Security Policy0.685Financial Crime Case Assessment0.66Development and Usability of Financial Crime Database System0.784Collaboration Between Financial Agencies0.754Availability Financial Security organization of Independent/Private0.701Rapid Response to Financial Emergency by Security Agencies0.582Availability of IT Security Facility at Financial Centres0.792Capacity Building/ IT Staff Development0.77IT Literacy of Conventional Security Personnel0.6140.441Proficiency of litigators on Financial Crime Cases0.783Public Awareness on Financial Crime and Security0.691Prosecution of Financial Criminals0.658 11Variables1Component 2 34National Policy on Financial operations and Security0.346-0.063-0.162-0.06Legislative, Regulatory and Institutional Framework on Financial operations0.316-0.08-0.077-0.072Legislature's, Regulatory and Institutional Framework on Financial Security0.288-0.148-0.0660.033Implementation of Conventional Security in Financial Institution0.252-0.018-0.032-0.099Implementation of Financial Security Policy0.270-0.0510.124-0.246Public Awareness on Financial Crime/Security-0.135-0.1590.0610.493Development and Usability of Financial Crime Database System-0.1130.522-0.209-0.022IT Literacy of Conventional Security Personnel-0.096-0.1660.350.167Capacity Building/ IT Staff Development-0.081-0.0660.547-0.171Collaboration Between Financial Agencies-0.0830.486-0.138-0.092Availability of Independent/Private Financial Security organization-0.1080.433-0.001-0.146Availability of IT Security Facility at Financial Centres-0.083-0.130.567-0.112Proficiency of litigators on Financial Crime Cases-0.125-0.073-0.1560.602Financial Crime Case Assessment0.228-0.083-0.012-0.015Rapid Response to Financial Emergency by Security Agencies-0.0520.2830.039-0.087Prosecution of Financial Criminals-0.031-0.03-0.1850.454to issue 1 while sampled respondentRes6 has the highest contribution of 3.89 (21.85%) toissue 2. Similarly, sampled respondent described withidentity Res7 has contribution of 3.29(19.35%) to issue 3 and sampled respondent Res6 hasthe highest contribution of 4.50 (19.93%) to issue 4. 12Respond-netsNpFosAsFrOAsFrSCsAssFsAssFCcAsProFCLigPrPubArItLitFCSEcCapStRapReFCDbsCollbInOrg 13Factor 1Factor 2Factor 3Factor 4Respondents%%%%ScoreContributionScoreContributionScoreContributionScoreContributionRes13.7611.360.804.512.3113.562.6811.85Res25.7917.470.020.100.653.841.627.19Res33.229.732.3413.152.9217.143.0313.42Res44.6213.961.8310.281.086.323.5115.54Res53.4710.482.5514.33-0.30-1.772.7712.28Res61.173.523.8921.851.579.204.5019.93Res72.668.041.749.773.2919.350.783.47Res84.0212.120.452.530.935.470.210.93Res93.9111.801.679.413.0517.912.4911.01Res100.501.512.5014.081.538.980.994.38Total33.12100.0017.78100.0017.03100.0022.58100.00 14ComponentExtraction Sums of Squared Loadings Total % of Variance Cumulative %18.53953.36753.36721.1887.42660.7933.8215.13165.9254.8155.09571.020 © 2019 Global Journals ## Appendix 1 ## Questionnaire on Analysis of Financial Crime Related Issues in the Scenery of Nigeria Section A: Profile of Respondents The purpose of this Questionnaire is to conduct investigative analysis of financial crimes related issues in Nigerian with a view to developing a pro-active solution. 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