Risk Assets Management and Financial Performance of Deposit Money Banks in Nigeria

SIAR-Global Journal of Economics, Finance & Accounting (GJEFA) www.siarpublications.org [email protected] Vol. 1 Issue 1 Sept.-Oct. 2025 75 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Risk Assets Management and Financial Performance of Deposit Money Banks in Nigeria 1Fidelis Umar Abel; 2Yua Henry; 3Temitope Abiodun Oje 1Department of Banking and Finance, Joseph Sarwuan Tarkaa University, Makurdi 2 1Department of Banking and Finance, Federal Polytechnic, Wannune Benue State Corresponding Author; [email protected]. ORCID iD 0009-0000-4710-9757 3Scholar, College of Business and Leadership, Eastern University, St Davids, Pennsylvani, USA Abstract This study investigates the effect of risk assets management on the financial performance of listed deposit money banks in Nigeria. The research specifically examines the effect of three key risk asset components: total loan to deposit ratio (TLDR), non-performing loan ratio (NPLR), and capital adequacy ratio (CARR) on bank profitability, proxied by return on assets (ROA). Employing an ex-post facto research design, the study analyzed panel data from 10 listed deposit money banks in Nigeria over a 10-year period (20142023). The data were subjected to descriptive statistics and a pooled ordinary least squares (OLS) regression analysis to test the hypothesized relationships. The findings reveal that the capital adequacy ratio has a significant positive effect on bank profitability. Conversely, while the loan to deposit ratio exhibited a positive relationship and the non-performing loan ratio a negative relationship with profitability, neither of these effects was statistically significant. The study concludes that maintaining a robust capital base is a critical driver of financial performance for Nigerian banks, providing a buffer against risks and enhancing investor confidence. The insignificant effects of TLDR and NPLR suggest that Nigerian banks may have developed effective strategies to mitigate the direct impact of these credit risk factors on profitability, or that other macroeconomic and operational variables play a more dominant role.The findings recommend that bank managers should prioritize optimizing capital allocation policies to leverage the positive impact of capital adequacy while ensuring continued adherence to regulatory standards to foster long-term financial stability and performance. Keywords: Risk Assets Management, Financial Performance, Capital Adequacy Ratio, Loan to Deposit Ratio, Non-Performing Loans, Deposit Money Banks, Nigeria. 76 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Introduction The banking industry has seen major transformation and challenges affecting their financial performance in recent times. The challenges and difficulties posed by the global financial crisis, the advent of new technologies, regulatory reforms, changing customer expectations, and economic fluctuations have all contributed to an evolving banking landscape and how the global financial performance of the banking system is affected (Ali & Dhiman, 2019). Consequently, traditional banking models have undergone transformations, with a greater focus on cost efficiency and risk management (Adamu, 2022; Ajekwe, Yua, & Tyona, 2024). This issue has led to questions regarding financial performance drivers of banks and the sustainability of their business models. If these questions are answered, it may proffer an understanding of factors that have direct bearing on risk assets management and banks’ financial health essentially for policymakers, regulators, investors, and researchers alike over the world, Mkuma, Henry, & Oje, (2025). Risk assets management is the identification, assessment and prioritization of risks followed by coordinated and economical application of bank assets to minimize, monitor and control the probability and effect of risk assets (Adekunle et al.. 2011; Ajekwe, Yua, Epor, & Victor, 2024). Thus, risk assets management involves the process of identifying risks that affect bank credit facilities, assessing their implications, deciding on a course of action, and evaluating the results. Thus, effective risk management seeks to maximize the benefits of credit facilities while minimizing its negative effect. Risk assets management in the form of Risk-weighted assets (RWA) represents an aggregated measure of different factors affecting the evaluation of financial instruments including cash. Major risk components of the RWA calculation are credit risk. Credit risk components are loans to deposit ratio, non-performing loans ratio and the capital adequacy ratio, considered together to adjust the nominal value of to its more realistic value. In this way, a proper measure of the level to which the underlying risk asset is increasing or decreasing the balance sheet of banks is ascertained (Crouhy, 2006, Saunders & Cornett, 2007; Akwam, et al, 2021). Loan management is another concept that is closely related to risk assets management, uncertainty of adverse outcomes that do not reach the expected profit and negatively affect banks’ loan management and financial status. With regards to banks, this phenomenon is a critical aspect of risk assets management. When borrowers default on payments or exhibit a consistent pattern of late payments, these loans are categorized as non-performing loans. Such instances constitute a major bottle neck to the stability and the financial status of the banks (Kafle, 2023; Omale, Yua, & Azubuike, 2024). The reasons behind nonperforming loans vary widely, encompassing economic downturns and inadequate risk assessment by the lending institution. Non-performing loans are tracked meticulously by banks, as they signal potential financial distress within their customer base and consequently limits banks’ financial performance, Ejinkonye, Yua, Nwanko, & Mbanefo, 2023). Financial performance is a cornerstone of financial stability, especially within the banking sector. It serves as a crucial buffer against unforeseen risks, providing a safety net that ensures banks can weather economic downturns without compromising their ability to meet obligations. Similar to this definition, Jalloh (2017), Jianhua and Katler (2013), and most recently Kwashie, Baidoo and Ayesu (2022) stated that, financial performance of banks acts as a bulwark, absorbing losses and preventing a domino effect that could jeopardize banks sustainability. One of the widely used measures of bank’s financial performance is the profitability of banks. It measures the returns banks generate from use of their assets in terms of loans to investors and customers. Accordingly, Jalloh (2017) stated that, banks should meticulously assess their risk profiles and allocate loans accordingly. This process involves a delicate equilibrium: too little loans expose a banks’ inability to support investment drive, while an excess may hinder profitability. Striking the right balance requires astute risk assets management, financial acumen, and a keen understanding of market dynamics and the banking system, Wuave, Yua, & Yua, (2020). 77 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. The risk management theory emphasized optimizing investment portfolios by taking return and risk into account. In this sense, banks are considered sane investors who aim to optimize their profits while lowering their risks. They do this by utilizing efficient risk management techniques (Kwashie et al. 2022). This implies that banks carefully consider all relevant data regarding credit risk before making judgment. With the use of the risk management framework, portfolios can be built with the goal of either minimizing risk to an appreciable level or maximizing predicted returns (Adamu, 2022). The theory shades light on how diversification and loan risk management might affect a bank's balance sheet, which greatly impacts the sustainability of banks. The banking system plays an important role in the development of any nation, especially in a developing economy like Nigeria. According to Yua, (2025); Yua, & Temitope, (2024); Yua, Epor, & Utor (2023); and Levine (2004), banks facilitate industrial growth, resource mobilization and allocation, boost capital formation, stimulate productivity and growth and provide capital for commercial enterprises. Similarly, Khalid and Hanif (2005; Bem, Mike & Yua, 2025) stated that, banks play a vital role in developing economies an also serve as engines of economic growth. Therefore, the important role of the banking industry in the development of any nation cannot be neglected. This mandate banks to critically look at critical issues such as risk assets management which poses a direct threat to their very existence. Investors and stakeholders must lay emphasis on the establishment of an effective and efficient risk assets management system in order to retain public confidence in the banking system. In light of the preceding, the study is set out to examine the effect of risk assets management on financial performance of deposit money banks in Nigeria. The study specifically examined: the effect of loans to deposit ratio on profitability, ascertain the effect of non-performing loan ratio on profitability and determine the effect of capital adequacy ratio on profitability of listed deposit money banks in Nigeria. 2.1 Conceptual Framework The diagram below explains risk assets management and financial performance. Figure 1: Conceptual Diagram Source: Author’s compilation, 2025. Financial Performance: Profitability Risk assets management Loan to deposit ratio Non - performing loan ratio Capital Adequacy Ratio 78 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Risk Assets Management: Risk assets management of banks refers to the process of managing and overseeing the bank's credit exposures to minimize the likelihood of financial losses. These risk assets include loans, advances, capital adequacy, and other forms of credit extended to individuals, businesses, and governments. Banks face risks such as default, market volatility, and changes in the economy that can affect the repayment of these assets (Osaze, 2011). The loan to deposit ratio (LDR) is a key financial metric used to assess the lending capacity and risk exposure of banks. This ratio is calculated by dividing the total loans a bank has issued by its total deposits. Non-performing loans are those loan facilities which borrowers often have difficulties repaying and there is a growing public attention to the issue over the years with the Nigerian experience resulting to some banks failures (Timothy, 2018). Capital Adequacy Ratio: Regulatory authorities, such as central banks and other financial regulatory agencies, establish minimum capital adequacy ratios that banks must maintain to ensure financial stability and protect depositors' funds. These regulations aim to stop banks from taking excessive risks and becoming insolvent in the event of bad economic conditions or unexpected losses. Typically, higher capital adequacy ratios indicate greater financial strength and resilience for banks. 2.2 Theoretical Review 2.2.1 Risk Management Theory Markowitz (1952) is the lead proponent of the risk management theory. Risk management theory, advanced by Nguyen and Faff (2003), provides a systematic framework for identifying, assessing, and mitigating potential financial and operational risks to achieve corporate objectives. It emphasizes balancing risk and returns through strategic planning, risk asset management, capital adequacy ratios, and other mitigation strategies to enhance financial stability and decision-making. The theory is supported by two main perspectives: the profit or shareholder value maximization motive and the managerial utility maximization motive (Markowitz, 1952). The first asserts that firms engage in risk management to reduce financial distress costs (Ross, 1976; Stulz, 1996), benefit from tax advantages through leverage (Basazinew, 2017), and prevent underinvestment when external funds are costly (Bali et al., 2004). The second suggests that risk-averse managers hedge to protect their wealth and human capital, especially when firm-level hedging is more cost-effective (Stulz, 1984). Moreover, hedging can signal managerial competence to external investors (Ayim & Agyemang, 2021). 2.3 Empirical Review Adeyinka and Henry (2024). Using OLS regression on two Nigerian banks’ financial statements, the study found loan loss provisions insignificantly affect income; results lack generalizability due to limited sample size. Kafle(2023). Analyzing ten Nepalese banks (2012–2021), results showed non-performing loans negatively affect profitability, while capital adequacy, loan-to-deposit, cash reserve ratios, and bank size positively influence returns on assets. Butola et al. (2022). From 38 Indian banks (2005-2019), results showed credit-to-deposit, operating profit, and capital adequacy positively relate to profitability, while non-performing assets, interest margin, and provision coverage show negative effects. Adamu (2022). Panel regression on Nigerian banks (2011–2020) revealed non-performing loans and capital adequacy negatively affect profitability, while loan loss provisions improve it; study excluded recent market risk data. Al Zaidanin and Al Zaidanin (2021). For 16 UAE banks (2013–2019), non-performing loans and costincome ratios significantly reduced profitability, whereas capital adequacy, liquidity, and loan-to-deposit ratios showed weak positive but insignificant impacts. 79 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Banu, Sayaduzzaman, and Sil (2021). Studying four Bangladesh state banks (2012–2016), they found credit risk indicators weakly affect profitability; leverage ratio inclusion was criticized for not representing true credit risk concepts. Saleh and Afifa (2020). Using GMM analysis on emerging market banks (2010–2018), the study found credit risk, liquidity risk, and bank capital significantly influence profitability, with non-performing loans capturing credit risk effectively. Munangi and Bongani (2020). From 18 South African banks (2008–2018), results showed credit risk negatively impacts profitability, while growth improves financial performance, confirming non-performing loans as key credit risk measures. Ekinci and Poyraz (2019). Using 21 Turkish banks, findings revealed non-performing loans negatively correlate with return on assets and equity across state-owned, private, and international banks, indicating adverse credit risk impacts. Namasake (2019). Analyzing 42 Kenyan banks (2010–2015), the study found financial leverage, interest rate, and foreign exchange exposure significantly and negatively affect profitability, showing market risk’s continued challenge. Adegbie and Dada (2018). Using mixed methods, findings showed strong links between risk asset, liquidity management, and bank performance; non-performing loans and low cash deposits negatively affect assets and capital. Osayi, Kasimu, and Nkwonta (2018). Examining ten Nigerian banks, findings revealed derivative financial assets positively and significantly affect profitability, emphasizing derivatives’ importance in enhancing banks’ earnings and overall financial performance. 3.0 Methodology This research employed ex-post facto research design that involves the collection of data across firms on same key characteristics over specific time for the purpose of identifying a common trend of behavior amongst these banks. The design is useful in determining the prevailing behaviors in a population based on past existing data, and is considered appropriate for the present study in since already there are existing data to explore the impact of risk asset management on financial performance of listed deposit money banks in Nigeria. The population of the study consisted of fourteen (14) listed deposit money banks on the Nigerian Stock Exchange as at April 2024. The banks are selected through a purposive sampling technique. The purposive sampling is on the basis of banks with complete data ranging from 2014 to 2023, banks that have been listed from 2014 through to 2023, and banks that report their finances only in Naira value. The list of banks to be used as sample is presented below: First City Monument Bank plc, First City Monument Bank plc, United Bank for Africa, Wema Bank plc. 3.2 Method of Data Analysis The descriptive statistics is employed to summarize the collected data in a clear and comprehensible manner by employing numerical approach. The multiple regression (Panel) technique using ordinary least square regression (OLS) method is adopted in investigating the relationship between the dependent and independent variables. The study adopted the preliminary test for incidences of co linearity in the model which is also necessary. To do this, the Skewness test, the multicolinearity test, and the Dublin Watson test statistics will be deployed. The main advantage of these statistics is that they filter out variables that might distort the result of regression analysis. The study analysis is done using Stata 12.1 version. 80 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. 3.3 Model Specification The study model is specified as; Profitability f (Loan to deposit ratio + nonperforming loan ratio + Capital Adequacy Ratio) ------(1) The model is specified in econometric form as follows: PROFit = β0 + β1TLDRit + β2NPLRit+ β3CARRit+εit ------------------------------------------------- (2) Where; βo = Constant PROF = Profitability TLDR = Total loan to deposit ratio NPLR= non-performing loan ratio CARR = Capital adequacy ratio it= Cross-section (i) at time (t) ε = Error term used in the model. β1 –β3= coefficient of the independent variables. Decision Rule: to accept the null hypothesis if the calculated probability of significance is greater than 0.05. 4.0 Results and Discussions Table 2: Descriptive Statistics of the Variables Min Max Mean Std. Obs. PROF 0.0025847 0.3254044 0.0418577 0.0625736 100 TLDR 0.0026735 0.9915656 0.4402719 0.3202596 100 NPLR 0.0023009 0.9963152 0.1955195 0.2378553 100 CARR 0.1066 0.285 0.184002 0.0354328 100 Source: Stata output From the Table above, the result established that the number of observation for each variable is 100. This means that the study data was collected over a panel of 10 banks for 10 years (2014-2023). For the dependent variable (PROF), the Table revealed a minimum value of 0.0025847 and a maximum value of 0.3254044. The result further revealed a mean value of 0.0418577 for PROF with a standard deviation of 0.0625736. The higher deviation shown by the banks’ PROF reveals that there is high difference in respect to the profit margins between the banks. This could be due to the fact that other banks have larger size in terms of assets thus have stronger financial performance than the smaller banks. For the independent variables, TLDR revealed a minimum value of 0.0026735 and a maximum value of 0.9915656. TLDR further showed a mean value of 0.4402719 with a deviation of 0.3202596. NPLR revealed a minimum value of 0.0023009 and a maximum value of 0.9963152. NPLR further showed a mean value of 0.1955195 with a deviation of 0.2378553. CARR revealed a minimum value of 0.1066 and a maximum value of 0.285. CARR further showed a mean value of 0.184002 with a deviation of 0.0354328. The minimum and maximum values shown by the data represents the highest and lowest values recorded by the banks for PROF, TLDR, NPLR and CARR; while the standard deviation shows the level of fluctuation in the data sect amongst the bank’s under-study. 4.2.2 Diagnostic Tests A number of tests are carried out to ascertain if the data used in the study meet the requirements of the regression technique. They include; data normality test, multicollinearity tests, and heteroscedasticity test. 81 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Data normality test To test for the level of disparity between the data sect which might disrupt the outcome of the result or to ascertain the fitness of the data used for the study, the skewedness and probability of Kurtosis test as data normality tests are employed. Table 6 reveals the normality test result: Table 3: Data normality Variables Pr. Skewedness PROF 0.0000 TLDR 0.0000 NPLR 0.0000 CARR 0.6201 Source: STATA output To test for normality/stationarity of data, the Skewness statistics are used. According to Berenson and Levine, (1999), data is not normally distributed if the probability of skewness is <0.05. Also large positive value for skewness indicates a long right tail; an extremely negative value indicates a long left tail; which is an indication of non-normality of data. The data set for all the variables reveal skewness statistic values that are that are all significant at <0.05 except that of CARR; which means that all the data except CARR values are not within the accepted skewness range for normality. The result of skewness in respect to the study variables reveals that the data set has outliers which may distort the outcome of the regression result if further checks are not put in place. To do that, the study resorted to physical verification of data and venting to filter out cases of extreme should they not align with the stipulated formula. This was done and no data was deleted as all the data met the required criteria. Thus, the reason for the non-stationarity is the fact that accounting data mostly are measured in ratios with wide variation and disparity in the banks PROF, TLDR, and NPLR ratios which could be the reason for the non-stationary data. Regardless, further pre and post regression test are put in place to ensure validity of the regression outcome despite the non-stationary data employed in the study analysis. Multicollinearity test Two tests are carried out to determine the presence or absence of multicollinearity in the data used in this study. They are correlation analysis and variance inflation factor (VIF). i. Correlation analysis Table 4: Correlation results TDLR NPLR CARR TLDR 1.0000 NPLR 0.1371 1.0000 CARR -0.1913 -0.3223 1.0000 Source: STATA Output Correlation considers two variables at a time to determine how they relate to each other. These types of checks are necessary because high correlation cause problems about the relative contribution of each predictor to the success of the model (Guajariti & Sangeeta, 2007). The correlation matrix above shows the absence of a variable to influence the outcome of another among the explanatory variables. All the variables show a low correlation with the highest correlation estimated at 0.1371 between TLDR and NPLR. Correlation statistics that are above 0.75 are considered harmful for analysis (see Gujarati & Sangeeta, 2007) but this is not the case with the current study. 82 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. Variance Inflation Factor (VIF) Table 5: VIF result TEST Model 1 Variance Inflation Factor (VIF) <10 (1.10) Source: Stata output in appendix III To further establish the absence of multicolinearity, the study re-test the independent variables using the VIF test. If the variance inflation result is above 10 then it calls for concern.Table 5 above shows a mean VIF value of 1.10for the model which is within the benchmark value of <10. This further indicates the absence of multicollinearity in the model and this means no independent variable should be dropped from the model. Heteroskedasticity test The study employs Breusch-Pagan Cook test to ascertain the presence or absence of heteroscedasticity in the regression result. Table 6: Heteroscedasticity results Breusch-PaganCook Weisberg Test Decision Robustness Decision Model 1 Prob. 0.0000 Not appropriate 0.0475 Appropriate Source: STATA Output From Table 6 above, it can be gleaned that the model result has heteroscedasticity problem since its probability value is significant at 0.0000. The presence of heteroscedasticity in the model shows that our sampled banks are not homogeneous. This therefore means that a robust or panel regression approach will be needed to capture the impact of each bank heteroscedasticity on the results. The study then employed the Robustness test to give credence to the study choice for a Panel data analysis in the model. The Robustness test in Table 6 above reveal a significance level of 0.0475<0.05 for the model. The robustness result revealed the study choice to adopt the panel regression method employing both fixed and random effect models to ascertain the possible effect of a time variation or bank specific variation in the data used for analysis. 4.2.3 Regression of the Estimated Model Summaries This sub-section presents the results produced by the two model summaries to analyze the three objectives earlier stated in section 1 of the study. Table 7: Pre regression estimation test table for model 1 Statistic P-value i. Hausman Test 0.1856 ii. Lagrangian Test 0.0920 Decision Pooled effect regression Source: STATA Output For the model; to enable the study to choose between the pooled model, fixed-effect model, and the random effect model, the Hausman, and Lagrangian test are conducted with the comparable results/tables placed in Appendix II. The result of the Hausman specification test from table 7 shows a probability value of 0.1856>0.05 which is insignificant thus informs the preference of the random-effect model from the Hausman test. To choose between the random effect and pooled model, the Lagrangian test conducted revealed a probability value of 0.0920 which informs the study’s final decision to choose the pooled effect model. This signifies that the set of data for the banks have no significant issue of banks-specific variance 83 Official Publication of the Society of Innovative Academic ResearchersSIAR PUBLICATIONS Advancing Real-Time Innovative Knowledge Globally. Copyright ©SIAR Publications. All rights Reserved. nor time variance in measurement of the variables that are considered in the regression model. Thus, the pooled effect model is analyzed below: Table 8: Regression Result. S/N Statistic Pooled 1 R-Square 0.1576 2 Adjusted R-Square 0.1312 3 F-Prob 0.0009 4 F-Stat 5.98 5 Coef (Prob) TLDR 0.0158774 (0.398) NPLR -0.0392109 (0.137) CARR 0.5869263 (0.001) _Cons -0.0654617 (0.080) Source: Extract from Stata Output Table 8, presented the regression result between TLDR, NPLR, CARR and PROF. From the model summary table above, the following information is distilled. The R2 which measures the degree of variation of the dependent variable caused by the independent variables stood at 0.1576. The R2 otherwise known as the coefficient of determination shows the percentage of the total variation of the dependent variable (PROF) that is explained by the independent or explanatory variables (TLDR, NPLR&CARR). Thus, the R2 value of approximately 0.1576 indicates that 15.76% of the variation in the profitability of the listed banks is explained by variation in TLDR, NPLR, and CARR while the remaining 84.24% (i.e. 100-R2) could be accounted by other variables not included in this model like other corporate governance mechanisms and government policies stipulated in Nigeria. The adjusted R-square of 0.1312 indicates that, if other variables are considered, the model result may change by 0.0264 (0.1576-0.1312). The regression results as presented in Table 8 above to ascertain the relationship between TLDR, NPLR, CARR and PROF of the banks shows that, when all the independent variables are held stationary or without the variable intercept model (Constant); the PROF variable is estimated at -0.0654617. This simply implies that, when all independent variables are held constant, there will be decrease in the PROF of listed banks up to the tune of 6.5% occasioned by factors not incorporated in the study. Thus, a unit increase in TLDR will lead to increase in PROF by 1.5%. On the other hand, a unit increase in NPLR will lead to decrease in PROF by 3.9%. While a unit increase in CARR will lead to increase in PROF by 58.7%. Finally, the result reveals that there is a significant variation of F.Stat (5.98) at a probability value of 0.0009 which means the model as a whole is statistically fit. 4.3 Test of Hypotheses The hypotheses stated earlier in section one of the study are tested in this section. Ho1: Total loan to deposit ratio has no significant effect on profitability of listed deposit money banks in Nigeria. The result shown in Table 8 reveals a P-value of 0.398>0.05 for TLDR against PROF. The null hypothesis is accepted in this case; which means that, total loan to deposit ratio has no significant effect on profitability of listed deposit money banks in Nigeria. Ho2: Non-performing loan ratio has no significant effect on profitability of listed deposit money banks in Nigeria. The result displayed in Table 8 reveals a P-value of 0.137>0.05 for NPLR against PROF. The null hypothesis is accepted in this case; which means that, non-performing loan ratio has no significant effect on profitability of listed deposit money banks in Nigeria.