Managing Credit Risk in Nigerian Banks

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Ifeyinwa Ajah, HLF15 participant. A functional bank credit system is the key that unlocks the possibilities of economic progress. Credit is very important in wealth creation and is the main business activity for most commercial banks. In Nigeria, we have many distressed or even failed banks, resulting in a loss of investment for shareholders, reduced credit expansion, job loss, and poor economic growth. According to the former Governor of the Central Bank of Nigeria (CBN), Sanusi Lamido Sanusi in 2009, the state of the balance sheet of most banks showed that shareholders essentially have lost their investments in the banks following the financial crisis. For some banks the percentage of non-performing loans to total loans was in the range of 50% and loans tied to the capital market had lost about 70% of their value. This is due to credit risk.

Finding solutions to these problems is my main research motivation. Presently, there is no viable credit risk management system (CRMS) that unifies the loan activities of commercial banks in Nigeria. This enables practices such as identity and collateral fraud, inherent in the loan granting process. Additionally, loan decisions taken at the branch level by either the manager or the loan officer can be biased towards personal preferences. Also toxic loans might not be reported to the central bank to prevent a loss of customers.

There is currently no automated system that supports data interoperability for credit information sharing. This encourages multiple lending to customers. The current credit assessment system has no capacity to detect fake identity and fake collateral, compute creditworthiness of an obligor, assess credit risk and compute the capital requirements to reduce the credit risk.

To offer solution to the problems outlined above, my research presented a Model of Digital Nervous System-based Bank Credit Risk Management in Nigeria (DNS-CRMS) that enables drastic reduction of credit risk which lead to bank failure in 2009. It is a browser – based software that determines creditworthiness of an obligor, assesses credit risk and computes the capital requirements to reduce credit risk. It informs banks whether to accept or reject applications for loan, providing output that enable users to understand the reason behind the decision.

The DNS approach uses both the internet and extranets to interconnect loan processes of various banks, CBN, Credit bureau and other stakeholders for effective information sharing. It enables the central bank to have first hand information on customer’s debt profile as well as monitor loan processing activities in banks. This prevents multiple lending to customers that lack the capacity to pay. We use biometric authentication technology to detect fake identity and GPS to detect fake collaterals in terms of landed property. The system also uses a robust database in eliminating erroneous classification of a customer’s loan as performing in one bank and doubtful or toxic in another bank. This is possible by retaining the loan transaction history of every customer in the database. This information helps banks to make intelligent loan decisions for individual borrowers. The system has built in creditworthiness and credit risk assessment modules.

The new CRMS makes use of theoretical and mathematical financial models to estimate credit risk using a credit risk scorecard. A complex study is carried out to determine a credit score for any business unit or individual involving various factors primarily extracted from the credit report, with payment history providing deep insight into the subject’s financial transactions. The credit risk report generated after the analysis is reviewed by the loan committee who then takes the final decision on whether to grant a loan or not. The decision is sent to a central loan database which is then accessed by the loan officer. This centralized process guards against erroneous approval of any kind of loan at the branch level. The officer merely has to inform the customer about the decision without succumbing to personal bias. The new system provides mechanism that heightens borrowers’ incentive to repay: every borrower knows that in case of default the personal reputation with all other potential lenders is ruined, effectively cutting the borrower off from credit or making it more expensive.

I am delighted to be here at the 3rd Heidelberg Laureate Forum and I enjoy meeting distinguished experts from both computer science and mathematics, as well as fellow young researchers form across the globe. I am particularly fascinated about the 2015 Hot Topic session, ‘Brave New Data World’. Most interestingly, Kristin Tolle of Microsoft Research lectured on “Using Big Data, Cloud Computing and Interoperability to save lives” This will really help me in enhancing my project on Bank Credit Risk Management in Nigeria.  According to Kristin, “Bringing together data generated by multiple sources can aid first responders to be more proactive than reactive and potentially enable direct notification to individuals who are at greater risk’.  This is exactly what my research project is about.

Image on courtesy of Ifeyinwa Ajah.
Image on courtesy of Ifeyinwa Ajah.

 

Ifeyinwa Ajah received Ph.D degree in computer science from the Ebonyi State University,  Nigeria, in 2013. Her thesis focused on using Digital Nervous System approach in modeling a viable Credit Risk Management System (CRMS) to drastically reduce credit risk in Banks. Ifeyinwa is currently an undergraduate project coordinator of the department of Computer Science at Ebonyi State University. She was a recipient of Ambassador for ACM in 2014, Award Certificate from Student Associate Scheme University of East London, United Kingdom, in 2005 and a Commendation Letter from the Cross River State NYSC for outstanding performance in the Service Year in February 2000. Her research interest is in the area of Software Engineering. Her research also extends to Internet Programming, Database development, and Computer Networks.

 

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