An appraisal of digital credit scoring systems in corporate banking: A case study of Wema Bank, Lagos
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Background of the Study
Digital credit scoring systems represent a significant advancement in corporate banking, enabling banks to assess borrower creditworthiness more efficiently and accurately. Wema Bank in Lagos has adopted digital credit scoring models that utilize big data, machine learning, and advanced analytics to evaluate corporate loan applications. These systems allow for the rapid processing of vast amounts of financial and non-financial data, thereby facilitating more objective and transparent credit decisions. By leveraging alternative data sources and predictive algorithms, digital credit scoring systems enhance the accuracy of risk assessments, reduce the likelihood of non-performing loans, and optimize loan pricing. This innovative approach not only improves operational efficiency but also strengthens customer confidence by providing consistent and unbiased credit evaluations. However, the implementation of digital credit scoring systems is accompanied by challenges, including data integration issues, model calibration, and regulatory compliance. This study appraises the effectiveness of digital credit scoring systems at Wema Bank, examining their impact on credit risk management and overall corporate banking performance (Oluwaseun, 2023; Adenola, 2024).
Statement of the Problem
Wema Bank faces several challenges in fully realizing the benefits of digital credit scoring systems. Integration with legacy systems and ensuring data quality are significant obstacles that can lead to inaccuracies in credit assessments. The complexity of calibrating machine learning models to reflect rapidly changing market conditions and borrower behaviors further complicates the credit evaluation process. Moreover, regulatory uncertainties regarding the use of alternative data in credit scoring present additional challenges. High implementation costs and the need for continuous system upgrades, combined with a shortage of skilled personnel to interpret digital credit scores, further limit the system’s effectiveness. These issues contribute to potential misjudgments in credit risk evaluation, affecting loan performance and overall profitability in corporate banking (Oluwaseun, 2023; Adenola, 2024; Chukwu, 2025).
Objectives of the Study
- To evaluate the effectiveness of digital credit scoring systems at Wema Bank.
- To identify challenges related to data integration, model calibration, and regulatory compliance.
- To recommend strategies for optimizing digital credit scoring for improved risk management.
Research Questions
- How effective are digital credit scoring systems in evaluating corporate creditworthiness at Wema Bank?
- What challenges affect the accuracy and integration of digital credit scoring?
- What measures can enhance the performance of digital credit scoring systems?
Research Hypotheses
- H₁: Digital credit scoring systems significantly improve the accuracy of credit risk assessments.
- H₂: Integration and calibration challenges negatively affect the performance of digital credit scoring.
- H₃: Enhanced regulatory alignment and training improve digital credit scoring outcomes.
Scope and Limitations of the Study
The study focuses on Wema Bank’s corporate banking division in Lagos, analyzing digital credit scoring systems over recent fiscal periods. Limitations include evolving regulatory standards and potential data access constraints.
Definitions of Terms
- Digital Credit Scoring Systems: Automated systems that use data analytics to evaluate borrower creditworthiness.
- Corporate Banking: Banking services provided to large business entities.
- Predictive Analytics: Techniques that analyze current and historical data to forecast future outcomes.
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