Understanding Credit Modeling and Willingness to Pay: A CedisPay Perspective

Understanding Credit Modeling and Willingness to Pay: A CedisPay Perspective

Introduction

Kwamina Duker, CEO of the Development Bank of Ghana (DBG), recently discussed on Joy FM Ghana a collaborative initiative between the DBG and the Central Bank of Ghana to build a "willingness to pay" credit model. While this project holds potential, several key considerations must be addressed, especially regarding data sharing, data refresh rates, and real-world application for accurate credit modeling.

This article explores the intricacies of credit modeling, the limitations of theoretical models without real-world data, and the challenges Ghana faces in credit scoring. We also draw on CedisPay's experience to demonstrate why analyzing real data and customer behavior is crucial for predicting willingness to pay accurately.

Executive Summary

Accurate credit models are essential for determining customer willingness to pay. While the Development Bank of Ghana (DBG), under Kwamina Duker, is undertaking a commendable initiative with the Central Bank, the effectiveness of any credit model depends on real-world data and practical lending experience.

At CedisPay, we believe that data and customer behavior analysis are the cornerstones of successful credit modeling. Since 2021, CedisPay has refined its approach through proprietary tools such as the Mobile Money Analyzer and Bank Statement Analyzer, achieving a 94% on-time payment rate. This success underscores the importance of combining data with practical experience in credit modeling.

Addressing Data Sharing and Protection Concerns

Kwamina Duker highlighted that banks, SMEs, and government agencies, including the GRA and DVLA, are key sources of data for building the credit model. However, concerns about data protection laws arise. For instance, SMEs cannot share customer data with the Development Bank without appropriate legal authorization. While credit bureaus are permitted to collect such data, it's unclear whether laws allow SMEs and banks to share customer information directly with the Development Bank.

Moreover, a critical issue is customer consent. Have customers agreed to this data sharing? Customer data is a valuable asset, and its use should be governed by strict data protection regulations to ensure ethical handling.

The Importance of Data Refresh Rates

Building a data set is only the first step. Customer behavior is fluid, and without regular updates, the data quickly becomes obsolete. The key question is: how frequently will this data be refreshed to reflect changes in customer behavior? A successful credit model requires continuous data updates to remain relevant and accurate. This is especially important as outdated data can lead to incorrect assumptions about customer payment behavior, reducing the effectiveness of the model.

A Critical Look at Credit Modeling and Willingness to Pay

The DBG's initiative follows a scientific process:

• Set goals: Develop a credit model that predicts customer payment behaviors.
• Test hypotheses: Create models based on assumptions, though with limited data from the Development Bank’s customer base.
• Evaluate results: Without actual lending data, the results remain theoretical.
• Adjust the model: True adjustments require testing with real-world lending data.

While the DBG’s experiment is a positive step forward, its limited lending experience and focus on corporate clients raise concerns about whether it can develop an effective model for individual credit behavior.

The Importance of Real Data and Experience

Credit models must be built on real-world data, not just theoretical assumptions. Understanding customer behavior—what triggers defaults, who is likely to repay, and what patterns suggest willingness to pay—is essential. Typically, a reliable model requires at least three years of real data to identify patterns accurately.

Challenges in Ghana's Credit Scoring Landscape

Several factors complicate credit scoring in Ghana:

1. Incomplete Credit Bureau Data: Not all financial institutions share customer data with credit bureaus, creating a fragmented view of borrower behavior.
2. Cash-Based Economy: A significant portion of transactions in Ghana remain cash-based, leaving little digital footprint for assessing creditworthiness.
3. Utility Data Gaps: Utility payments, a key indicator of financial responsibility, are not consistently reported to credit bureaus.
4. Incomplete Loan and Bill Records: While mobile money and bank statements provide some data, they often lack comprehensive loan and bill repayment records.

CedisPay: A Real-World Solution

Since its founding in 2021, CedisPay has focused on real customer data to address these challenges. Using proprietary tools like the Mobile Money Analyzer and Bank Statement Analyzer, CedisPay has gained unique insights into customer behavior, enabling us to build an accurate credit model. This data-driven approach has resulted in a 94% on-time payment rate, proving the value of real-world data in credit modeling.

CedisPay’s success is built on several factors:

• Proprietary Algorithms: Our machine-learning algorithms continually evolve to improve risk assessments.
• Strong Risk Management: A high on-time payment rate reflects our commitment to managing credit risk effectively.
• Customer-Centric Approach: Prioritizing the customer experience fosters loyalty and referrals.
• Strategic Partnerships: Collaborations with pension trustees, investment firms, and mobile money operators enhance our market reach.

The Future of Credit Modeling in Ghana

While experiments like the DBG’s are a step in the right direction, they must be followed by real-world applications. CedisPay's data-driven approach shows that accurate credit models come from analyzing customer behavior, not just theoretical frameworks.

Proposed Solutions for Ghana’s Credit Landscape

1. Open Banking and Data Partnerships: Open banking initiatives should encourage more comprehensive data sharing between financial institutions, credit bureaus, and utility companies.
2. Financial Education: Improved financial literacy programs will empower customers to make informed borrowing decisions.
3. Enhanced Credit Reporting: Incentivizing data sharing by utility companies and lenders would provide a fuller picture of customer behavior.
4. Alternative Credit Scoring Models: Incorporating non-traditional data sources such as mobile money transactions, bill payments, and rental histories can provide a more accurate view of creditworthiness.
5. Regulatory Oversight: Stronger regulations are necessary to ensure responsible lending practices across the industry.

Conclusion

To build effective credit models, financial institutions in Ghana must focus on gathering and analyzing real customer data. While the DBG’s experiment is a positive start, real-world application is critical to its success. CedisPay’s experience shows that accurate credit models, grounded in customer behavior and technological innovation, are essential to driving financial wellbeing and expanding access to credit in Ghana.

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