The Unseen Power of Non-Financial Data

In the shifting sands of global markets, traditional financial data no longer cuts it alone for assessing credit risk. It's akin to using a sundial in the age of smartwatches. The action now lies in harnessing non-financial data—from ESG (Environmental, Social, and Governance) scores to real-time payment behaviours and alternative economic indicators. These metrics, once peripheral, have become critical to gaining a deep, actionable understanding of creditworthiness. This article, inspired by insights from Baker Ing 2022 webinar with subject-matter experts, peels back the layers on why non-financial data is changing credit risk assessment.

The complexity and interconnectivity of global markets now demand a nuanced approach to credit risk. Traditional metrics like financial statements and past credit performance, while still relevant, often lag too far behind to effectively respond to rapid market shifts. Non-financial data steps into this breach, offering a dynamic and rich view of an entity’s risk profile.

Recent studies highlight the importance of integrating non-financial data with traditional financial metrics to improve credit risk assessment models. Together, these studies underscore the practical utility and enhanced accuracy achieved by integrating financial and non-financial data in credit risk models.

Baker Ing's webinar on these matters served as a microcosm of expert opinion on this topic. Maria Anselmi emphasised the growing significance of ESG scores in evaluating long-term sustainability and ethical governance. Shaun Rees highlighted how real-time payment behaviours could be a window into financial health. And, Markus Kuger pointed out the value of alternative economic indicators, especially in times of uncertainty.

Non-financial data opens new vistas for credit risk assessment, marrying the old with the new to forge a path towards more informed and effective decision-making processes.

The Emerging Power of Non-Financial Data

The use of non-financial data in credit risk assessment is no longer a fringe idea; it's rapidly becoming mainstream. This shift is underscored by research such as that conducted by Rasa Kanapickien? and her team, who developed an innovative enterprise trade credit risk assessment (ETCRA) model tailored for small and micro-enterprises in Lithuania. Their findings were striking: while models that rely solely on financial ratios are effective, incorporating non-financial variables significantly enhances their performance. This not only validates the importance of traditional financial data but also highlights the indispensable value of non-financial data in providing a more nuanced and comprehensive assessment.

Kanapickien?’s research sheds light on the limitations of traditional financial metrics like profitability, liquidity, solvency, and activity ratios. These metrics, while essential, often provide an incomplete picture. By weaving in non-financial data—such as payment behaviors and qualitative indicators—the assessment model can offer a richer, more detailed view of an enterprise's financial health and risk profile. This holistic approach enables credit risk professionals to make more informed decisions, particularly for small and micro-enterprises, where financial data alone might not capture the full spectrum of creditworthiness.

In essence, integrating non-financial data broadens the scope of credit risk assessment, ensuring that it is not only more accurate but also more reflective of the complex realities businesses face today. This marks a significant step forward in the evolution of credit risk management, paving the way for more reliable and insightful evaluations.?

ESG Metrics: The New Frontier

Environmental, Social, and Governance (ESG) metrics have moved to the forefront of non-financial data, offering crucial insights into a company's long-term sustainability and ethical practices. The work of E. Altman and his colleagues underscores the transformative potential of these metrics. Their research demonstrates that incorporating qualitative non-financial information—such as legal actions, company filings, audit reports, and firm-specific characteristics—significantly boosts the predictive accuracy of credit risk models for small and medium-sized enterprises (SMEs).

Altman's study revealed that companies with a history of legal issues or negative audit reports are more prone to default. This highlights the critical importance of such qualitative data in risk assessment. Additionally, firm-specific characteristics, like governance practices and board diversity, showed a strong correlation with creditworthiness. These findings underscore the substantial value of comprehensive ESG metrics in evaluating a company's risk profile, providing deeper insights into factors that drive long-term sustainability and ethical governance.

• Environmental Metrics: These evaluate a company’s impact on the planet, including carbon emissions, energy efficiency, and waste management. Companies excelling in environmental performance typically adopt sustainable practices, invest in renewable energy, and commit to reducing their carbon footprint. Such efforts not only benefit the environment but also bolster the company’s reputation and mitigate regulatory risks, making them more attractive to investors and lenders.
• Social Metrics: These focus on a company's relationships with its employees, customers, and communities. Factors like labor practices, diversity and inclusion, and community engagement are crucial indicators of social responsibility. High performance in these areas can lead to increased employee satisfaction and retention, as well as greater customer loyalty—all of which positively influence the company’s creditworthiness.
• Governance Metrics: These assess the quality and effectiveness of a company’s leadership, including board diversity, executive compensation, and ethical business practices. Strong governance structures are indicative of sound decision-making and effective risk management, which are essential for long-term sustainability. Companies with robust governance practices are better equipped to handle crises and maintain investor confidence.

In essence, integrating ESG metrics into credit risk assessments allows for a more comprehensive evaluation of a company's risk profile. It provides credit risk professionals with a clearer view of the factors that influence a company's long-term sustainability and ethical standing, ultimately leading to more informed and reliable credit risk evaluations.

Real-Time Payment Behaviours: The Pulse of Financial Health

Payment behaviours are the heartbeat of a company’s financial health, providing real-time insights into how promptly debts are settled. This data is indispensable for uncovering underlying financial stability or distress. Sean Rees underscores the critical role of real-time payment behaviours in assessing credit risk. Access to such real-time data allows for continuous monitoring and timely adjustments to credit assessments, offering early warnings of potential financial instability.

Rees’s perspective is supported by the research of Roozmehr Safi and colleagues, who demonstrate that non-financial web data can predict the creditworthiness of businesses, especially when reliable financial data is scarce. This is particularly relevant for online businesses and SMEs, which might lack extensive financial histories but display valuable non-financial indicators of creditworthiness.

Safi’s study identified several non-financial factors from B2B exchanges that significantly influence credit risk, such as customer reviews, website traffic, and social media presence. These factors provide real-time insights into a company's market position, customer satisfaction, and operational performance. By incorporating such non-financial data into credit risk models, lenders can gain a more accurate and timely understanding of a company's financial health and potential risks.

Combining both internal payment data—like accounts payable and receivable records—with external payment experiences reported by suppliers and other creditors offers a comprehensive view of a company's payment trends. This holistic approach enables credit risk professionals to identify patterns and deviations that may indicate financial health or distress, facilitating proactive risk management. For example, a sudden surge in late payments or requests for extended payment terms could signal liquidity issues, prompting a reevaluation of the company's creditworthiness.

Integrating real-time payment behaviour data into credit risk models ensures that assessments are not only more accurate but also more reflective of the current financial realities faced by businesses. This dynamic approach to credit risk assessment allows for a more responsive and informed decision-making process, enhancing the ability to manage and mitigate potential risks effectively.?

Integrating Non-Financial Data: A Strategic Approach

Integrating non-financial data into credit risk assessments requires a strategic approach. Companies need to establish robust data collection and analysis frameworks to leverage the full potential of these diverse data sources. Key steps include data integration and quality control, continuous monitoring and real-time updates, and collaborative data sharing.

• Validation of Data Sources: Ensuring data integrity starts with validating the credibility and reliability of multiple data sources. Cross-referencing data points from various providers is essential to maintain consistency.
• Standardisation of Data Formats: Data often comes in various formats, which can be a hurdle for seamless integration and comparison. Standardising these formats into a common framework is necessary. Advanced analytics and machine learning algorithms, such as decision trees, can significantly enhance predictive accuracy when integrating diverse data sources. Khemakhem et al. highlighted that decision trees offer higher predictive accuracy for credit risk assessment than artificial neural networks, especially when the data is balanced. This makes them an invaluable tool in the data integration process.
• Advanced Analytics for Discrepancy Identification: Advanced analytics and machine learning algorithms play a crucial role in identifying and rectifying discrepancies within data. These technologies can detect anomalies and outliers, ensuring that only high-quality data is used in risk assessments. Employing such techniques enhances the reliability of integrated data and improves the overall accuracy of credit risk models.

By adopting these steps, amongst others, we can effectively integrate non-financial data into credit risk assessments, leading to more comprehensive and accurate evaluations. This approach not only improves the reliability of credit risk models but also provides deeper insights into the factors that influence creditworthiness, enabling more informed and proactive decision-making.

The Imperative of Real-Time Monitoring

Given the dynamic nature of credit risk, continuous monitoring and real-time updates are essential for maintaining an accurate risk profile. This involves several key strategies:

• Automated Tracking Systems: Implementing automated systems to continuously track changes in key indicators—such as payment behaviors, ESG scores, and alternative economic metrics—is crucial. These systems provide real-time alerts and updates, allowing credit risk professionals to respond swiftly to emerging risks. Laura Miliūnait? et al.'s research supports the implementation of AI-driven systems for continuous monitoring and real-time updates. AI methods can efficiently process large volumes of non-financial data, providing timely insights and enabling dynamic, responsive credit risk management.
• Proactive Risk Management: Real-time monitoring allows companies to proactively adjust their credit risk assessments. This might involve tightening credit terms for high-risk clients or extending more favorable terms to those demonstrating strong financial health. AI-driven systems can help identify these trends early, enabling proactive adjustments and mitigating potential risks before they escalate. This proactive approach ensures that credit risk assessments remain relevant and accurate, adapting to the latest available data.
• Capitalising on Opportunities: Continuous monitoring also helps companies identify and capitalise on emerging opportunities. For instance, a sudden improvement in a client’s ESG score or payment behaviour might prompt a review of their credit terms, potentially leading to enhanced business relationships. AI systems facilitate this by providing real-time data analysis and identifying patterns that indicate positive changes in a client's risk profile. This not only helps in mitigating risks but also in leveraging positive developments to foster stronger, more beneficial relationships with clients.

By integrating these methods, we can ensure that credit risk is not only accurate but also dynamic and responsive to changing financial circumstances.

Expanding Horizons: The Power of Collaborative Data Sharing

Collaborative data sharing rounds out the strategy by further broadening the scope and depth of available information, providing a more comprehensive view of credit risk. Key aspects of this collaboration include:

• Establishing Data-Sharing Agreements: Forming agreements with external partners—such as financial institutions, suppliers, and data providers—is vital for sharing relevant data. These agreements should clearly outline the terms and conditions of data sharing, ensuring compliance with privacy and confidentiality standards. Studies by E. Altman and colleagues advocate for collaborative approaches in data sharing to enhance credit risk models. They highlight the benefits of accessing a wider range of data sources, which can improve the accuracy and robustness of credit risk assessments.
• Participating in Industry Consortia: Joining industry consortia, like Let's Talk Credit Ltd and data-sharing networks can provide access to a broader range of data sources. These consortia often aggregate data from multiple participants, offering valuable insights and benchmarks that individual companies might not access independently. Collaborative data sharing through consortia leads to a more comprehensive understanding of industry trends and sector-specific risks, thereby enhancing the overall effectiveness of credit risk assessments.
• Leveraging External Insights: Accessing data from external partners enhances the understanding of market trends and sector-specific risks. This collaborative approach enables companies to benchmark their performance against industry standards and gain insights into best practices. By leveraging external insights, companies can improve their credit risk models and make more informed decisions based on a wider array of data points.

Collaborative data sharing unlocks a wealth of information that individual companies can't gather on their own. This expanded data pool enriches risk assessments, making them more accurate and comprehensive. It also fosters a culture of shared knowledge and continuous improvement, where companies can learn from each other and adapt to emerging trends and risks more effectively.

Conclusion

The integration of non-financial data into credit risk assessment represents a shift in how companies evaluate creditworthiness. By leveraging ESG scores, real-time payment behaviours, and alternative economic indicators, businesses can gain a more comprehensive and nuanced understanding of risk. This holistic approach not only improves the accuracy of credit assessments but also promotes sustainable and ethical business practices.

As the economic landscape continues to evolve, and quicken, the importance of non-financial data will only grow. Traditional financial metrics, while still valuable, are no longer sufficient on their own to provide a full picture of credit risk. The rapid pace of change in today’s global economy demands more immediate and detailed insights, which non-financial data can provide. Companies that proactively integrate these data sources into their risk management strategies will be better positioned to navigate uncertainty and drive long-term success.

For further information and detailed insights, readers are encouraged to connect with the experts - Shaun Rees and Markus Kuger - and access additional resources through Baker Ring's Global Outlook section where the original slides can be found, as well as the full webinar on YouTube.

To support this paradigm shift, we've recently introduced CreditHubs. Designed to transform dense market data into clear, actionable insights, CreditHubs equips professionals with the tools they need to stay ahead. Offering free access, it ensures that credit professionals can seamlessly adapt to regulatory changes and economic shifts across markets. As we continue to expand CreditHubs, it will offer a wide range of regional and industry-specific hubs, providing a robust, go-to resource for trade credit insight. You can check out the first CreditHub here (sign-up for updates at the bottom of the page): https://bakering.global/credithub-australia/

References

• Kanapickien?, R., et al. (2019). "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania."
• Safi, R., et al. (2014). "Using non-Financial Data to Assess the creditworthiness of Businesses in Online Trade."
• Altman, E., et al. (2008). "The Value of Non-Financial Information in SME Risk Management."
• Miliūnait?, L., et al. (2023). "The Role of Artificial Intelligence, Financial and Non-Financial Data in Credit Risk Prediction: Literature Review."
• Khemakhem, S., et al. (2018). "Predicting credit risk on the basis of financial and non-financial variables and data mining."
• Baker Ing International (2022). "Executive Recap: Leveraging Non-Financial Data". https://bakering.global/product/executive-recap-leveraging-non-financial-data/
• Baker Ing International (2022). "Webinar: Leveraging Non-Financial Data". https://bakering.global/product/webinar-non-financial-data/
• Baker Ing International (2022). "Webinar: How Data is Changing Credit – The Impact of Non-Financial Data". https://www.youtube.com/watch?v=um7KEVW0-tc

?