Benel D. Lagua l February 16, 2024 l Business World
Having spent most of my active career deep in the trenches of the lending side of financial institutions, I have seen the complex concerns of credit decision making. At the end of the day, credit analysis is a practice that combines elements of both art and science. On one hand is the science of obtaining and analyzing the facts of a loan request through objective data analysis and quantitative matrices. On the other, it requires the art of making subjective judgment about information and assessing the credibility of the borrower. Striking the right balance between these two is crucial.
Credit risk analysis is science in the sense of employing the methodology of natural science, which consists of procedures and practices of theorizing, testing and revising new information. It relies heavily on data-driven methodologies and financial ratios. Analysts scrutinize balance sheets, income statements and cash flow statements to gauge a borrower’s financial stability. Ratios such as debt-to-equity, current ratio and interest coverage provide insights on borrower’s abilities to meet its financial obligation. The science involves interpreting these numbers to assess risk.
Moreover, it delves into the statistical realm, utilizing predictive models to forecast a borrower’s future performance. Credit scoring, for example, assigns numerical values based on factors like payment history, outstanding debt and length of credit history. This objective evaluation streamlines the lending process and makes it more efficient.
However, Terence Yhip and Bijan Alagheband wrote a caveat. “A related fallacy is the belief that the mathematization of credit analysis makes it more accurate and objective. At best, mathematics and statistics are just tools, albeit indispensable, to detect, test and quantify patterns in a large data set… Used properly, they are powerful and useful tools for making informed investment decision. Mathematical models, no matter how sophisticated or carefully constructed, will still be a limited although important tool in credit analysis because much of the information inputs is qualitative.”
Credit analysis is thus also part art because it involves experience, practice, skill and imagination. Interpreting numbers requires understanding the industry dynamics, macroeconomic factors, and the qualitative aspects of a borrower’s business. This is illustrated in the assessment of management quality and strategy. Numbers tell part of the story, but understanding the people at the helm is equally critical. This human element introduces a level of uncertainty transcending pure science.
In practice, seasoned credit analysts often rely on intuition and experience in making nuanced judgments. This involves reading between the lines of financial statements, detecting subtle warning signs, and identifying both risks and opportunities not solely derived from the quantifications made. The analyst is like an artist painting a comprehensive picture, blending the colors of quantitative data and qualitative insights. The analyst’s experience and acumen contribute the shades and insights that make the overall judgment complete.
The warning here is not to equate subjectivity of the art with guesses and personal prejudices. The subjectivity discussed here is grounded in the observation of empirical data, extensive training, and seasoned experience. The overall understanding of the issues thus should improve. This is the reason why expert judgment is necessary to complement the number results.
Credit analysis is thus an intricate balance between science and art. Financial analysis and statistical modeling are combined with the intuition and interpretative skills of an artist. This fusion empowers the credit analysts to navigate the complexities of credit risk with finesse for the desired outcomes.
This blend of art and science is especially most critical when lending to small businesses which generally suffer in terms of the opacity of their financial statements. A strict by the book approach is difficult and may not result in the desired quality loan portfolio. In fact, many applications may be denied outright simply because of the absence of the right data set. How much art and science to apply depends on the problem in question. That is why banks must invest in the right people to bring alive these principles. That is what developmental lending is all about.
The reader who is a potential borrower must be keenly aware of the many intricacies of lending decisions. Awareness of what is on the other side of the bargaining table should help applicants package their approach to any borrowing transaction. It is crucial to read and comprehend the concerns of the other party to ensure transparency and avoid potential pitfalls.
The borrower should strive to provide accurate and comprehensive details to mitigate information gaps. After all, a good lending transaction should lead to a win-win arrangement where the initial problems of information asymmetry are resolved to the satisfaction of both parties. Mutual trust will lead to a long-lasting and beneficial financial partnership.
The views expressed herein are the author’s own and do not necessarily reflect the opinion of his office as well as FINEX.
*** Benel Dela Paz Lagua was previously EVP and chief development officer at the Development Bank of the Philippines. He is an active FINEX member and an advocate of risk-based lending for SMEs. Today, he is independent director in progressive banks and in some NGOs.