In 1918, four years after he was hired by the Du Pont Corporation to work in its treasury department, electrical engineer F. Donaldson Brown was given the task of untangling the finances of a company of which Du Pont had just purchased 23 percent of its stock.
(The company was General Motors.) Brown identified a mathematical relationship that existed between two commonly computed ratios, namely net profit margin and total asset/turnover to arrive at Return on Assets. This, the original Du Pont Analysis, remained for almost fifty years the key determinant of a company's profitability and efficiency.
Although a company's historical financial performance remains one of the basic measures for loan and risk evaluation, it is not a fully reliable indicator because a loan is to be repaid from future cash flow, not past performance and lenders recognise they are unlikely to make the right loan decision, or the right loan structure, by fixing future repayment ability only on the basis of past performance. Consequently, lenders have learned instead to rely on financial projections and models that incorporate both quantitative and qualitative data as the means for arriving at well-informed credit evaluation decisions and loan management benchmarks.
One of the major roles of a loan management process is to generate and supply information that facilitates decision making. Loan officers themselves are able to use information more effectively when they consider and weigh input from different sources. Not only is economic, management, and financial information necessary for effective credit analysis, but also knowing at what stage to use this information and what weights to apply on this information are of paramount importance.
Decision-making itself is a process of choosing among competing alternatives and this crucial managerial function is interlinked with planning. Credit Evaluation and Loan Monitoring managers cannot plan properly without making decisions, and to make sound decisions, such individuals need a model or models that help them to select among competing objectives and methods.
For lenders to the corporate sector, employment of the correct financial models becomes of heightened significance when economies face rapid growth or uncertainty; or when corporate customers undergo a significant change (such as rapid sales growth, new technology, major capital expenditures) or an operating-cycle change or new competitive threats. Consequently, financial projections and risk models have become an integral part of decision-making for loan management because, without application of the right tools, loan decisions can become even higher risk for the lender.
A projection is defined as an estimate of future performance based on assumptions that certain things will happen. It is not a prediction of what will happen. This is an important distinction because, quite often, loan managers become so immersed in details they accord finality to their calculations and tend to press alarm buttons when the actual figures do not correspond with their projections.
Of course, it is possible to come up with an educated estimate of future performance through logical or fact-based assumptions. Certain assumptions are always necessary when making projections and the accuracy of the assumptions drives the accuracy of the projections. A bad set of assumptions can lead to the wrong decision in the process of evaluating or managing the loan.
Using projections and evaluation models helps to establish a framework of standards and points of reference within which loan and relationship managers can operate with confidence, relative uniformity, flexibility and harmony. The last attribute is important because a 1989 study (Waymond Rodgers, Journal of Bank Cost & Management Accounting) demonstrated that loan managers normally perceive outcomes as gains and losses (or loan/do not loan), rather than as states of relationship or benefit, leading to internal conflict between separate business units of the lending organisation.
A number of experiments (by Waymond Rodgers & John Thomas) have also identified that, in general terms, credit evaluation and loan monitoring managers can be divided into two groups: those who prefer detailed information (or data-driven types) and those who prefer to look at the broader indicators (or conceptually-driven types).
The data-driven types tend to rely on large amounts of financial information, such as profitability and liquid assets before they begin their decision making process. The advantage in this style of loan processing is to add more structure and certainty in analysing financial statement information while the disadvantage is that less priority is given to management risk factors and forecasting.
Conceptually-thinking loan officers focus more on overall financial indicators, such as leverage information, and less on specific detailed quantitative information. The broader canvas of qualitative data lends weight to their projections and forecasting but the disadvantage is that, by placing less emphasis on the analysis of financial statements, they can overlook details that could be crucial for sound loan management of that portfolio.
Assisting credit evaluation and loan monitoring officers, whether data-driven or conceptually driven, to make better informed decisions has become one of the most important components of their training for two reasons.
First, there is much evidence that a lack of financial control is often a quick path to business failure - according to Dun & Bradstreet's Business Failure Records, "poor financial practices" ranks No 2 behind "economic conditions" as the principle cause of business failures. Second, inculcating sound knowledge of credit management and loan monitoring at the tactical level ensures better compliance with Basel II risk parameters at the strategic level.
SOME OF THE TOOLS USED INTERNATIONALLY FOR CREDIT EVALUATION AND LOAN MANAGEMENT ARE:
(a) The BCG matrix method, which has two dimensions, market share and market growth, and uses the product life cycle theory to determine what priorities should be given in the product portfolio of a business unit and states that, to ensure long-term value creation, a company should have a portfolio of products that contains both high-growth products in need of cash inputs and low-growth products that generate a lot of cash.
(b) The McKinsey/GE Matrix, which is more sophisticated than the BCG Matrix in that (i) Market (Industry) Attractiveness replaces market growth as the dimension of industry attractiveness and (ii) Competitive Strength replaces market share as the dimension by which the competitive position of each strategic business unit is assessed.
(c) PESTLE Analysis, which uses specific checklists that combine Political, Economic, Social, Technological, Legal and Environmental factors to assess the competitiveness and viability of a business or organisational unit strategic plan.
(d) Porter's 5 Forces analysis, which is a framework for industry analysis and business strategy that includes (i) three forces from 'horizontal' competition: threat of substitute products, the threat of established rivals, and the threat of new entrants; and (ii) two forces from 'vertical' competition: the bargaining power of suppliers, bargaining power of customers.
When combined with quantitative data produced from cash flow analysis, working capital efficiency indicators, liquidity, profitability and debt servicing capacity indicators, the qualitative tools identified above and other non-financial sources of information can produce a well-reasoned and logical risk evaluation framework that can be used also for effective loan management.
(The writer is a management consultant ([email protected])
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