A few days ago I met a fellow after doing laps in the pool, ala Michael Phelps! (I’d like to think we know as much about construction as Michael knows about swimming.) We began talking and sure enough he was the proud owner of a thriving construction company… but it wasn’t always that way. In fact, he shared with me the trials and tribulations he had experienced in the construction business. We laughed about the scrutiny his work received when doing custom mansions for the very wealthy and how the Irish side of him loves whiskey. And then we talked more seriously about a dramatic change in his career. You see, this strong willed Irishman was a victim of a key risk factor: Mismanagement of cash flow.

He shared with me how cash flow had put him out of the construction business. His claim to fame was the installation of high end custom wood work in plush offices and homes. As he became bigger, he just was not prepared for the cash flow crunch that he would experience. He shared with me his frustrations at getting paid from General Contractors who always had an excuse for not paying, and he used a few choice words. It was obvious that he had experienced what has put so many companies out of business, a cash shortage. He indicated he was making good money, and I believe that because custom millwork brings a good margin and there is not a lot of competition for highly specialized woodwork. He had different types of wood shipped in from all over the world and he shared with me how even though he was profitable, when he pursued the bigger work, cash flow became too much of an issue and he was forced to reinvent himself. This certainly is a familiar story.

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Technorati Tags: Accounts receivable, Bank Credit, Business, Cash flow, Construction, Construction Risk Managment, General contractor, risk factor, Risk management, Surety Credit, Trade Contractor

This title seems especially appropriate following the recent Beijing Olympics. But today we are not talking about Chinese culture, we are talking about qualitative data and quantitative data, risk data and financial data, causes for success and causes for failure. What do these have in common? As the Chinese definition goes, they are two complimentary qualities that, when put together, form the whole.

At the end of the day, business is about achieving profitability, which is defined as the ability of an enterprise to generate revenues in excess of the costs incurred to produce those revenues and is often measured by a rate of profit or rate of return on investment. Credit underwriters also seek to achieve profitability, and that means avoiding large, unforeseen losses. To maximize profitability, underwriters need to find the optimal balance between premiums charged and risk present.

Unfortunately, as discussed in , underwriters are often working with insufficient, inadequate, or obsolete data so measuring the “risk present” becomes quite a tall order, and many times involves outright guessing. They have no way of knowing where the applicant lies in the . Fortunately, with the advent of a standardized mean to collect and analyze qualitative data, most of these underwriting deficiencies can be overcome. In this post, we’ll discuss how qualitative and quantitative data fit together to form a complete picture of an applicant during the credit underwriting process.

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Technorati Tags: credit underwriting, Enterprise Risk Management, ERM, failure risk, guarantee underwriting, maximum profitability, profitability risk, qualitative data, quantitative data, Risk analysis, risk data, risk premium

Credit underwriting decisions are a cornerstone of any economy. Made wisely, they can assist entrepreneurship, promote economic growth, and generally ensure that capital is allocated to its highest and best use. On the other hand, poor credit underwriting decisions can negatively impact an industry or the economy as a whole.  Recent troubles in the U.S. economy are directly tied to the poor credit decisions of lenders to support prospective home owners who had little money and provided little information about their financial strength in an over-inflated housing environment. Recent failures of banks such as IndyMac are partly tied to poor credit underwriting decisions and over-leveraging.  The failure of banks to consider the full range of construction risk is leaving many banks high and dry due to the recent spate of construction business failures, with many more to come. The five consecutive years of recent losses in the surety industry was directly related to poor credit underwriting decisions. With all of these losses you have to wonder what is going wrong. The answer is twofold: an unusually high tolerance for risk and credit decisions based upon insufficient data.

Creditors

In the case of mortgages that went bad, because loans could be packaged and resold, an anything goes atmosphere developed and many risk management practices were thrown out the window. Many loans were provided based on simple applications that provided minimal financial information. The fallout of this lending environment is showcased on Mortgage Lender Implode-o-Meter. In the case of IndyMac, a large portfolio of non-performing Alt-A loans, sometimes called liar loans, and risky construction and land development lending, left the bank with very little cushion in a falling housing market. Other banks impacted by losses only relied on financial data, failing to consider all the risks of lending to high risk industries such as construction and auto dealerships.

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Technorati Tags: boom and bust, Credit, credit decisions, credit underwriting, Economy of the United States, IndyMac Bank, qualitative data, quantitative data, Risk analysis, risk control, Risk management, Surety