I still remember during a find credit choice, I made a decision about the direct role of predictive analytics. One of our loan applications under review was from a freshly established business that had been in operation for a period of two and a half years. First, the fact that company balance sheet and credit history showed that gambling with moderate risk. Although we have got predictive analytics by our side which indicates that the business is reconsidering the growth potential of the business and the risk of default. Through assessing dozens of different variables, such as industry trends, market climate, and the company's known historical growth curve, the model we developed predicted that the business would most likely undergo an exponential growth rate within another two fiscal years. Moreover, the models evidenced the reduced default risk that the company faced compared to its initial perception. This is attributed to its cash flow management, which was consistently in check, as well as the demand for the products, which was anticipated to be on the rise. Thanks to that knowledge, as we approached the venue, we were able to determine the right solution. We granted the facility at slightly better than standard conditions – such favor was extended due to the insights derived from the predictive analytics. This latter-to-be decision was equally beneficial for the company as well as for our bank as the business fulfilled the expected growth and expensively repaid its obligations for provided credit. This experience showed me the propitious side of predictive analytics in the credit process, which allows us to consider deserving borrowers that may be bypassed otherwise by a more limited and drawn-back risk assessment.