All businesses have some level of risk. Whether it be customers who do not pay their credit or customers who stop buying from them.

Can risk be measured? Risk of course, has a level of uncertainty attached to it, and may seem difficult to measure.  Many people find it difficult to believe that Risk can be measured.

Risk can be measured. We are fortunate, in today’s times, with advanced technology and historical data, we can use data to recognize historical cases that put businesses at risk. Exploratory Data Analysis and techniques such as Machine Learning can help businesses to identify patterns in the data that are associated with Risk. Using statistical significance testing we are able to identify which factors scientifically contribute to the risk under study.

Of course, not all cases will be identified as ‘Risk’ but some cases will have a higher probability of risk than other cases. This means that a business would need to have a strategy in place to manage the different levels of risk. Some “Risks” are “critical” and need urgent response and action, while other “Risk” are important but less urgent.

In the case where your business provides credit to customers, it is important to identify which customers are highly not likely to pay their loan and the size of their loan. As some customers will have a loan size of $1000 with a probability of 90% chance of not paying back the loan. Another customer may have a loan size of $10 Million with a probability of 60% chance of not paying back the loan. Businesses often forget to look at the loan size and only focus on the probability of someone being a credit risk, when in fact, the higher loan amount is most times more critical that it be paid back than the smaller loan sizes. Businesses need to have different risk minimization strategies based on “Size of Loan” and “Probability of Credit Risk”.

When Businesses have a Strategy for Risk Minimization, they can integrate their credit data analysis results with their risk minimization strategy and automate it into a complete decision support system for their credit officers.

Fortunately, with the large volumes of data, businesses can use machine learning techniques to crunch customer transactional, demographic, social media, and lifestyle data, etc to score customers faster and smarter giving businesses the enhanced customer experience and competitive edge in decision making.