Predictive Analysis

Can Businesses Measure their Risks? Can these Risks be Managed?



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.

Should you be Learning more about Machine Learning Algorithms?


More and more businesses are switching from reacting to situations to anticipating them. How do they do this?

In order to anticipate or predict a situation, an organization must have a business question in mind. Questions such as:

Is this a fraudulent transaction or not?

Will the stock price in the next week go up or not?

Is this particular person a credit risk or not?

Will this customer buy item ‘A’ or not?

How many units will be sold next week?

What will the stock price be tomorrow? etc

The best way to make business decisions is to analyse the relevant historical data and to identify the patterns in the data. To identify the relevant data, it is important that the analyst speaks with the domain experts to help them identify the relevant data. Further, it is not only the analysis of the historical data (being reactive always) that can help answer the above business questions. It is also important that businesses use predictive data (being proactive and sometimes preventing negative activity) to help answer the above questions.

Machine Learning is a method for data analysis, in particular, Machine Learning Algorithms focus on using the relevant data inputs to make Predictions for the business or Classify the business products, services or customers.

Machine Learning is an algorithm that explores the data for patterns and identifies the key data (factors) that will help the business to predict/classify its target outcome of interest (like the above business questions). Machine Learning uses statistical techniques (such as the logistic regression, neural networks, decision tress, etc) and computer programming languages (such as Python, R, SAS, Matlab, etc) to automate the predictive/classification models using algorithms that iteratively learn from the data and find the hidden insights that humans would not usually have known about. These hidden insights are like ‘gold for the business’ as they usually improve the business predictions/classifications without being explicitly programmed where to look. The Machine Learning Algorithms further improve when they are regularly updated with new data and knowledge.

For Machine Learning Algorithms to be effective, it is important for businesses to be able to capture the relevant, good quality data in real time or almost real time and to allow the right people in the business to apply the predictions/classifications timely and thereby gain advantage over their competitors.

Are you interested in learning more about machine learning algorithms and how your business can improve its sales/predictions and become ore efficient? Contact me at