Mitigation of Risk and Crisis in Business using Machine Learning

As we all know, India is considered the world’s largest economy and the world’s largest marketplace for huge number of corporates all across the globe. Thanks to the open market system, we have the world’s largest number of thriving businesses both small scale as well as large scale. But as the number of businesses increases, the level of competition amongst them also increases and in the end, the survival of a business organization comes into focus. And to ensure higher survivability in a highly competitive economy, Risk mitigation becomes very important. Again to ensure this, Risk Analysis comes to play, which is the most crucial component in ensuring a safer path for the business organization.

Risk Analysis requires a lot of data as it involves making highly volatile predictions, which plays a very decisive role in choosing the company’s fate. Since humans are very prone to error as compared to computer intelligence, Data Science comes to the rescue. But why is it so? It’s because Data Science, unlike the traditional Analytic approach, uses Statistics, Machine Learning and Deep Learning algorithms like Regression, Classification, SVM, Decision Trees and Neural Networks to process a very huge set of data, and come up with a solid prediction that is bound to mimic the real-life consequences. How does it do that? How can we reduce the risk? Let us see how it is done.

What is Risk Analysis?

Risk Analysis is the process of understanding and identifying the key elements that can undermine the productivity and profit of a business organization or project undertakings by the organization. This enables the experts to focus on the key elements and take up strict measures so that stability is maintained. In order to identify the elements, the Data Analysts needs to focus on detailed information such as project plans, sales report, security measures and much more.

Using Risk Analysis

The following steps describe how the process of Risk Analysis is carried out.

Identify the Threat

Traditionally, the first step in Analysing risk is always about Identifying the threat involved. A threat can be anything depending on the situation the term is being used. For example, in a project, the threats involved are budget, time constraint or quality and performance issues. In finance, the threats involved are stock market fluctuations, geopolitical scenarios, business failure, etc.

Depending on the threat elements, which are also sometimes referred to as Dependent Variable, the factors being responsible are selected. These factors are also called ‘Independent Variable’. This process is required because it reduces the redundancy in the calculation of the threat factors, which ultimately decreases the accuracy in the model’s prediction. This entire process of picking selective data for calculating the end result i.e threat is called ‘Data Preprocessing’.

Selecting the Model

Every Machine Learning model needs to be trained and tested on the given data. After testing, a confusion matrix consisting of True Negative, True Positive, False Positive and False Negative can be derived. From this matrix, we can calculate Accuracy, Recall and F1 score of the model used in the given data, before predicting risks.

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Finding Accuracy

Accuracy = (True Positive + True Negative) /(True Positive + True Negative + False Positive + False Negative)

Finding Precision

Precision = True Positive/(True Positive + True Negative)

Finding Recall

Recall =True Positive / (True Positive + False Negative)

Find Accuracy

Accuracy = True Positive + True NegativeTrue Positive + True Negative + False Positive + False Negative

Find Precision

Precision = True Positive/(True Positive + True Negative)

Find Recall

Recall = (True Positive)/(True Positive + False Negative)

Higher is the F1 Score for a particular model over the given data of the business firm for Risk Analysis, higher is the preference of the model over the other Machine Learning or Statistical Model. In short, it is a system of nested hit and trial, which includes a certain amount of tweaking of the model to make it even more preferable, including changing the model to increase efficiency.

Reading from the Outcome

Once the training and testing of data and selecting the model is completed, the model is ready for use. Depending on the model used, a graphical report is mostly generated which makes the data interpretation much easier. Now, any new data if inserted into the model shall generate the required output stating the points of focus to reduce the impending risks involved. 

Financial Analysts can calculate the risks involved using many traditional methods and the computer too can calculate the risk using various Machine Learning Models. The only and the major point of difference is accuracy with automation and reduction of human efforts and errors. However, just like any other useful tool, predictions using ML and DL also have their own cons. A sudden change in the market atmosphere always requires certain tweaking to be done to a model to ensure higher accuracy and not let the model beat around the bush. This means, these sophisticated algorithms in spite of being robust in performance, shall always be needing efforts of data scientists and engineers to ensure the proper functioning of these models. Another con is that with a change in data size and pattern, a model needs to be redesigned which is a very heavy and costly process. Nonetheless, Machine Learning algorithms have been proven ten times more efficient and quicker than the traditional financial analyst and predicting the risks involved in a business is perhaps the best application of Machine Learning in the practical industry.

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