Use Of Data Science In Risk Management –A Synopsis!
Risk management is a practice being followed by almost all profit-oriented organizations in order to sustainably maximize return on investment (ROI). While in previous decades, paper based risk assessment was performed it was a tedious process and only a few opted for it. But after the software boom in the 1990s and beyond, new modeling programs and services have emerged to offer a significantly less tedious way to assess and manage risks.
The emergence of the concept of big data, i.e., large amounts of uncategorized data, in the last half decade has led to organizations desiring to utilize the big data for their self-growth. Data science is a discipline aimed at providing tools to analyze the big data and help organizations achieve higher ROI.
Data science has been used in many sectors. In the field of risk management, it is advantageous to employ it in many sub-fields. For instance, in the financial sector, assessing risk is extremely important in order for an organization to be profitable. Large amounts of data are generated in the financial sector through bank transactions, customer behavior in spending and saving, manufacturing trends, market price fluctuations, etc. Data science can assist in analyzing these big data sets and make meaningful inferences that can, in turn, enable financial sector organizations to make profitable ROIs.
In the insurance sector, large amounts of data are flooding the digital space and insurance companies need to comprehensively analyze such data for risks. Data science and analytics can help in organizing information gathered and help insurers provide better services to their clients.
In transportation, data science can prove beneficial to the safety of passengers. Telematics, which is a discipline of engineering that relates to transmitting digital information over large distances, enables the collection of a vehicle’s data like speed, fuel level, engine functioning, brake system, etc. This information can then be used along with GPS to determine how safe a vehicle is.
In the robotics industry or in industries that can potentially use robotic exoskeletons, data science tools can help analyze the information gathered by such exoskeletons worn by workers performing a dangerous process. It can be used to ensure the safety of the workers by constantly monitoring worker health parameters, working conditions like temperature, pressure, any noxious gasses, etc.
Both the above instances in robotics and transportation utilize machine learning on a large scale. Machine learning tools process multiple data sets at a time and produce multiple iterations and outcomes, each with different risk assessments.
Weather forecasting also is by definition a tool used to avoid or reduce exposure to harmful natural events. Weather models already utilize software programming on a large scale and this is considered to be an early adoption of data science before such a word even came into existence. Now more advanced modeling software is being involved in this field.
Similarly, risk management in public healthcare systems also involves large populations and equally large number of factors like food, water quality, air pollution, vector prevalence, sanitary conditions, etc. Utilizing data science tools to predict model outcomes enables government and healthcare providers to assess potential risks and ways to reduce them.
Thus, wherever there is an abundance of data and complexity, data science is a very useful tool to make sense of the data generated.
REFERENCES
https://www.safetynational.com/conferencechronicles/data-science-risk-management/
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