Data science has become a significantly more influential practice over the years, and organizations of all kinds are now riding the data science wave. Specifically, more companies are using data to identify customer trends and preferences, make market predictions, improve internal efficiency, and up their marketing efforts –– all in the hopes of generating more business. Today, however, data science can be used for far more than these standard practices. In this piece, we’re going to look at how to use data science in risk management and the role it plays when it comes to the analysis of organizational risk.
Generally speaking, a lot of businesses today employ or contract data analysts and are thus equipped to implement data-driven solutions to a variety of problems. This is how a lot of companies go about some of the aforementioned data-related tasks relating to everyday operations and growth. Beyond this, however, more businesses today are also working with actuaries. A professional actuary helps businesses to use data specifically for managing risk. They delve into the collected information and work with business leaders and stakeholders regarding important decisions that will protect the company against potential setbacks and inefficiencies.
Here’s a closer look at how data science in business is used to analyze and manage organizational risk.
Data science in Risk and Fraud Detection
Dealing with investments of any kind always carries some risk. For businesses, it almost always boils down to financial risk. And in order to mitigate risks like these, businesses have turned to data science.
Insurance companies and financial entities such as banks segment customers based on the potential financial risk they pose. Smaller businesses use this information to analyze patterns in spending and make sure that there aren’t any unauthorized expenditures that may otherwise fly under the radar (think small monthly subscriptions that don’t exist, from which a hacker receives a small amount each month from each company that doesn’t notice the monthly charge).
Businesses generally have specific budgets allocated for advertising. This is a good thing in general, and having a strong marketing operation is essential. However, when data science is not used to back the process, there is real risk of money, time, and substantial effort all being directed toward inefficient processes. On the other hand, a strong data operation behind your marketing effort will ensure that you identify target audiences, learn how they want to be approached, and consistently adapt according to both performance and need. This way there is very little risk of wasted resources.
A business is prone to having unexpected problems that burn through money pretty quickly: unforeseen expenditures, clients that pull out at the last minute, or even a global pandemic that causes us to close up shop for months at a time. These are problems that require proactive and efficient responses. Data science can help in this regard by giving decision-makers as much information about whatever problem has come up as quickly as possible. This doesn’t eliminate consequences, but it does remove the risk of companies being entirely blindsided by misfortune. Coupled with quick thinking, sound data analysis can potentially save a company astronomical sums of money (not to mention time and stress) by enabling rapid assessment of unexpected challenges.
Data Science in risk management: Cutting Costs
We’ve mentioned the possibilities of surprise expenses and fraud, but the concept of cutting expenses in general deserves its own section. Simply put, every organization that is not practicing thorough data analysis is at risk of unnecessary overspending. Analysts and actuaries assessing data can help to pinpoint exactly where a business might be spending too much, as well as suggest alternative avenues for finances.
Sometimes the benefit may come in the form of something small and seemingly insignificant, such as consolidating some monthly subscription-based services into a more comprehensive software. Other changes can be bigger. Migrating data to a cloud-based service, for instance, may save a great deal on hosting costs; some businesses are also seeking significant savings by downsizing offices (or eliminating them altogether).
As you can see, using data science in risk management helps immensely when it comes to helping companies mitigate or even eliminate financial and organizational risks. Just make sure to hire the right talent and approach analysis the right way. Data that is handled poorly or analyzed incorrectly cannot provide the same benefits.
Article contributed by Lane Simon