Risk is an inherent part of any business, and the onus is on the decision-makers to manage that risk. Furthermore, while decision-making has become more accurate with the help of various tools and methods, the vast volume of data in today's world is difficult to manage.
This is where predictive analytics for risk management steps in.
Whether your business is large or small, there will be various potential risks. For example, if you are a software organization, you might encounter unexpected bugs in your solution or fail to meet a due date. A risk management process will be needed to mitigate these.
Financial services and banking were among the earliest industries to start utilizing predictive analytics, which is a type of data analytics and an emerging branch of advanced analytics. Today, it is used by healthcare, eCommerce, governments, and many other industries and institutions.
It's only uphill from here. The market for predictive analytics is expected to reach 12.41 billion USD by 2022.
Through data mining, AI, deep-learning algorithms, machine learning and data modeling, you can use predictive analytics for risk management of your business. But what are the kinds of risks you will be encountering?
There are many types of business risks, and you might get a different answer as to what they are from each person you ask.
This is a broad definition and may involve extended credit, interest rate fluctuations, or the debt load of your own business. All these may lead to unexpected losses of money or investment, or harm to cash flow. Financial risk management is one of the things your company should be heavily focusing on.
A real-life example of financial risk would be the closure of Toys "R" Us due to capital structures and debt-heavy buyouts.
This includes unexpected occurrences such as natural disasters, damage to your building, a server outage, or even just an employee blunder. It may even be considered a sub-set of financial risk. Operational risk can be external, internal, or a combination of the two, and lead to loss of business continuity and money.
A real-life example of an operational risk would be HSBC's failure to prevent money laundering in 2012. This resulted in them incurring a fine of 1.9 billion USD from the US Department of Justice.
Predictive analytics will help you develop a strategy for risk management and mitigation. Threats, assets and vulnerabilities will be pointed out, and the commensurate risk will be identified.
This is done by using current data and identifying trends for more accurate decision-making, forecasting and planning. With predictive analytics, your business can identify opportunities, expose risks and predict outcomes, and act quickly on these.
Risk management analytics can answer questions such as:
Will demand for my product drop off next month?
How much will we have to spend on overheads?
Do we have an estimated revenue for our new product?
What will the business' financial mid-period look like?
However, it's not just about predicting future events from a clean slate. If your business faces an adverse incidence, predictive analytics can help leadership identify and analyze the root cause. Subsequently, they can take preventive measures to minimize the risk of such an incident happening again.
It is important to note that predictive analytics for risk management can only be useful if businesses already have a defined risk strategy. You should have a system developed with adequate controls to accept or resolve risks.
Predictive analytics can be used to predict supply chain activities. This can help risk mitigation and even frauds.
A supply chain has many critical parts and roles, and even one disruption can result in unsatisfied customers and lost money. Since your customer satisfaction is linked to the timeliness of your shipments and deliveries, it is best to have an efficient supply chain. Hence, many companies invest both time and money into supply chain risk management (SCRM) strategies.
To begin with, you must understand where potential risks might occur, and then predict how plausible they would be at a particular time. While some risks are commonplace and easy to understand, others are difficult to predict, such as natural disasters or terrorist attacks.
Nevertheless, it is important to be prepared for the ones that are more likely, and even have a backup plan in case of the more unpredictable ones. (The COVID-19 pandemic has made that abundantly clear.)
Predictive analytics will never completely erase risk or be 100% accurate. With most predictive analytics, the challenges lies with an over-reliance on algorithms that are unable to predict the variables for human behavior or emotion.
Another limitation is that the use of data mining works well for static or linear issues, but not necessarily for complex ones such as human decision making.
However, predictive analytics has proven extremely helpful in certain industries or in specific applications, especially if the risks are based on facts and certain variables such as the number of products produced.
For example, predictive analytics can predict the chances of a breakdown of essential equipment. Furthermore, quality assurance models can prevent defects in services and products. The healthcare industry, too, heavily uses predictive analytics to cut costs and improve efficiency.
We hope this article has given you an insight into how your organization could use predictive analytics for risk management.
Integrating risk management solutions through predictive analytics services helps empower organizations to learn from past adverse incidents and adapt accordingly.
Have you used predictive analytics for a risk management plan before? How did it help (or not)? Let us know in the comments!
If you require professional predictive analytics services, BluEnt is just a click away. We've been operating since 2003 and cater to Fortune companies, large energy and tech companies, and homebuilders.
Ready to take your business to the next level with integrated risk management solutions? Contact us now!