by Sara Salim, Marketing Executive
According to financial managers and corporate technology experts, the pandemic has made CFOs aware of the vulnerability of their companies to the broadest external risks in decades. Today, data availability is limitless, technology is getting smarter, and business dynamics are more demanding than ever. In light of this increasing complexity and speed of change, it is essential to an organization’s long-term and strategic success to harness the power of an effective risk management process. The coronavirus has forced CFOs to build resistance, not only to COVID19 disruptions, but also to other external risks such as cyberattacks, customs wars, and climate change. Source
The data analytics power
During the pandemic, CFOs used advanced data analytics to reduce costs, optimize cash management, and secure supplies. CFOs, who are more effective at managing risk today, have abandoned the old forecasting methods that relied primarily on spreadsheets and back-to-back historical data. According to CFOs and business technology experts, they have implemented advanced analytics that track external and internal risk based on large amounts of data in real or near time. Advanced technologies like machine learning can be used to perform predictive analytics to account for the risks and drawbacks of risky decisions. Banking and financial services were probably one of the first industries to embrace and recognize the value predictive risk analytics can offer. Analytical algorithms are becoming more sophisticated and allow faster and more accurate decision making. According to Sharon Daniels, CEO of Arria NLG, a provider of artificial intelligence that converts structured data into natural language:
“You need to be able to look at what’s actually going on across the full landscape of risks, rather than relying on expectations based on internal historical data. There is just no room to be any further behind when it comes to detecting and measuring risks.” Source
In recent years there have been mining accidents around the world, explosions that destroyed oil rigs and small towns, and horrific accidents caused by overworked trains being derailed. For each one of these examples, however, there are dozens of small incidents that happen to businesses every day that affect employees’ safety. Employee safety is a decisive factor in risk management. Managing the health and safety of employees should be the top priority for all workplaces, regardless of whether it is a factory or an open-plan office. Mitigating risks linked to employee safety increases employee loyalty: when employees feel safe and secure, they are more likely to continue working for you. This reduces the possibility of employees leaving their job, and reduces the risks a business faces when an employee quits his job. For this reason, putting employee safety first in a business and mitigating and reducing the risks they might be facing everyday, will help the business protect business finances and avoid higher insurance premiums. Source
Transforming risk management with predictive analytics
The use of predictive analytics has become the norm for companies around the world. As each company focuses on reducing the risks associated with its workforce, predictive analytics comes into play and proves to be the solution to this requirement. One of the reasons predictive analytics has gained popularity is because of the ability to search thousands of records and past trends, and identify and detect weaknesses. With the rise of new risks, the use of data analytics has become more important than ever.
What is predictive analytics?
Predictive analytics is the process of analyzing current and historical events that help make predictions about the future. It is an emerging branch of advanced analytics that uses a wide range of techniques such as statistics, modeling, machine learning, artificial intelligence, and others. With the help of predictive analytics, companies can solve difficult and challenging problems to open up untapped opportunities and potential risks.
How can predictive analytics improve risk management?
When a business is faced with a catastrophic incident, predictive risk analysis enables management to analyze the cause or reason for the incident and take preventive action to ensure that something like the incident does not happen again. The proper use of predictive risk analytics helps organizations evolve and become more agile, enabling faster execution of risk mitigation controls and effective organizational change. Organizations need to identify all the critical metrics that affect the business. So the next step is to bring predictive analytics skills and know-how to the entire enterprise. Predictive analytics can map the changes that have brought about changes in the industry. When the head of a company has such foresight, they can make the right decision when the company is in the midst of an unplanned or crisis situation. Predictive analytics in risk management helps companies minimize risks that could damage brand equity or lead to losses. Corporate finance executives must manage, distill, and share data while following the guidelines defined in the risk mitigation framework to eliminate risk, reduce inefficiencies, and drive growth. A structured risk mitigation process supported by predictive risk analysis can enable better decision making and ensure that corrective action is taken to mitigate risks.
We would like to end our blog giving you five reasons why all risk managers should use data analytics. Data analytics will help you:
- Prevent repetitive losses
- Improve Insurance Premiums
- Improve Reporting
- Monitor Performance
- Forecasting and Decision Making
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