Embrace the Change through Complete Risk Assessment

Excellent credit risk management offers your organization an opportunity to greatly improve overall performance and secure a competitive advantage.

We need to understand that risk comes with opportunity. As finance tools improve, there is a huge opportunity to build internal practices for mitigating risk and deliver value through technologies like artificial intelligence and machine learning. The leaders that understand risk are more likely to succeed. Using modern technology and tools can help leaders monitor and predict financial risk in their customer base.

What is Risk Assessment?

Having a comprehensive risk management framework in place for your business is imperative. To better understand how finance leaders prepare for disruption, Dun & Bradstreet conducted a risk management study in the year 2018. Not surprisingly, finance teams said managing risk is a struggle.

The key to embracing potential disruption is to develop a comprehensive risk framework. While risk is unpredictable, a detailed and thorough risk assessment can offer peace of mind and reinforce confidence in employees, suppliers, partners, and customers.

There are several factors to consider while creating a successful risk strategy. Firstly, assign a leader to supervise the plan and ensure that employees understand their role in managing risk. Secondly, you should document all critical business processes and identify potential risks. Ideally, you should prioritize risks based on the likelihood of occurrence and its impact on your business. Finally, examine your plan regularly to identify new risks and monitor the plan’s effectiveness.

Preparing for Risk with Predictive Analytics

Organizations can get ahead of business risks by using and implementing machine learning and predictive analytics. While the practice of predictive analytics is not new, many organizations are still not sure of how to use it to their benefit. The thing to remember here is that predictive analytics reveals if something is likely to happen, not that something is going to happen.

Predictive risk analytics uses a wide variety of techniques, including data mining, statistics, machine learning, and artificial intelligence, to analyse current data and make predictions about the likelihood of unknown future occurrences. Thanks to machine learning algorithms, organizations can better and more rapidly predict what is more likely to happen and prepare themselves in a more cost-effective manner.

Building Data-Connected Enterprises to Tackle Business Risk

Formulating a business risk strategy that leverages predictive analytics can help organizations monitor and predict customer risk. Organisations need to understand that there is more to collecting accurate data, as outdated and incorrect data can become a liability for the organisation rather than being an asset. With a standardised-data structure, it is easier to access important information and encourage employees to make the smarter decisions that are necessary to avoid risk.

Sponsor Ads

• Ad Space Available - Rent it Now!

Report this Page

Created on Feb 26th 2019 23:13. Viewed 393 times.

Share on APSense

Comments