Using Business Analytics to Improve Risk Management
The business failure of a customer or supplier can cause financial stress for any business. Dun & Bradstreet found in its 2018 “(R)evolution of Risk Management” study that the ability to monitor and predict risks in the customer and supplier base are top concerns for finance leaders. For years, companies have relied on business credit scores and ratings to help with supplier and customer risk management. Advanced predictive analytics, along with effective customer or supplier risk assessments, can take these insights to the next level, helping finance and risk managers and procurement officers understand the state of a business and providing a level of transparency that can sound the alarm before a company collapses.
What Is Predictive Analytics & How Businesses Can Use It?
Predictive analytics is the use of algorithms, machine learning, and historical data to determine the likelihood of a specific outcome and provide the best assessment of what will happen in the future. Risk analytics, or risk management analytics, is predictive analytics specifically focused on managing risk-related issues. Risk analytics is applicable in a variety of scenarios, but for businesses specifically, the practice is commonly used to predict which business relationships might be risky and which are statistically more safe. Using risk analytics can also help determine credit limit recommendations, loan limits, or deductibles and suggest the terms and conditions to offer when working with another company.
Knowing whether a business is struggling can indicate whether it will be able deliver on its promises as a supplier or customer. If the business cannot deliver its product or pay its bills, or if it goes out of business entirely, it could leave other companies that depend on it scrambling for solutions.
Here is how advanced analytics for businesses can help identify a struggling partners, suppliers, and customers:
- Credit Scorecards
Scorecards can be used to produce predictive scores and ratings using information about an individual business. These scores and ratings are typically based on a business’s cash flow, payment practices, financial performance, and other metrics, as well as on historic industry trends. They can indicate delinquency, financial stress, the likelihood of failure, and supplier risk. The scores are programmatic analytics solutions that can be used directionally by risk managers looking to determine whether to work with a given company or what terms and conditions to offer it. - Statistical Clustering and Segmentation Analysis
Grouping businesses by similarities can help create the scores and ratings mentioned above and can also help data analysts recognize trends and spot potential issues. Industry and location are both examples of statistical clustering, where, in this case, determinations about risk can be made based on specific events affecting an isolated industry or location. Weather patterns in the southeastern US can affect certain industries and businesses, for example. - Data Aggregation and Statistical Analysis
Having a huge repository of data allows for more accurate prediction and advanced linking when aggregating data and performing statistical analysis. How many years a company has been in business is one oft-mentioned factor when discussing failure risk, but many other factors can affect the health of a business. For example, if a business is very dependent on a specific customer or supplier, potential issues with the customer or supplier could impact how risky partnering with the original business could be for another company. By looking at large and linked data sets, statistical models can be created that can predict the likelihood that a business struggles or succeeds. Data linking can be especially important because it can uncover issues that might not be apparent using data on a single business to determine risk. - Scenario Analysis
Mapping specific scenarios of financial risks or economic conditions – whether firm-specific or macroeconomic – that a customer or supplier portfolio might encounter and studying all possible outcomes can help with identifying weak partners, whose poor health in a given scenario may cause considerable stress to the business if that scenario actually occurs. These stress-testing exercises can be performed on customer or supply chain portfolios in order to evaluate their robustness in all possible conditions. An analysis might reveal some businesses within the portfolio have a higher likelihood of failure or default in specific situations. The next steps would include strategic planning, such as placing these businesses in a specific risk bucket and watching them closely as different economic conditions unfold. Such measures can help manage the risk from these businesses effectively. - Machine Learning
All of the examples mentioned above can be executed traditionally using a combination of machine learning and human intelligence. Depending on the volume of data, how complex the data structure is, or how specific the problem set is, machine learning alone might do a better job than using traditional analytical methods.
The Advantages of Credit Risk Modeling
The combination of all these tactics can be used to create a credit risk modelling which will help credit risk and procurement managers make intelligent decisions around business partnerships. Risk indicators can help reveal if there is a likelihood that a company might fail. But even though a company is struggling, there may still be a chance that with the right information and the right decision-makers at the helm, most of the identifiable risks could be mitigated proactively. Sometimes working with such a company might be worth the risk, if the company partnering with it is prepared.
To learn more about business risk, please visit our Finance section.
