What Makes a Good Credit Monitoring Score
A good credit monitoring score helps credit managers avoid financial distress and get ahead of bankruptcy. However, all company credit scores aren’t created equally, which poses a big challenge to credit managers: which elements of a well-rounded score are crucial in making informed credit decisions?
The truth is, a streamlined process of monitoring and detecting risk is half the battle. In order to ensure financial health, a credit manager will want to use a score that incorporates data from many sources to calculate a precise, accurate credit score:
Patterns of financial distress through time
If a score only tells a company’s current story without considering the past it has limited value. Credit managers already use an array of other data -- trade information and quarterly financial reports -- to determine the financial health of a company. But a superior credit score must provide something more substantial. It should be able to detect patterns, like periods of late payments or changes in liquid assets, that predict financial distress in order to protect a company’s portfolio.
A credit monitoring tool should have the capacity to isolate only the companies in your portfolio that pose the greatest financial risk, as demonstrated by their scores, allowing time to reduce any negative impact.
Comprehensive data sourcing
When a score uses only one source of data its accuracy is inherently limited. Sourcing data from multiple outlets, like financial statements, historical data, and trade payable information, offers a wider scope from which decisions can be made. Essentially, a scoring model built on the past and present financial standing of a company provides a better foundation from which to examine the risk of future financial distress.
Manually sifting through this data for one company can be cumbersome, let alone across an entire portfolio of companies. Worse, the more data you end up reviewing manually the more likely it is that key financial health indicators will be missed. Using a score that aggregates a range of financial data provides you fast insight and allows you to focus your energy where it will have the most benefit.
The confines of payment history
Payment data only provides a partial picture of financial health. What’s more, payment history -- an accurate metric for private company risk -- can be a misleading metric for public companies, which tend to remain current right up until the day they declare bankruptcy. And though private companies might make up the bulk of your company’s portfolio, the bulk of your dollar risk likely resides with public companies.
True, payment data can be an accurate marker of company health in the private sector since private companies must adhere to more strict regulations in terms of borrowing and capital. For public companies, this is not the case.
Public companies have easy access to capital and financing, which enables them to make timely payments while masking an undercurrent of other credit issues like debt ratio. In fact, our research shows that when public companies fail, the dollar amount is staggering: in 2016, 353 public company bankruptcies constituted $358 billion in combined assets.
Real time data
Company financial data is reported quarterly, and can provide useful insight into how a company performs over time, but under that data, companies in financial distress might be able to hide the issues they’re facing. Financial data alone does not provide a well rounded picture of a company’s financial health, therefore a timely assessment of real world data contributes to a score’s level of accuracy.
This is why the FRISK® score incorporates a company's stock price volatility, adjusted for dividends. This information captures the wisdom of markets and provides a daily update of the risks a financial counterparty poses to your company. The constant monitoring immediately alerts you to a problem should something drastically change between reporting periods so you can act quickly.
Behavioral data inputs from advanced technology
The continuous advancements in technology provide credit managers with more accurate data at an exponential rate. Scores that incorporate the tools of applied science yield more deeply insightful results. One important piece of emerging data source is crowdsourcing.
Crowdsourcing key individuals within the credit management industry can provide insightful click patterns, which can detect risk earlier than ever before. Crowdsourcing, in order to add value, should adhere to strict standards:
- Time testing- crowdsourcing, like any other tool, should be tested for accuracy over time and improved upon when necessary
- Relevance- the users sourced should be industry elites who provide insight the community can benefit from
- Anonymous- keeping the crowdsourced individuals anonymous removes any bias
- Research- just incorporating click patterns isn’t enough. The information provided by crowdsourcing should be researched and the most relevant data extracted for use
In the real world, credit departments are spread thin and hard pressed for resources. The daily operation -- onboarding new customers, reviewing customer credit limits, updating cash flow forecasts, supporting sales -- leaves little time to continuously monitor the companies in your portfolio.
The technology solution and scoring method you use should be advanced, innovative, and intuitive. It should provide accuracy on a consistent basis and constantly monitor your portfolio for risk. This way, you can allocate your time where it can be the most effective.