The credit risk process relies mainly on processing large amounts of data in a proprietary credit risk model to give a recommendation in terms of probability of default – the rating- and a credit limit. The result has a higher accuracy depending on the quality of data. The quality is defined by the sources of data and the dates of the events to which they refer. With the appropriate data, the credit risk department can assess the real capacity of its clients but in most cases, external data is not easily collectable and incorrectly processed. The result is often unsatisfying with respect to the reality of the business. Often, to facilitate the process, credit managers buy data or credit risk reports from Credit Risk Agencies to confirm the internal assessment.
When the assessment is negative, the cost of such report seems high as it prevents the business from generating sales. Even though it prevents the company from incurring losses, the results cannot be recorded in the accounts. When the assessment is positive, the credit limit is a general evaluation of the client’s capacity to pay its debt. Each credit agency has its own Credit Risk Model; a confidential algorithm that combine data and knowhow.
Accordingly, the credit rating and limits are different from one credit risk agency to another. The quality, the size and the length of the database that allow the design of Credit Risk Models is the main asset of Credit Risk Agencies. The sources of data vary from one country to another and data is usually collected manually. The manual process of collecting data is expensive but it creates an entry barrier. A new comer will have to finance the collection of data for many years until its database has enough information to estimate the Credit Risk.
This situation is changing in some countries, since official institutions are developing websites to check market information. These on line sources facilitate the process of collecting digitalized external data that can be used to identify basic ratios. Any company can generate simple descriptive statistics. But companies that look well beyond basic statistics by developing predictive models to identify the most profitable clients and those most likely to default are developing analytics.
“Analytics” were recently popularized to the public through the book and movie Moneyball. Written by Michael Lewis and starring Brad Pitt, the movie’s plot depicts a coach using analytics to lead its baseball team to become a league champion.Companies like Amazon have successfully implemented analytics, challenging the traditional way business is done. Today, long established companies, such as Procter and Gamble and UPS, are introducing analytics to create a competitive advantage.
The cost of analyzing large amounts of data prevented most companies from developing analytics. But affordable powerful computers and the last generation of “business object oriented technology” allow the final user to create sophisticated decision trees and algorithms to implement new business processes. The results can be instantly tested and optimized.
What will be the impact of “Credit Risk Analytics” on traditional models used by companies to assess their clients?
Credit risk managers are adopting this philosophy to create “Credit Risk Analytics.” Supported with the right technology, credit managers pool workflows of internal and external data to get a comprehensive understanding of their clients’ credit worthiness.
The Credit Managers’ knowhow of business specificities paired with internal and external digitalized data allow the development of the highest quality credit risk models fitting companies’ business needs. The result is an immediate improvement of the company’s profit and cash flow.
New information that affects a client is automatically processed by the system and immediate actions are taken to protect the company according to the decision trees designed by the credit risk manager. The information is sent automatically to the sales department and senior management, developing a culture of transparency and enhancing the company’s corporate governance.
Companies that adopt a digitalized data philosophy quickly improve their profit and cash flow. They select the best clients and become more efficient by shorting the process and increasing their independency with less manual work.
Is Romania a country for Credit Risk Analytics?
Romania is one of the most advanced countries for external digitalized data about companies. Numerous sources of information exist and can be leveraged to structure a valuable risk assessment.
These external data combined with the internal data (payment behavior) extracted from the company’s system allow the design of Credit Risk Model optimized for the company’s needs. Each time new information is available, the credit risk rating and credit limit are calculated. The development of digitalized data and the technology gives Romanian companies the opportunity to create their own Credit Risk Solutions optimized for their activity.
By being more efficient, early adopters create a competitive advantage versus their more conservative competitors. The results embedded in the sales and marketing strategy transform the credit risk process into a strategic weapon that immediately improves the company’s profit and cash flow.