8 Reasons to Use Credit Scoring
There are many excellent reasons to incorporate automated credit scoring in your credit (and collection) operations, including faster and better quality decisions, enhanced customer service, more effective compliance and controls, and significantly reduced overhead. Employing credit scoring in a B2B environment is in itself a credit management “Best Practice”, especially if you have many customers. Here are some benefits to consider from credit scoring, which includes leveraging the use of your own historical data, as well as credit bureau business information and industry trade payments.
- Speed of Credit Decision. Scoring can dramatically shorten the time it takes to get orders approved, which is a major customer service and sales benefit.
- Personnel and Overhead Savings. Scoring automates the decision process, dramatically cutting down the personnel costs associated with credit approvals, letting you do more with less.
- Establishing Policy. You can use credit score ranges and payment score ranges to establish corporate policies for risk level acceptability and slow payment tolerance.
- Credit Policy Consistency. Scoring insures consistency in your application of credit policy, and as a result helps keeps you in compliance with Sarbanes-Oxley and audit requirements..
- Decision Objectivity. Taking the human element and emotions out of a decision will usually produce a better result, including recognizing likely frauds.
- Set up Risk-Based Collections.. Use blended credit risk scores to set collection priorities, making sure the accounts with the highest risk for non-payment get collection attention first. If you focus on “risk”, and not just age and value, you will have better outcomes and less bad debt.
- Proactive Customer Relations. You can become a partner instead of a roadblock if you counsel customers on how they can improve their scores, by highlighting areas of weakness for them.
- Fewer Bad Debts. You can expect reduced bad debts using valid scoring methodology, since otherwise many smaller customers would not get the same level of review as the larger exposures.
Elements Used in Automated Scoring
Financial metrics for large corporations can be predictive in the 2-5-year range, and many credit analysts still use the traditional Altman-Z Score for this purpose or, better yet, the Credit2b R-Score™, which also incorporates cash flow elements into the calculation. However, as a scoring element for smaller debtors,, financials are often not available and, when they are, they are often stale, or unreliable and subject to fast swings. For these smaller debtors, the other data elements become more critical, including:
- Years in business
- Experience of principals
- Number of employees
- Revenue and Assets
- Business and Industry Trend
- Financial information (if available) using a dozen key metrics
- Public records information
- Supplier payment experience (including your trade credit groups)
- Bank and Lender Experience
- Credit Line Utilization
- Credit exposures of other industry suppliers
- Public filings, liens, etc.
- Transparency with key suppliers
- Derogatory comments; Reputation
Using Big Data and Machine Learning
Building truly predictive credit scoring is now significantly easier with the ability to capture “Big Data” from multiple sources and analyze it with powerful software and hardware, where the “machine” can learn from experience, conclude and adjust its conclusions – that is, make and correct its decisions – based on experience, patterns and trends it sees in the data. This big data analytics field is expected to grow almost 30% in the next three years, according to IBM, and is a trend that is powering Credit2B’s credit analytics.
This trend is called “Machine Learning”, where computers are “taught” to detect patterns in data in order to both predict and validate outcomes with regression testing of past events, and then adjust those based on current events. Reaching this goal has gotten much easier due to the rapidly increasing power of computer processing.
Deming said “Without data you’re just another person with an opinion”. We would add “if you can’t process data in a way to produce real insights, you just have numbers”.
Our experience is that that leveraging the science of machine learning can produce significant improvements in predictive credit and payment scoring. We use these techniques to build and customize automated credit scores and credit lines for large trade creditors that need to improve and accelerate credit decisions. As we are a cloud-based service, there is no software to buy, and we integrate easily with client processes and systems.
Designing a Scoring Model
Our Credit Scoring Framework has a Simple Flow:
- We agree on the outcomes you would like to predict (e.g., bankruptcy, default, severe delinquency, or X days late).
- Our model team does all the work, in collaboration with you.
- We create a training sand-box using on data attributes from multiple sources, including your own experience; for example, business standing, financials, and debtor payment histories, often as many as a dozen separate data elements, even unstructured credit data, such as industry “attitudes”. Sharing this in our cloud platform speeds adjustments, saves time, and simplifies managing the data.
- We can adjust for the outcome you are aiming for, or test multiple outcomes based on “model training sets” created in your sandbox, adjust the importance or weight of certain elements.
- More elements do not always better produce a better outcome; what is important is to pick and test for the right elements. For very small businesses, factors such as years in business, number of employees, and social reputation are critical. Economic factors are important. If the consumer economy turns down, it is a leading indicator of problems with payments and defaults in subsequent quarters.
- Machine regression picks and weight-adjusts the credit attributes that are critical for the outcome you are trying to predicts in real time.
- Using real-time industry data ensures continuous updates to scoring attributes and weights.
Advantages: The scoring models self-adjust continuously without manual updates, pulling in more data types than traditional models, including micro and macro-economic variables to target the prediction of outcomes that meet your company’s needs. The models adjust for specific industry or business needs based on your unique data or information, defining your preferred outcome with great precision.
Calculated Dollar Credit Lines
To complete this fully automated process, we can optionally provide calculated credit lines that can be integrated with any financial or ERP that you use.
- By applying your corporate policies, we can calculate a Dollar Credit Line for you, adjusted to your unique circumstances. This takes into consideration a number of factors, including your tolerance for risk (are you in a fast growth or more risk-averse environment), lender or insurance limits, or product profit margins (a consumer good with a 60% gross margin will tolerate more risk than a service with a 17% margin).
- Our modelers interpret your process and policy, replicating your rules in a computer model. By way of example, where no financials are available and the customer has fewer than 10 employees, you may decide “do not calculate a credit line” but instead perform a manual review, or adjust a CCL based on the presence or absence or magnitude of certain elements.
- Through feedback through a regressive “fit test”, we can adjust the algorithms as required to bring your scores in line with your desired outcomes.
Advantages: Custom Calculated Credit Lines are built to your circumstances, and adjusted as required by your rules, policies and both internal and external events. Machine generated credit lines are useful for accelerated decisioning and risk analysis across an entire portfolio. Credit2B can actually do this across 99% of registered businesses in North America.
By using computational power and a scientific approach to data analysis, we at Credit2B.com can produce extraordinary results for automated credit decision processing and in doing so, improve credit decisions and customer service, while streamlining credit operations.