High volumes of credit requests are costly to a company due to the number of employees needed to process paperwork, customer service time spent relaying credit information, and riskier credit portfolios due to out-of-date views on customers’ ability to pay. High volumes of requests can be due to either new customer credit applications coming in or through requests generated from credit holds or order blocks. Delays in the approval of credit for both types of requests can lead to unhappy customers and lost sales.
Most companies respond to increased volume of credit requests through workforce increases or lower standards for credit risk evaluation. The former is costly to the enterprise due to payroll increases and costs associated with the onboarding and training of new employees. The latter is costly to the enterprise in the form of a riskier credit portfolio. The core reason a high volume of credit requests is costly to the enterprise centers on the numerous manual processes included in the credit approval process. These processes are filled with manual, paper-centric tasks that include faxing, filing, organizing, and searching through stacks of paper.
The following diagram segments credit requests between High/Low Value and High/Low Volume for a total of four different segments. A Low Value request poses little risk to the credit portfolio due to the desired credit terms and credit limits. A High Value request poses a greater risk to the credit portfolio due to the amount of credit the customer is requesting. Both value and volume can be subjective based on business or industry factors.
In each of the four segments, the manual processes can be automated and digitized through an electronic system. Documents required for credit requests, such as Trade/Bank references, credit bureau reports, and financial statements, are automatically requested from their specific sources. Information is received and stored electronically for quick and easy access. Follow-ups, notifications, and reminders can be automated using a rule-based workflow.
Low Value credit request decisions are automated based on quantifiable and repetitive everyday credit decision rules applied in the credit department. Automation of these types of requests poses little risk to the organization due to the negligible effect each request has on the overall credit portfolio due to the small size of the request or the strong payment record of the customer. Automation of High Value credit requests presents challenges due to the high amount of risk posed to the portfolio by the customer in question. In these situations, it is best to adopt a risk segmented approach that utilizes both an automated and manual approval strategy. Based on risk classification of the request a decision is either automated or sent to a credit manager for manual approval.
Looking at the chart, the need for automation, of both process and decision, is most greatly felt in the Low Value/High Volume segment. The high volume creates significant back office costs that can be eliminated through automation. The low risk posed by a Low Value credit decision allows for the easy automation of the actual decision, based on predefined criteria.
The Bectran Instant Decision Manager(IDM) accomplishes the automation of both process and decision. Users segment their customer base by risk and apply appropriate credit assessment and data requirement rules for automatic approval for new customer credit applications and credit limit increase requests from existing customers. Credit requests that meet the specified criteria are automatically approved or declined based on the IDM process rules. Utilizing preset, user-defined criteria IDM automatically collects data from the required sources, scores the data based on a custom scoring model, and then produces a credit decision instantly. IDM greatly benefits credit departments subjected to high costs generated from Low Value/High Volume credit requests. IDM reduces paper usage, makes customers happy due to instant credit decisions, and frees employees to work on higher value tasks.