Credit analysts are hired for their ability to make the most crucial decisions in the credit to cash cycle. The decisions they make assist sales, improve customer relationships, and improve the company’s safety. Instead of using their analytical skills, they end up spending most of their time performing highly clerical tasks.
Let’s look at the day to day activities of two credit analysts, Fiona and George. Fiona’s company has an excellent credit management software solution that enabled both Robotic Process Automation (RPA) and Artificial Intelligence (AI). On the other hand, George has to do all the tasks manually.
George has to look at all of the paper-based credit applications that have come in and enter the data into the ERP manually. Given the laborious nature of this task, data entry mistakes are inevitable.
If there are any missing fields, he then has to contact the company and ask for the required details.
Once all the details have been filled in, he checks to be sure that the filled details are correct for each and every customer. For example, a customer might have missed a digit while filling his company’s bank account number.
After this, George goes to the different portals of public financials and credit agencies to pull more data about each customer.
Then he puts this data into his company’s scoring models to come up with a credit score and credit limit.
In some cases, he might also have to set up a meeting with his manager, provide her with the details, and then get her approval.
The prospect fills out an online credit application (present on the company’s website or via a link sent by email) which makes sure that all the details are filled, and the validation on the form also checks whether the data entered is correct or not. Then the RPA-powered credit solution proceeds to pull data from credit agencies, credit groups, credit insurance agencies, and public financials. It compiles all of this data and calculates risk categories and suggested credit limits for the prospects based on the credit policy at Fiona’s company.
All that Fiona needs to do is look closely at the data available to see if the suggested risk category is right, if it isn’t she overrides the decision based on his experience and then it is up to Fiona’s manager to validate the suggested credit limit. All of these tasks are automated!
When Fiona (the credit analyst) reaches her office, the first thing she sees is a prioritized worklist for the day. All Fiona has to do is check for exceptions. Structured and hierarchical workflows also make sure that the credit limit is approved by Fiona’s manager or even her treasurer if the account is strategic.
Once every 6 months, George and the other analysts on his team take the accounts assigned to them and perform reviews. They go through an entire process similar to that of customer onboarding.
First, George makes a list of the accounts under him. Then he filters out the ones with low credit limits because it’s not feasible to review the credit limits of every account.
Next, he looks at their payment history to check punctuality, types of deductions, order size, and seasonality among other things. He also checks to see if any collaterals like bank guarantees, securities, and insurance documents have expired. Then he pulls their current credit score.
Finally, he re-calculates the credit scores and re-assigns credit limits for every customer assigned to him. Even after all this, he has to book a meeting with his manager to get the critical accounts reviewed. Most of this time and effort is spent doing clerical tasks so he cannot even review the less critical accounts.
Every critical alert including collateral expiration, rating downgrade, or a bankruptcy alert from a credit agency would have automatically triggered an ad-hoc credit review and suggested a revised credit score. Therefore, Fiona’s worklist of periodic reviews is minimal. Fiona also has the option of setting reminders on her system to re-evaluate and perform reviews for some of her less critical accounts.
Moreover, Fiona doesn’t even have to run various reports and search excel sheets to get data on payment history, credit reports, open A/R, deductions, and many more.
Whenever George sees that an order has come in, he has to explicitly check for the current credit limit and put the order on hold. If it is a critical customer, he informs the collections department about it, asking them to collect previous dues to free up credit. The collections team may follow up with George to ask for additional information about the customer.
Once the cash is collected, he waits for the cash application team to apply it and close past invoices.
If either of these teams doesn’t react quickly, he also has to follow up with them.
If the customer tells the collections team that they’ll pay it later, George checks the payment history of the customer. Then he makes an exception in his system and releases the order by temporarily increasing their credit limit.
All of this disrupts the daily plan of three departments: credit, collections, and cash application.
The artificial intelligence aspect of Fiona’s credit solution predicts which orders may be blocked in the future. It does this by looking at the customer’s credit limit utilization and past payment trends. Looking at this, Fiona sends the customers a reminder beforehand and prevents blocked orders all together!
If an order still gets blocked, Fiona’s company’s credit solution automatically sends correspondence to the collections team with all the details about the customer.
She then follows up with the cash application team to make sure that the order can be released quickly.
When she has to update the credit score, all of the past order histories have already collaborated and a revised credit score is suggested by the solution.
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