Data analytics and automation is joining hands and transforming the way organizations access and manage risks. Essentially, enterprises – big and small – are collecting data, creating a full-bodied databank, and using all that cold hard scores to make decisions.
Where boosting sales, improving processes, minimizing payment issues, and business growth are some of the obvious perks of data analytics. One perk that was least expected, yet openly welcomed was that of accessing the risk credit sales present.
Most traders and manufacturers have to sell their goods on credit. That means, it requires businesses to decide whether the retailer has a good enough credit performance to be trusted with the stock.
While there’s nothing wrong with it, there is a slight problem. Businesses cannot afford the time it takes to go through the credit history of so many buyers.
What if we tell you that lending choices can actually be made without human intervention or major human intervention at least? Moreover, what if we tell you that the chances of the decision being wrong would also be lesser?
It’s true. Automation and data analytics have taken over the lending decisions and freed humans of yet another tedious task. McKinsey shared that effective automation, the ones that reduce manual processes can simplify lending. Interestingly, it also makes it easier to comply with regulations and eliminates human bias.
Here’s why we think data analytics and automation can save the companies from choosing the wrong person to sell on credit:
Automation And Analytics Are Full Of Possibilities
There’s no limit to what you can do with data and how you can use it for your business growth. In fact, data can be used to solve some of the historical problems in the company. One telecom company even recognized data analytics’ role in solving a longstanding issue of the need for revolving credit by low-income individuals.
Any company that is able to harness data and concoct it with automation, can come up with endless ways to process risks and make decisions. All you need to do is access the payment histories of the lenders and decide whether it makes them a good creditor or a poor one.
The aforementioned telecom company actually created a risk model with the data and automation tools, to find out the ability of the customers to pay their loans back.
Automation And Analytics Allow Companies To Be Systematic
While most companies keep track of their corporate customers by assigning them an ID that will make it easier to go back to their payments history, they forget being systematic.
In order to have an enriched data of all the businesses they are providing service to , there should be an integrated system that identifies all the touch points. For instance, clienteles can be divided into loyal ones, big spenders, and prospective clients etc.
Similarly, the data of clients who always avail the services on credit, and those who always pay it back, and those with a poor credit history should also be systemized, so that decision-making becomes easier and more accurate with little bias.
Automation Tools Allows Advanced Accounting And Bookkeeping
A company’s responsibility doesn’t end after passing the verdict on who can be trusted with credit sales and who can’t. The next step involves getting that payment back on time.
Automation takes that task upon itself by sending reminders to the buyers, by creating invoices immediately and luring the buyers with a direct link to the credit card payments. It also makes the process easier and faster to help the buyers avail the early payment discount.
Automation Tools And Data Can Help The Companies Rank The Lenders
There’s another way in which businesses can take one look at the data and decide whether the credit buyer is worth the risk. Automation can allow companies to methodically rank lenders. They can quantify the risk sales based on the history of the lenders and make the decision easier and less time-consuming.
The risks can be calculated based on a certain limit of the credit sales, on collaterals or guarantees, the chances of default on the basis of risk rating etc.
Based on this scorecard, automation tools itself can give a green light to the lenders based on the ranking they have been programmed to go easy on.
Automation Can Set Credit Limits
One way to stay safe with high-risk sales is to establish credit limits. Set a red flag for any amount that goes beyond that limit so that the system refuses to process that sale. This limit can be established based on the companies’ borrowing capacity.
Automation Can Manage Credits
Automation not only decreases the risk of credit sales but also manages the credits. No longer would the businesses have to rely on manual bookkeeping and regular checkups to find out who has yet to pay their dues back. This will help safeguard the working capital of the company.
Develop A Process For Handling Overdue Accounts
Like we mentioned before, businesses should diligently chase after the buyers to pay their dues. That doesn’t mean they have to physically run after them, or ask their employees to spend hours on the call trying to get payment. It has to involve strategy and automation. For instance, program the software tool to send reminders in the first 90 days after the due date, because that is the time when the chances of getting paid are high.
Automation today has made it possible for the businesses to attain knowledge based on the data it collects. Businesses can make a better and wiser decision with data analytics, especially when it comes to risk management. While businesses are already knee deep in risks, with automation and data analytics, credit sales don’t have to be one.
Omnisys Solutions has a well-developed practice in RPA, which supports both data analytics initiatives as well as carry out automation driven initiatives. With a focus on business value, Omnisys leverages RPA to deliver tangible dollar benefits to its customers.