With companies realizing the importance of credit analysis, its value is increasing every day. It becomes the job of analysts to review data and approve scores, and the way in which is done has evolved from the past, and is yet to morph further. We will be going through the credit process, how it was in the past, and what is the present scenario, and what the future holds.
Everyone knows, to get off to a good start is the best way to achieve something. It applies everywhere, whether you’re planning a trip, or working to get that promotion for the extra dollar. The same to companies as well. They want growth. Growth that is not fast and risky, but growth that is steady and sustainable. Why you may ask. As the Laws of Nature dictate, the Universe is growing, and becoming more stable at the same time. And with science as a witness, it’s best to mimic nature.
So, how do companies grow bigger? In layman’s terms, by doing business, of course! Business that is healthy for growth. Like a healthy diet. And the Credit department is the dietitian that determines how a company can accomplish that. Determining what’s good and what’s bad for the company is a tough task. Setting the right limits for business is what prevents poor deals, and more importantly, reduces the risk of a default.
How can a company get off to a good start? The order to cash cycle is how business is conducted, and getting the right traction can help with a good start. Credit is the initial step for a company in the O2C cycle. A good and efficient Credit team can lead the road map to a successful business deal. When your company receives a new order, or when a new customer is being on-boarded, the very crux of the trade deal is defined by the Credit team. They have to be updated on every factor that might affect a customer’s ability to pay, be it their payment patterns, revenue, or performance in the market, and make the call on risk-to-reward ratio.
Not only this, trade credit helps companies, especially small ones, gain customers easily, and to improve customer relations, as customers can use that cash on other needs. Not just doing business, but getting customers is also essential for the growth of a company, and offering credit can be a good rainmaker. So where did this idea of credit originate from?
The roots of credit can be found going deeper than the 13th century when the need for credit arose due to the lack of supply of metals for minting coins. The credit system was more widespread and used than the well-known barter system. Consumers and suppliers would keep tabs of who owed what, and this network spread everywhere, from within families and neighbors, to different trade groups in different geographies. For centuries, credit was a major form of transaction, even more pervasive than coins or barter. Credit was also convenient for traveling for the common man, and more importantly, to traders, who used bill of exchange in the medieval times to keep credit information handy, as carrying heavy coins was a burden.
Credit was lent on the basis of trust, honesty, and reputation, and a person or merchant with a bad history of payment would not benefit from this. Even centuries ago, some form of research and analysis was done before offering credit. A lot of time was spent on collecting the credit that was lent, and often times, debtors failed to pay, a problem that is seen even today. And to avoid this, creditors relied on bonds, notarized records, sureties, and pledges. From 15th to the 17th century, credit systems progressed towards banking, with the importance of credit slowly fading.
Although trade credit lost its significance, it was still used between merchants and companies, to make trading easier with them. A rise in the use of credit was seen after both World Wars, with the purpose of attracting customers and increasing trade as a way to get a financial foothold in unstable economies. With this, many insurers offered credit insurance as well, to protect companies from debts and defaults. Since then, credit has been etched to the Business to Business world, as a way to gain customers, and remain ahead of the competition.
Credit scoring became important to companies for knowing which customers were safe to do business with, and the traditional process involved contacting other companies the customer did business with, studying their business model, going through their financial records to identify payment history, and publicly available records. Credit scoring back then was difficult and very inaccurate
With the modern age and its technological advances, companies do business across oceans, with every detail of trade and transaction communicated and stored digitally. In spite of the fact that digitization may seem like a breath of fresh air that has supported globalization of trade and made the allocation of credit easier, a lot of problems still persist. With growing business and customer needs, the impacts on companies and their consequences are increasing as well. Credit management is such a process where these effects are felt. Let’s take a look at how.
The very beginning of doing business with any potential customer is to get them to submit a Credit Application. It is a mandatory process that every potential customer needs to go through. Like any other application, it requires them to fill out details of their company. And this is where the first problem arises. While filling out applications, many details are left blank, due to various reasons. Maybe the person filling out the form doesn’t know the answer to the required field, or maybe it is against that company’s policy to give out that piece of information. Maybe it’s a bit of both, the person doesn’t know whether he is allowed to fill that field or not, and how much his company’s policies dictate the limit to information being given.
With this, it becomes the responsibility of your credit team to take a step ahead and approach the applicant for those details, since this could make or break the opportunity for a new customer. After correspondence with the applicant, those details are extracted and filled in the application. A simple task of submitting an application can take days, delaying the chance of doing business with a new customer. A simple solution to this problem could be an online platform to submit an application with details that are necessary, be marked as mandatory. Unless those fields aren’t filled, the application cannot be submitted.
Another problem could be the collection of financial data required for credit analysis and scoring from the customer. This could include documents like revenue details, financial records, licenses, tax documents, guarantees, etc. Not just this, some of these could be outdated or non-standardized, meaning they may be redundant to your credit team’s analysis. Credit information, reports, ratings, and scores also have to be collected from credit agencies and bureaus for additional information needed for scoring. The collection of stacks of information can be hectic, difficult, or even impossible at times. Your credit team needs to align its needs with what data is offered and has to make do with that. Integration with agencies and bureaus may seem digitized, but a lot of manual work is involved in doing just that.
The problems don’t just end there. There’s paperwork to be done. And there’s a lot of it. With hundreds of applications flowing in, the credit information gathered is immense. There’s an exponential growth in potential customers and it becomes difficult for your credit team to keep up with the applications and documents. Onboarding customers becomes a strenuous task, with papers piling up every day. Tracking applications and supporting documents is difficult, and if some papers are missing, an analyst has to gather them again, essentially starting from scratch. This could be tackled with the use of a digital means to gather and store all information from applicants and agencies, where all necessary documents can be accessed easily, without the problem of physically storing each paper. So, what are some other problems related to information gathering and storing?
There’s a lot of issues when it comes to the data part. Credit analysis is data-driven, and hence there’s a lot of information involved in scoring thousands of customers. It is necessary to have a platform in place which stores all the information. Usually, it is all in physical form, and it becomes laborious to find and collect each piece of information when required. Recently, companies are switching to digital means, to store data in cloud storage or in a central database. This makes retrieval of information easier.
But this may also lead to other problems, such as data security, storage reliability, and backup, and the level of access an analyst or manager has to the data since these are sensitive in nature. This becomes a cause for concern as this information could be seen by anyone with malicious intent. So a well secured and robust database needs to be in place, and it should offer basic features such as searching using filters, data backup, centrally accessed storage, etc.
Another problem with the aggregation of credit data from agencies and bureaus is that the information is presented in different formats. Different agencies and bureaus have different scoring and rating models, and the information provided by them might vary. This non-standardization of data causes confusion and analysts have to do extra work to find credible information. This could take weeks, considering the number of customers a company might have.
Sometimes, the data that is available with your credit team, or with the credit agencies is outdated, and an updated document or piece of information isn’t available in any of your sources. Or the data that is available is incomplete and doesn’t have all the fields of information required. In these cases, your credit team has to run the analysis based on incomplete information, and this increases the risk of a default, as the credit score allotted could be inaccurate.
Credit scoring is the most important task of credit teams. It decides how much credit limit should be assigned to a customer to avoid the risk of a default while garnering the maximum revenue from that customer. Assigning limits can take a lot of research and analysis into an account. This is a result of going through hundreds of papers, for thousands of customers. It is a tedious task, and any rushing could produce incorrect scores and put the company at risk. To overcome the time taken by analysts to allot scores, many techniques are used by the credit teams, and these can be problematic.
Many credit teams use a common scoring model for all accounts or a group of accounts. In other words, they use a one-size-fits-all model for their customers. This is an inefficient way of scoring, as many companies differ in many ways, such as industry type, geography, local laws, etc. For example, a pharmaceutical giant will not have a problem in clearing its credit limit as they have a constant cash flow, whereas a retail business may run into losses due to constant competition from retail giants.
A different way of looking at this is the geopolitical scenario of a customer. For a company in Mexico, it is necessary and often advised to consider the age of that company into scoring, as the local laws and political influence can decide the future scope of that company. And the same may not be considered while scoring for a customer who is located in the United States. Hence, analysts have to identify these traits and consider them as factors while scoring.
Another problem is the use of scoring models that are too complex, or too simple in nature. Let’s break it down for easier understanding. A model that is too complex might consider factors with a heavy weightage that may otherwise, and in reality be insignificant. The same applies to the opposite of this too, models that do not consider important factors may lose out on an accurate score and can expose a company to the risk of a default. These models also have to be compliant with the credit policies of the company while assigning a limit to a customer.
While the scoring is managed by the credit team, it is influenced by the sales team as well. The credit team will decide on a certain limit, but the sales team might say otherwise, claiming certain promises and assurances given to that customer for the sale to take place. Even in general, the input of the sales team into an account is considered very important. And the repeated communication between sales and credit is problematic and can often lead to confusion and bias. There needs to be consistent and reliable communication between these teams for deciding a credit limit.
Now, a customer’s creditworthiness doesn’t remain constant. It changes with time, depending on the very factors that decide the credit score, such as the growth of that company, its share prices, revenue, profits and losses, and the management it is under, etc. Based on these changes, it is imperative to change the credit score, and the credit limits given to these customers as well. This is where periodic reviews come into the picture. As the name suggests, it stands for the time-to-time reviews on the credit score of an account, and it can help identify increasing risks associated with doing business with customers or whether your company can increase the limit to get more revenue from that account.
The main challenge with periodic reviews is that it keeps piling up with an increasing number of customers. This is because the work done to do these reviews increases with the increasing customer base, and hence analysts have to contact agencies and bureaus for the latest information and communicate with the customer to get relevant data. As the number of customers adds up, the time taken to do these increases as well. This leads to a lot of pending periodic reviews and causes untimely reviews. Let’s say a customer might be going bankrupt and a periodic review that could’ve identified this has been pending for months, this increases the risk of a default or non-payment.
Another problem is the collection of credit data for hundreds of accounts. Analysts have to search through directories to gather old records, analyze them, find payment patterns, collect updated credit data from sources, and then try to assign a credit score. This means they have to find and analyze hundreds of thousands of pages of information for periodic reviews, and this takes the majority of their time. Automating small tasks like collection of data by integrating with sources can save a lot of time and effort involved. Analysts also have to identify the model and factors involved in scoring one account, because any major change in the scoring can lead to inaccurate results.
With new technologies being incorporated into the accounts receivable scenario, new ways of credit scoring and data aggregation are being introduced. These trends are making the job easier for analysts, with some level of additional analysis and automation.
One such new method is the use of a dynamic credit scoring model instead of a static one. What is dynamic scoring? It is a credit scoring model that uses real-world and real-time factors that might affect a customer’s ability to pay. This is important because there are many aspects other than what financial documents and reports include. These factors change in real-time and can drive any customer to bankruptcy, hence it is necessary to include them.
These dynamic factors could be plummeting share prices, geopolitical issues such as a change in government, change in management or organizational structure, mergers and acquisitions, etc. These could result in major changes in the decisions made regarding their businesses and hence increase the risk of a default in payment. Therefore, a credit scoring model should be incorporated which uses dynamic factors for assigning a limit.
Another new method that has surfaced is using live-updates along with dynamic scoring, to get the best risk containment strategy possible. What this does is it sends updates to your credit team whenever there is an escalation of risk associated with a customer, such as bankruptcy or falling share prices, and this can help your team to block orders from that customer and inform the collections team to communicate with the customer for payment.
Companies also opt for a faster way of onboarding customers. This allows for getting into business faster, thus adding to the revenue. Some ways of doing this are approving applications for small customers who place small orders, or by having a system in place which runs the analysis on credit scoring and suggests limits based on set parameters. This requires some level of automation in the process.
A new strategy used by companies is buying credit insurance. This allows them to safeguard their finance whenever a customer fails to make a payment. This is essential whenever a company opens its business in a new country, or whenever there is a risk of recession. While expanding to new geographies, companies often face the problem of doing business with new customers since there are various risks involved, mainly due to the fact that not much information is known about the customers and there are different laws in place. With changes in business reforms and laws, a recession is imminent, and once it hits, there will be a long list of bankrupt customers and trade defaults. So to secure themselves from bad debt, companies often buy credit insurance.
With the current problems still existing, the future looks at ways to tackle these and make credit management easier and faster. This could mean using the latest technology in the credit space. Some new innovations that are being incorporated in credit are Artifical Intelligence, Robotic Process Automation, Mobile Applications, etc.
Taking a look at mobile applications, these could enable customers and credit teams to work away from their office. Some uses for customers could be the submission of credit applications online via the mobile app, request for a credit score review, view blocked orders, etc. For the credit teams, it could mean running an analysis while on-the-go, gathering documents while away from a desk, or even communicating with team members, other departments, and customers via the mobile application.
A very promising aspect is the use of Robotic Process Automation (RPA) and Artificial Intelligence (AI). It allows almost all manual work to be done automatically. By using RPA, certain tasks and processes can be defined by programming them, and the RPA bot does those tasks, like mimicking an analyst’s work. For processes that cannot be defined by certain rules and algorithms, and some level of decision making is involved in doing them, Artificial Intelligence is used.
Using AI, tasks such as deciding a credit score based on factors that are not necessarily bound by metrics can be done easily. It can also approve credit applications
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