A credit study is a mandatory first step for any person or company willing to obtain a loan. This analysis aims to indicate how soluble borrowers are, whether they are able to meet their obligations and, contrarily, what is the potential magnitude of their credit risk.
A traditional credit analysis requires a strict procedure that involves three key steps: obtaining information, a detailed study of this data and decision-making.
Information gathering:
At this initial stage, banking information such as credit extensions, payment history and sources of recovery is required. For example, in the case of a company requesting a loan, a credit analysis would look into future investments and how the leant funds will be used up. Likewise, the lenders will investigate if the person, both physical or legal, has any guarantee that assures the full recovery of the debt.
What happens if a person is soluble but is not part of the banking system?
The big issue with the traditional method of collecting information for a credit analysis is that it does not allow people who are outside the system to be analysed, such as students, immigrants or entrepreneurs. For this reason, today there are new ways of analysing credit that are much more thorough than the traditional procedure, since they use not only financial data, but also intelligence based on the general behaviour of the person.
An example of this is alternative information. Alternative data arises from non-traditional sources including cell phone metadata such as app payments, social network behaviours, Internet purchases, mouse or keyboard movements, among others. This enables a holistic view of the person or institution without necessarily having a credit history.
Information analysis:
In traditional credit scoring, this stage begins with the verification of documents such as ID, passport, business licenses, among others. It continues with the study of past financial information such as balance sheets, financial statements, cash flow, etc. In the case of a company, the scope of the project is also studied, in other words, whether the project is scalable, its business performance, competition levels and company growth. Analysts use this data to understand potential risks, and if the person or institution will have the sufficient liquidity to pay the loan. This whole process can take a long time since it is done by people.
Unlike traditional credit studies, new alternative credit analysis uses machine learning algorithms, which accelerate processes allowing credit scores to be obtained automatically. This means better customer service and the ability to respond to multiple requests.
Decision making:
In a traditional credit analysis model, once the analyst has collected and verified the information, he identifies the risk and sends his recommendation, be it positive or negative, to a credit committee that will make the final decision. This results in more time delays.
Alternative credit analysis is superior in this last step as well, since systematisation avoids human errors and facilitates new types of data processing that obtain accurate credit profiles. On the other hand, with the use of machine learning intelligence, the ability to learn continuously is amplified, which allows models to improve and to obtain more accurate behaviour patterns over time. This enables lenders to make informed decisions.
In short, alternative credit studies are here to stay, simplifying processes and allowing companies to make decisions more effectively and with fewer risks. Taking advantage of the benefits that this brings will allow lenders to have greater competitive advantages and attract a wide array of new clients.
