It has been almost two years since my work in designing Credit Score Cards and apart from a couple of notoriously exceptional days, I usually very much enjoy doing it. In this piece, I hope to walk you, enthusiasts, through the basic definition and my understanding of a Credit Score Card in the simplest of words along with the information we leverage to build it.
What is a Credit Score Card?
Imagine a process here — An applicant walks into a bank to apply for a Credit Card. Since Credit Card is a lending offering from the bank — it bears a certain risk because in case the applicant is unable to pay back the amount borrowed, the bank has to take the monetary hit. Therefore, it becomes imperative that the bank makes the cognizant decision of accepting or denying the application basis on how risky the applicant seems.
Here’s where the tool of Credit Score Card comes in — It helps the financial institutions in accessing the risk associated with an applicant. In mathematical terms, Credit Score Cards tell us the probability of an applicant defaulting (unable to repay the loan) within the repayment period. Credit Score Cards come in handy for all kinds of lending products like Credit Cards, Retail Loans, Unsecured Loans, etc.
Now your Curious George’s question pops — How is a Credit Score Card built?
The journey of building a working Credit Score Card involves a lot of steps, the first and most important of which is covered in this piece that is — Information, thy beautiful Information!
Level 1 — Leverage Information
The core building block of Credit Score Cards is the applicant’s data through which we can determine how risky a customer he/she would become. There is a multitude of data sources that we can leverage however, for the sake of mental blocks — we will categorize them into three major parts.
1) Application Information
2) Bureau Information
3) Third-Party Information
1) Application Information –
Whenever a candidate applies for a lending product from a bank, he/she has to fill in an application form designed by the financial institution. You’d be surprised how those seemingly annoying checkboxes turn into incredibly valuable information. Upon the compliance approval of the respective country, some of the interesting fields that are converted into information are –
a) Area of residence
b) Loan Status
c) Annual Income
d) Employer details
e) Mobile Number/Area Code
And there are a lot more, varies through each financial institution.
2) Bureau Information –
This is the cornerstone of information that goes into building a Credit Score Card. I have witnessed some countries with amazingly rich bureau information about a candidate which makes a Score Card very robust. The information synthesized from the bureau can be used to not only build a Score Card but take more decisions like what interest rate to offer on the lending product, line assignment, repayment methodology, etc. The length and breadth of the information can undoubtedly make a plethora of differences in the lending business. Again, amongst the detailed view of an applicant received from the bureau — some of the most common fields used are –
a) Bureau Score
b) Other Open/Closed Trades of an applicant
c) Repayment History on other trades
d) Any Negative comments from other Financial Institutions etc.
There are multiple ways through which this information can be used to squeeze out the maximum predictability of an applicant’s performance. To put things in perspective, almost 70% of the useful information that goes into a Credit Score Card is sourced from the region’s Bureau, pretty cool stuff right!
3) Third-Party Information –
This is where the exciting and innovative part comes in. Apart from the regular Application Information and Bureau Data, a lot of third-party aggregators are offering rich data that can help in credit decisions. These third-party aggregators can be built solely on the business model of providing data to Financial Institutions or they could be selling their main business’s data to help Credit Decisioning. In either case, we get some really great ideas out of it. Some of the interesting examples are –
a) Property Data — Where all do you have the real estate, the value, size of the property, etc.
b) Phone Data — Area Code, Messages (Customer Privacy and Compliance Approved), etc.
The good news is that by day, we see more and more third-party aggregators coming into the game making data and information sorting pretty interesting.
Once we have all the information about a candidate, we are ready to make a storyline of his/her predicted performance.
Building Credit Score Card is a long game so sit tight — we are just getting started, Cheers!
Please feel free to reach out to me at firstname.lastname@example.org or linkedin — https://www.linkedin.com/in/anshika-parihar-bb69a7145/