What is a risk score and what is a credit score?

What is a Risk Score and what is a Credit Score? It is important to know these concepts before you start building an ML model for credit risk scoring.

What is a Risk Score?

A risk score is a mathematical score. It is based on individual risk factors.

The risk score assesses the risks that are present naturally in the specific object/service being scored.

For example, your bank may have revealed to you that you have an astounding credit score. This makes you qualified for a higher credit cutoff or uncommon proposals/offers on its financial products.

Another example, an organization might be not able to raise the necessary assets/funds or be ineligible to take part in a contract tender. In this case, it is corporate/bond credit rating that isn’t sufficient or up to the mark.

People working in the financial administration industry know about these situations.

Regardless of whether you are an individual, a startup, or an entrepreneur, you would have known about risk scores.


While applying for credit, raising money, or some kind of risk evaluation. Organizations typically assign risk scores to their counterparties, items & different exercises as part of their internal risk management.

What we have seen above is the functional utilization of the risk scores in the business. We use a standard strategy to derive risk scores.

This methodology has various individual risk elements. We assess each individual risk element/factor. We frequently grouped them into explicit classes for simplicity of reference and use.

Risk Scoring Process

The final risk score is an impression of the subject’s risk. It depends on the scored risk factors. We use it to look at and risk-rank numerous subjects against one another.

Generally, we create a risk scoring system for a specific use case. We should not reuse a particular approach in other disconnected situations.

Without a standard risk scoring model/scorecard, the stakeholders will struggle to make consistent and objective decisions for the business.

A risk score, by its design, is expected to be not difficult to utilize and execute. It should be highly interpretable, significant & actionable.

The risk scoring process recognizes the risk factors that you need to assess a subject against.

These risk elements can be interior or outer. It can be subjective or quantitative. It can be strategic or operational.

We remember the final short-listed risk factors for the scorecard

Generally, we assign a mathematical score (weight) to each factor. We assign higher weights to factors that are more significant or critical or important.

Generally, the expert scores each factor. They use documented standards or their understanding of the risk.

The final score can be a straightforward sum of the individual factor scores. It can also be a weighted average of all the individual factor scores.

We use the final risk score for dynamic decision-making purposes. For instance, the top 15% of the scores can be delegated Low Risk and the base 5% as High Risk.

Financial services organizations depend intensely on their inner risk scoring instruments. Generally, they use risk scores to:

  • Risk-rank potential borrowers
  • Estimate and price
  • Drive dynamic decision-making purposes

Other examples:

  • We use safety and security risk scores to assess the effects of potential situations.
  • We use risk scores to classify and predict the danger of having a specific ailment/disease.

What is a Credit Score?

A credit score is a mathematical score. We determine it through measurable examination. It addresses a client’s reliability. For example, its ability to repay credit commitments in a timely manner.

Thus, the separating factor between a risk score and a credit rating is:

  • The risk score can assess possibly any kind of risk
  • The credit score’s target is to assess a subject’s reliability/creditworthiness

The higher a client’s credit score, the higher will be its reliability and the lower will be the risk of credit default.

In the consumer market, we consider a consumer’s credit score to measure his/her lending eligibility.

In fact, financial assessments are currently utilizing large information & AI calculations.

Credit assessments are broadly used by financial institutions as a standard component to qualify likely borrowers. They order borrowers as indicated by the interior risk rules.

What is Credit Risk Modelling?

Credit risk modeling refers to data-driven risk models. It calculates the chances of a borrower defaulting on a loan (or credit card). If a borrower fails to repay the loan, how much amount he/she owes at the time of default? Moreover, how much the lender would lose from the outstanding amount?

As noted before, the factors and loads of any credit scoring model may vary across models.

We normally use the following variables for a credit scoring model:

  • Reimbursement history: “If the sum due was settled in time or not”
  • The aggregate sum owed: “Total outstanding balance, usually as a percentage of the total credit limit”
  • Length of credit record: “When was the primary credit line opened?”
  • Sorts of credit lines: “Does the purchaser have just a single sort of credit line (e.g., just a home loan)? Does the purchaser have a decent blend of retail advances, credit cards, home loans, and mortgages?”
  • New credit lines: “Number of new credit lines opened in a short measure of time” is, by and large, looked ominous.
  • Accessible credit: “Unutilized balance on open credit lines”

As noted before, banks broadly use credit scores in their loaning and estimating choices.


What is a Risk Score and what is a Credit Score? : Hope, this post has helped in answering this question.

Specifically, you covered the following:

  1. Firstly, we learned about risk scores
  2. Secondly, we understood the risk-scoring process
  3. Then, we extended our understanding of risk scores and learned about credit score
  4. Lastly, we learned about credit risk modeling (and a list of variables commonly used).

Check out the table of contents for Product Management and Data Science to explore those topics.

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