Building Great Credit Scoring Models
Tags: datascience, creditscoring, businessinteligence, analytics
Published: 2023-07-27 21:19:32

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title: Building Great Credit Scoring Models
published: true
description: Credit scoring models are extremely important for analyzing credit agencies that evaluate customers' credit worthiness. This document covers the fundamental features of building a great credit scoring model.
tags: #datascience #creditscoring #businessinteligence #analytics
cover_image: https://i.imgur.com/Faxf3af.jpeg
# Use a ratio of 100:42 for best results.
# published_at: 2023-07-27 20:58 +0000
---

Granting credit is a decision taken under conditions of uncertainty. In loans, installment sales, provision of services, etc., there is always the possibility of losing the amount borrowed. If a creditor is able to estimate the probability of a certain loss occurring, the decision-making process becomes more assertive, reducing possible losses. Because of this, several companies and financial institutions invest in Credit Scoring models. These models aim to predict, on the date of the decision, a possible granting of credit, if the granted operation implies losses for the creditor. The probability of this happening is called credit risk in the industry.

Therefore, Credit Score is a measure of credit risk. Credit scoring models is the market's common generic determination for credit score calculation formulas.
Quantitative Credit Score

The risk of a credit application can be assessed subjectively or measured objectively through quantitative analysis methodologies. The subjective assessment, despite incorporating the experience of a financial analyst, does not quantify credit risk. It is necessary to accurately estimate possible losses and expected gains for each operation and consequently make a decision. This measurement using quantitative methods has some advantages:

## Consistency in Decisions
If we submit the same credit operation to different analysts, different subjective assessments can be obtained, as the experience and engagement with the customer differ between them. Furthermore, the same financial analyst may give different assessments to the same proposal if it is presented at different times. We humans are like that. However, this does not occur when using a quantitative Credit Scoring model. Keeping the initial characteristics of the proposal unchanged, the calculated score will always be the same.

## Quick Decisions
The computational resources available today allow the score calculation to be computed almost instantly, right after data registration for a given request has been carried out. Hundreds or thousands of decisions can be made in just one day, with security and consistency. This short customer response time allows investment banks to have a competitive advantage.

## Remote Decisions
Currently, with the data transmission resources available, the lender does not need to allocate a financial credit analyst in each office or in each branch. The credit seller can record the data at the point of sale, and after sending that data, the model can almost instantly calculate the customer's credit score.

## Portfolio Monitoring
The quantification of individual risks allows continuous monitoring of the analysis of credit portfolios. This, in addition to ensuring the security of the portfolio's cash flow, also allows analyzing trends within the institution itself, an important step in building forecasting models.

## Credit Scoring Model
The idea behind credit scoring techniques is simple. Suppose that, in car financing, the lender analyzes only three characteristics of the applicants. The type of residence at the date of the request, whether the applicant has debts and whether the vehicle subject to the credit operation is new or used. As the number of variables increases, the complexity of the analysis increases. In this case, analysis is only viable with the help of advanced quantitative techniques.

The effectiveness of a credit scoring model directly depends on the information used to assess customer and transaction risks. Choosing this information correctly is extremely important for obtaining a good model.

We initially identified a set of predictor variables that we believe have the potential to discriminate whether a customer is eligible to receive a loan. These variables are used in the score calculation formula.

One way to begin identifying potential variables is by analyzing available databases. When analyzing these variables, ideas may arise to combine two or more of them to generate a new variable that provides information for the model. In large banks, customer and transaction data are commonly stored in different databases.
When identifying the team of experts, keep in mind the objective you hope to achieve with the Credit Scoring model. Data that can differentiate whether a client is able to honor their debts must be analyzed.
In general, you can classify information by following descriptions:

# Sociodemographic data of the applicant
Sociodemographic information of the spouse or partner
Applicant's financial information
Lender Relationship Information
Behavioral information

## Legal concerns
The first point concerns legal certainty. It is important to inform the customer that when requesting credit, they are authorizing consultation with credit protection bodies. Furthermore, the information obtained is confidential and should not be disclosed or shared under any circumstances, restricting its use exclusively to support the granting of credit.

This conduct should be done as a good practice in your company's routines. After all, it is customer data that can be exposed to criminal action. To do this, you need to train your team well, implement security tools and use secure applications.

Financial management software includes technologies that strengthen information security, such as cloud computing and blockchain (anchor blocks). It is worth remembering that, in isolation, the credit score is not a parameter for granting, its information needs to be cross-referenced with internal data from its customers, evaluating their payment capacity and their relationship with the company in order to reduce default rates.

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