Infographic: How Tech is Changing the Modern Credit Landscape
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How Tech is Changing the Modern Credit Landscape

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From the beginnings of General Motors Acceptance Corporation to the introduction of the Diner’s Club charge card, the history of credit has been filled with game-changing innovations.

Today, new innovations in tech are continuing to shape the consumer credit industry – and with U.S. consumer debt sitting at $13 trillion, these changes could play a role in impacting how consumers access credit both today and in the future.

The Modern Credit Landscape

Today’s infographic comes to us from Equifax, and it gives a snapshot of modern credit as well as a perspective on how new technologies such as trended and alternative data are changing the landscape.

It’s the second part of our ongoing three-part series on credit:

Part 1: The History of Consumer CreditPart 2: Modern CreditPart 3: Future
How Tech is Changing the Modern Credit Landscape
Part 1: The History of Consumer CreditPart 2: Modern CreditPart 3: Future

Credit scores play a massive component of consumer life, and they are used to gauge creditworthiness for big purchases ranging from homes to launching a business.

Interestingly, how this scoring works is not at all static – and new technology is being applied to increase accuracy as well as open credit up to more consumers throughout society.

Traditional Credit Scoring

The modern numeric credit score emerged in 1989, and it uses logistic regression to make informed decisions on a consumer’s creditworthiness.

The scoring model is made up of five distinct categories:

CategoryPercentageDescription
Payment History35%Are scheduled payments made on time?
Debt Burden30%Includes multiple factors such as number of accounts with balances, amounts owed, and debt-to-limit ratio.
Length of Credit History15%Average age of accounts and age of oldest account.
Types of Credit Used10%What type of credit is used? (i.e. revolving, installments, etc.)
New Credit Requests10%Hard new credit inquiries can hurt scores.

But this model does have its limitations. For example, traditional credit scores give a snapshot of credit rather than showing how the “big picture” of a person’s credit is changing. Further, current scores can also can be inhibited by a lack of data, resulting in an inaccurate representation of a person’s credit.

Tech to the Rescue

On a global basis, the data universe is doubling every two years – and this abundant new resource is revolutionizing consumer credit.

Trended Data
Instead of looking at a snapshot of a credit score, it’s possible to analyze the direction, velocity, tipping points, and magnitude of changes in a consumer’s credit history to get a bigger, more accurate picture. This is called trended data, and it can offer up to 20% improvement in predictive performance.

Alternative Data
Credit history is important, but there are increasingly other sources of data that can provide a view of a consumer’s creditworthiness. Alternative data taps into information on property ownership, wealth, how customers pay everyday bills, and other data sources to provide a more well-rounded picture.

Other Tech
Technology has given consumers unprecedented access to their credit data – and in the meantime, new science behind neural networks is being implemented to give even more sophisticated scoring capabilities.

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Infographic: Generative AI Explained by AI

What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.

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Generative AI Explained by AI

After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.

We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.

Everything in the infographic above – from illustrations and icons to the text descriptions⁠—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.

Without further ado, generative AI as explained by generative AI.

Generative AI: An Introduction

Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.

Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.

Generative AI has a wide range of applications, including:

  • Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
  • Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
  • Audio: Generative AI can generate new music tracks, sound effects, and even voice acting

Disrupting Industries

People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.

Generative AI has the potential to disrupt several industries, including:

  • Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
  • Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
  • Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences

Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.

In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.

How This Article Was Created

This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.

In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.

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