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Visualizing the Evolution of Consumer Credit

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The origin of credit dates all the way back to ancient civilizations.

The Sumerians and later the Babylonians both used consumer loans in their societies, primarily for agricultural purposes. The latter civilization even had rules about maximum lending rates engraved in the famous Code of Hammurabi.

But since then, consumer credit — and how we calculate creditworthiness — has gotten increasingly sophisticated. This is so much the case that technology now used in modern credit scoring would seem completely alien to people living just a few decades ago.

Video: Consumer Credit Through the Ages

Today’s motion graphic video is powered by Equifax, and it shows the evolution of consumer credit over the last 5,000 years.

The video highlights how consumer credit has worked both in the past and in the present. It also dives into the technologies that will be shaping the future of credit, including artificial intelligence and the blockchain.

A Brief History of Credit

We previously visualized the 5,000-year history of consumer credit, and how it dramatically changed over many centuries and societies.

What may have started as agricultural loans in Sumer and Babylon eventually became more ingrained in Ancient Roman society. In the year 50 B.C., for example, Cicero documented a transaction that occurred, and wrote “nomina facit, negotium conficit” — or, “he uses credit to complete the purchase”.

Modern consumer credit itself was born in England in 1803, when a group of English tailors came together to swap information on customers that failed to settle their debts. Eventually, extensive credit lists of customers started being compiled, with lending really booming in the 20th century as consumers started buying big ticket items like cars and appliances.

Later, the innovation of credit cards came about, and in the 1980s, modern credit scoring was introduced.

The Present and Future of Credit

Learn about the modern credit landscape, as well as how technology is changing the future of consumer credit.

The modern numeric credit score came about in 1989, and it uses logistic regression to assess five categories related to a consumer’s creditworthiness: payment history, debt burden, length of credit history, types of credit used, and new credit requests.

However, in the current era of big data and emerging technologies, companies are now finding new ways to advance credit models — and how these change will affect how consumers get credit in the future.

Modern Tech

Consumer credit is already changing thanks to new methods such as trended data and alternative data. These both look at the bigger picture beyond traditional scoring, pulling in new data sources and using predictive methods to more accurately encapsulate creditworthiness.

Future Tech

In general, the future of credit will be shaped by five forces:

  1. Growing amounts of data
  2. A changing regulatory landscape
  3. Game-changing technologies
  4. Focus on identity
  5. The fintech boom

Through these forces, new credit models will integrate artificial intelligence, neural networks, big data, and more complex statistical methods. In short, credit patterns can be more accurately predicted using mountains of data and new technologies.

Finally, the credit landscape is set to shift in other ways, as well.

Regulatory forces are pushing data to be standardized and controlled directly by consumers, enabling a range of new fintech applications to benefit consumers. Meanwhile, the industry itself will be focusing in on identity to build trust and limit fraud, using technologies such as biometrics and blockchain to prove a borrower’s identity.

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AI

Visualizing the Top U.S. States for AI Jobs

Nearly 800,000 AI jobs were posted in the U.S. throughout 2022. View this graphic to see a breakdown by state.

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Visualizing the Top U.S. States for AI Jobs

Much ink has been spilled over fears that artificial intelligence (AI) will eliminate jobs in the economy. While some of those fears may be well-founded, red-hot interest in AI innovation is creating new jobs as well.

This graphic visualizes data from Lightcast, a labor market analytics firm, which shows how many AI-related jobs were posted in each state throughout 2022.

In total there were 795,624 AI jobs posted throughout the year, of which 469,925 (59%) were in the top 10. The full tally is included in the table below.

RankStateNumber of job postings% of total
1California142,15417.9%
2Texas66,6248.4%
3New York43,8995.5%
4Massachusetts34,6034.3%
5Virginia34,2214.3%
6Florida33,5854.2%
7Illinois31,5694.0%
8Washington31,2843.9%
9Georgia26,6203.3%
10Michigan25,3663.2%
11North Carolina23,8543.0%
12New Jersey23,4472.9%
13Colorado20,4212.6%
14Pennsylvania20,3972.6%
15Arizona19,5142.5%
16Ohio19,2082.4%
17Maryland16,7692.1%
18Minnesota11,8081.5%
19Tennessee11,1731.4%
20Missouri10,9901.4%
21Oregon10,8111.4%
22Washington, D.C.9,6061.2%
23Indiana9,2471.2%
24Connecticut8,9601.1%
25Wisconsin8,8791.1%
26Alabama7,8661.0%
27Kansas7,6831.0%
28Arkansas7,2470.9%
29Utah6,8850.9%
30Nevada6,8130.9%
31Idaho6,1090.8%
32Oklahoma5,7190.7%
33Iowa5,6700.7%
34South Carolina4,9280.6%
35Louisiana4,8060.6%
36Kentucky4,5360.6%
37Nebraska4,0320.5%
38Delaware3,5030.4%
39New Mexico3,3570.4%
40Rhode Island2,9650.4%
41New Hampshire2,7190.3%
42Hawaii2,5500.3%
43Mississippi2,5480.3%
44Maine2,2270.3%
45South Dakota2,1950.3%
46Vermont1,5710.2%
47North Dakota1,2270.2%
48Alaska9700.1%
49West Virginia8870.1%
50Montana8330.1%
51Wyoming7690.1%

The following chart adds some context to these numbers. It shows how the percentage of AI job postings in some of the top states has changed since 2010.

We can see that California quickly became the primary destination for AI jobs in the early 2010s, presumably as Silicon Valley companies began developing the technology.

California’s share has since declined, with a significant number of jobs seemingly moving to Texas. In fact, many tech companies are relocating to Texas to avoid California’s relatively higher taxes and cost of living.

The 10 Most In-Demand Specialized Skills

Lightcast also captured the top 10 specialized skills that were required for AI-related jobs. These are listed in the table below.

SkillFrequency (number of postings)Frequency (% of postings)
Python296,66237%
Computer Science260,33333%
SQL185,80723%
Data Analysis159,80120%
Data Science157,85520%
Amazon Web Services155,61519%
Agile Methodology152,96519%
Automation138,79117%
Java133,85617%
Software Engineering133,28617%

If you’re interested in a career that focuses on AI, becoming proficient in Python is likely to be a good first step.

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