Technology
Visualizing the Evolution of Consumer Credit
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:
- Growing amounts of data
- A changing regulatory landscape
- Game-changing technologies
- Focus on identity
- 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.
Technology
Charted: The Jobs Most Impacted by AI
We visualized the results of an analysis by the World Economic Forum, which uncovered the jobs most impacted by AI.
Charted: The Jobs Most Impacted by AI
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
Large language models (LLMs) and other generative AI tools haven’t been around for very long, but they’re expected to have far-reaching impacts on the way people do their jobs. With this in mind, researchers have already begun studying the potential impacts of this transformative technology.
In this graphic, we’ve visualized the results of a World Economic Forum report, which estimated how different job departments will be exposed to AI disruption.
Data and Methodology
To identify the job departments most impacted by AI, researchers assessed over 19,000 occupational tasks (e.g. reading documents) to determine if they relied on language. If a task was deemed language-based, it was then determined how much human involvement was needed to complete that task.
With this analysis, researchers were then able to estimate how AI would impact different occupational groups.
Department | Large impact (%) | Small impact (%) | No impact (%) |
---|---|---|---|
IT | 73 | 26 | 1 |
Finance | 70 | 21 | 9 |
Customer Sales | 67 | 16 | 17 |
Operations | 65 | 18 | 17 |
HR | 57 | 41 | 2 |
Marketing | 56 | 41 | 3 |
Legal | 46 | 50 | 4 |
Supply Chain | 43 | 18 | 39 |
In our graphic, large impact refers to tasks that will be fully automated or significantly altered by AI technologies. Small impact refers to tasks that have a lesser potential for disruption.
Where AI will make the biggest impact
Jobs in information technology (IT) and finance have the highest share of tasks expected to be largely impacted by AI.
Within IT, tasks that are expected to be automated include software quality assurance and customer support. On the finance side, researchers believe that AI could be significantly useful for bookkeeping, accounting, and auditing.
Still interested in AI? Check out this graphic which ranked the most commonly used AI tools in 2023.
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