Consumer credit has been constantly evolving for more than 5,000 years, but the reality is that the most drastic changes to the industry came fairly recently.
Modern credit systems are now powered by sophisticated algorithmic credit scoring, the use of trended and alternative data, and innovative fintech applications. While these developments are all interesting in their own right, together they serve as a technological foundation for a much more profound shift in consumer credit in the coming years.
The Future of Consumer Credit
In today’s infographic from Equifax, we look at the cutting edge of consumer credit, including the new technologies and global trends that are shaping the future of how consumers around the world will access credit.
It’s the final piece of our three-part series covering the past, present, and future of credit.
The biggest problem that creditors have always faced is well-documented. There is more to a borrower than just their credit score. Yet creditors do not always have a 360 degree view of a consumer’s creditworthiness in order to better assess their overall score.
Called “information asymmetry”, this gap has gotten smaller over the years thanks to advancements in technology and business practices. However, it still persists in particular situations, like when a college student has no credit history, or when a rural farmer in India wants to take out a loan to buy seeds for crops.
But thanks to growing amounts of data – as well as the technology to make use of that data – high levels of information asymmetry may soon be a thing of the past.
Forces Shaping Credit’s Future
Here are some of the major forces that will drive the future of consumer credit, addressing the information asymmetry problem and making a wide variety of credit products available to the public:
1. Growing Data
90% of the data in all of human history has been created in just the last two years.
2. Changing Regulatory Landscape
New international regulations are putting personal data back in the hands of consumers, who can control the personal data they authorize access to.
3. Game-changing Technologies
Machine learning, deep learning, and neural networks are giving companies a way to garner insights from data.
4. Focus on Identity
Authenticating the identity of consumers will become crucial as credit becomes increasingly digital. Blockchain and biometrics could play a role.
5. The Fintech Boom
The democratization of data and tech is allowing small and niche players to come in and offer new, innovative products to consumers.
The Credit Revolution
No one can predict the future, but the above forces are shaping the credit industry to be a very different experience for consumers and businesses. Here are how things could change.
More Data, New Models
Current credit scoring algorithms use logistical regressions to compute scores, but these really max out at using 30-50 variables. In addition, these models can’t “learn” new things like AI can.
However, with new technologies and an unprecedented explosion in data taking place, it means that this noise can be converted into insights that could help increase trust in the credit marketplace. New algorithms will be multivariate, and they will be able to mine, structure, weight, and use this treasure trove of data.
|Artificial intelligence||Machine learning can “learn” from massive data sets, and apply these lessons for better scoring.|
|Bayesian||Models can update probabilities as more information is available, helping to better predict creditworthiness.|
|APIs||Application programming interfaces (APIs) make it easier for developers to use technologies, data, and to build new applications.|
|Neural networks||Brain-inspired AI systems designed to replicate the way that humans learn are used for deep learning. This enables the processing of raw, unstructured, and often abstract data for new insights.|
Neural networks will be able to look at a billions of data points to find and make sense of extremely rare patterns. They will also be able to explain why a particular decision was made – and at a time where transparency is crucial, this will be key.
Data Will be in the Hands of Consumers
Today, much of consumers’ financial data – such as loan repayment histories – is held almost exclusively by banks and credit agencies.
However, tomorrow points to a very different paradigm: much of the data will be directly in the hands of consumers. In other words, consumers will be able to decide how their data gets used, and for what. In Europe, changes have already been made to transfer control of personal data to the consumer, such as the PSD2, GDPR, and Open Banking (U.K.) initiatives.
Experts see the trend towards open data growing globally, and eventually reaching the United States. Open data will allow consumers to:
- Regain control of checking, mortgage, loan, and credit card data
- Give up more information voluntarily to unlock better deals from creditors
- Grant access to third parties (fintech, apps, etc.) to use this data in new applications and products
- Gain access to better rates, new lending models, and more
Identity Will Be Just as Important
As transactions become more digital and remote, how lenders verify the identity of borrowers will be just as important as the lending data itself.
Why? Credit is based around trust – and fraud is the biggest risk for lenders.
But fraud an be prevented by new technologies that help detect anomalies and prove a borrower’s identity:
Distributed, tamper-resistant databases can help secure people’s identities from fraudulent activity
Fingerprints, facial recognition, and other biometric identification schemes could help secure identities as well
New Game, New Players
With the vast expansion in types and volume credit data, new technologies, and standardized data in the hands of consumers, there will be a new era of third-party companies and apps that can provide useful and relevant services for consumers.
Here are just some emerging fields in lending:
|P2P Loans||Does a bank need to be an intermediary?
With peer-to-peer loans, you are matched to an appropriate lender/borrower.
|Microlending||Lending doesn’t always need to be in big amounts, like for a mortgage or auto loan.|
|Alternative credit scoring||Psychometric testing or the use of other data streams can be used to power this less traditional form of lending.|
|Niche services||With an open playing field, companies will fill every gap imaginable.|
In the future, consumers may not have to even request credit – it may be automatically allocated to them based on behavior, age, assets, and needs.
Consumers will have more control, and more options than ever before.
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.
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
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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