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.
Visualizing the Snowball of Government Debt
See the latest levels of government debt, based on the IMF’s most recent data. Where does your country sit in the snowball?
Visualizing the Snowball of Government Debt
Over the last five years, markets have pushed concerns about debt under the rug.
While economic growth and record-low interest rates have made it easy to service existing government debt, it’s also created a situation where government debt has grown in to over $63 trillion in absolute terms.
The global economic tide can change fast, and in the event of a recession or rapidly rising interest rates, debt levels could come back into the spotlight very quickly.
The Debt Snowball
Today’s visualization comes to us from HowMuch.net and it rolls the world’s countries into a “snowball” of government debt, colored and arranged by debt-to-GDP ratios. The data itself comes from the IMF’s most recent October 2018 update.
The structure of the visualization is apt, because debt can accumulate in an unsustainable way if governments are not proactive. This situation can create a vicious cycle, where mounting debt can start hampering growth, making the debt ultimately harder to pay off.
Here are the countries with the most debt on the books:
|Rank||Country||Debt-to-GDP Ratio (2017)|
Note: Small economies (GDP under $10 billion) are excluded in this table, such as Cabo Verde and Barbados
Japan and Greece are the most indebted countries in the world, with debt-to-GDP ratios of 237.6% and 181.8% respectively. Meanwhile, the United States sits in the #8 spot with a 105.2% ratio, and recent Treasury estimates putting the national debt at $22 trillion.
On the opposite spectrum, here are the 10 jurisdictions that have incurred less debt relative to the size of their economies:
|Rank||Country||Debt-to-GDP Ratio (2017)|
|#2||Hong Kong (SAR)||0.1%|
Note: Small economies (GDP under $10 billion) are excluded in this table, such as Timor-Leste and Solomon Islands
Macao and Hong Kong – both special administrative regions (SARs) in China – have virtually zero debt on the books, while the official country with the lowest debt is Brunei (2.8%).
How Tech is Changing the Modern Credit Landscape
The traditional credit score is becoming obsolete – and now, big data and new tech are already starting to shape the modern credit landscape.
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:
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:
|Payment History||35%||Are scheduled payments made on time?|
|Debt Burden||30%||Includes multiple factors such as number of accounts with balances, amounts owed, and debt-to-limit ratio.|
|Length of Credit History||15%||Average age of accounts and age of oldest account.|
|Types of Credit Used||10%||What type of credit is used? (i.e. revolving, installments, etc.)|
|New Credit Requests||10%||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.
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.
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.
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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