Debt

How Technology is Shaping the Future of Consumer Credit

Published

1 year ago

October 23, 2018

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.

How Technology is Shaping the Future of Consumer 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.

Technology	Description
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:

Blockchain
Distributed, tamper-resistant databases can help secure people’s identities from fraudulent activity

Biometrics
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:

Emerging fields	Description
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.

Related Topics:apps credit fintech future privacy technologies

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Central Banks

$69 Trillion of World Debt in One Infographic

What share of government world debt does each country owe? See it all broken down in this stunning visualization.

Published

20 hours ago

November 14, 2019

Jeff Desjardins

$69 Trillion of World Debt in One Infographic

Two decades ago, total government debt was estimated to sit at $20 trillion.

Since then, according to the latest figures by the IMF, the number has ballooned to $69.3 trillion with a debt to GDP ratio of 82% — the highest totals in human history.

Which countries owe the most money, and how do these figures compare?

The Regional Breakdown

Let’s start by looking at the continental level, to get an idea of how world debt is divided from a geographical perspective:

Region	Debt to GDP	Gross Debt (Millions of USD)	% of Total World Debt
World	81.8%	$69,298	100.0%
Asia and Pacific	79.8%	$24,120	34.8%
North America	100.4%	$23,710	34.2%
Europe	74.2%	$16,225	23.4%
South America	75.0%	$2,699	3.9%
Africa	56.9%	$1,313	1.9%
Other	37.1%	$1,231	1.8%

In absolute terms, over 90% of global debt is concentrated in North America, Asia Pacific, and Europe — meanwhile, regions like Africa, South America, and other account for less than 10%.

This is not surprising, since advanced economies hold most of the world’s debt (about 75.4%), while emerging or developing economies hold the rest.

World Debt by Country

Now let’s look at individual countries, according to data released by the IMF in October 2019.

It’s worth mentioning that the following numbers are representative of 2018 data, and that for a tiny subset of countries (i.e. Syria) we used the latest available numbers as an estimate.

Rank	Country	Debt to GDP	Gross Debt ($B)	% of World Total
#1	🇺🇸 United States	104.3%	$21,465	31.0%
#2	🇯🇵 Japan	237.1%	$11,788	17.0%
#3	🇨🇳 China, People's Republic of	50.6%	$6,764	9.8%
#4	🇮🇹 Italy	132.2%	$2,744	4.0%
#5	🇫🇷 France	98.4%	$2,736	3.9%
#6	🇬🇧 United Kingdom	86.8%	$2,455	3.5%
#7	🇩🇪 Germany	61.7%	$2,438	3.5%
#8	🇮🇳 India	68.1%	$1,851	2.7%
#9	🇧🇷 Brazil	87.9%	$1,642	2.4%
#10	🇨🇦 Canada	89.9%	$1,540	2.2%
#11	🇪🇸 Spain	97.1%	$1,386	2.0%
#12	🇲🇽 Mexico	53.6%	$655	0.9%
#13	🇰🇷 Korea, Republic of	37.9%	$652	0.9%
#14	🇦🇺 Australia	41.4%	$588	0.8%
#15	🇧🇪 Belgium	102.0%	$543	0.8%
#16	Netherlands	52.4%	$479	0.7%
#17	Argentina	86.1%	$447	0.6%
#18	Singapore	113.6%	$414	0.6%
#19	Greece	184.9%	$404	0.6%
#20	Austria	73.8%	$337	0.5%
#21	Indonesia	30.1%	$308	0.4%
#22	Portugal	120.1%	$289	0.4%
#23	Poland	48.9%	$286	0.4%
#24	Switzerland	40.5%	$286	0.4%
#25	Ireland	63.7%	$244	0.4%
#26	Russian Federation	14.6%	$242	0.3%
#27	Turkey	30.2%	$233	0.3%
#28	Egypt	92.7%	$231	0.3%
#29	Pakistan	71.7%	$226	0.3%
#30	Israel	60.8%	$225	0.3%
#31	Sweden	38.5%	$214	0.3%
#32	Thailand	42.1%	$213	0.3%
#33	South Africa	56.7%	$209	0.3%
#34	Taiwan Province of China	35.1%	$207	0.3%
#35	Malaysia	55.6%	$199	0.3%
#36	Venezuela	182.4%	$180	0.3%
#37	Norway	40.0%	$174	0.3%
#38	Colombia	52.2%	$173	0.2%
#39	Finland	59.3%	$163	0.2%
#40	Saudi Arabia	19.0%	$149	0.2%
#41	Iran	32.2%	$144	0.2%
#42	Vietnam	55.6%	$134	0.2%
#43	Philippines	38.9%	$129	0.2%
#44	Denmark	34.3%	$121	0.2%
#45	Hungary	70.8%	$114	0.2%
#46	Iraq	49.3%	$111	0.2%
#47	Nigeria	27.3%	$109	0.2%
#48	Bangladesh	34.0%	$98.1	0.14%
#49	Angola	89.0%	$94.3	0.14%
#50	Qatar	48.6%	$93.0	0.13%
#51	Romania	36.7%	$87.9	0.13%
#52	Lebanon	151.0%	$85.1	0.12%
#53	Czech Republic	32.6%	$79.9	0.12%
#54	United Arab Emirates	19.1%	$79.1	0.11%
#55	Ukraine	60.2%	$78.8	0.11%
#56	Morocco	65.0%	$77.0	0.11%
#57	Chile	25.6%	$76.3	0.11%
#58	Sri Lanka	83.3%	$74.1	0.11%
#59	Sudan	212.1%	$72.7	0.10%
#60	Algeria	38.3%	$66.5	0.10%
#61	New Zealand	29.8%	$60.5	0.09%
#62	Peru	26.1%	$58.8	0.08%
#63	Puerto Rico	55.5%	$56.1	0.08%
#64	Kenya	60.1%	$52.8	0.08%
#65	Slovak Republic	48.9%	$52.1	0.08%
#66	Ecuador	45.8%	$49.6	0.07%
#67	Ethiopia	61.0%	$49.0	0.07%
#68	Croatia	74.6%	$45.4	0.07%
#69	Dominican Republic	50.5%	$43.2	0.06%
#70	Oman	53.4%	$42.3	0.06%
#71	Jordan	94.4%	$39.9	0.06%
#72	Ghana	59.3%	$38.9	0.06%
#73	Slovenia	70.4%	$38.1	0.05%
#74	Uruguay	63.5%	$37.9	0.05%
#75	Kazakhstan	21.0%	$36.3	0.05%
#76	Bahrain	94.7%	$35.7	0.05%
#77	Costa Rica	53.5%	$32.3	0.05%
#78	Tunisia	77.0%	$30.7	0.04%
#79	Belarus	47.8%	$28.5	0.04%
#80	Serbia	54.5%	$27.5	0.04%
#81	Myanmar	38.2%	$26.2	0.04%
#82	Panama	39.5%	$25.7	0.04%
#83	Cyprus	102.5%	$25.1	0.04%
#84	Côte d'Ivoire	53.2%	$22.9	0.03%
#85	Bolivia	53.8%	$21.8	0.03%
#86	Tanzania	37.3%	$21.2	0.03%
#87	Zambia	78.1%	$20.9	0.03%
#88	Kuwait	14.7%	$20.8	0.03%
#89	Guatemala	24.7%	$19.4	0.03%
#90	Lithuania	34.2%	$18.2	0.03%
#91	Syria	30.0%	$18.0	0.03%
#92	Yemen	64.8%	$17.9	0.03%
#93	El Salvador	67.1%	$17.5	0.03%
#94	Cameroon	39.1%	$15.1	0.02%
#95	Luxembourg	21.4%	$14.9	0.02%
#96	Jamaica	94.4%	$14.6	0.02%
#97	Senegal	61.6%	$14.5	0.02%
#98	Mozambique	99.8%	$14.4	0.02%
#99	Bulgaria	20.4%	$13.3	0.02%
#100	Latvia	35.9%	$12.5	0.02%
#101	Turkmenistan	29.1%	$11.9	0.02%
#102	Uganda	41.4%	$11.6	0.02%
#103	Albania	69.9%	$10.5	0.02%
#104	Uzbekistan	20.6%	$10.4	0.02%
#105	Lao P.D.R.	57.2%	$10.4	0.01%
#106	Gabon	60.7%	$10.2	0.01%
#107	Congo, Republic of	87.8%	$10.2	0.01%
#108	Trinidad and Tobago	45.1%	$10.2	0.01%
#109	Iceland	37.6%	$9.8	0.01%
#110	Honduras	40.2%	$9.6	0.01%
#111	Mauritius	66.2%	$9.4	0.01%
#112	Paraguay	21.5%	$9.0	0.01%
#113	Azerbaijan	18.8%	$8.8	0.01%
#114	Nepal	30.2%	$8.8	0.01%
#115	Papua New Guinea	35.5%	$8.2	0.01%
#116	Bahamas, The	63.3%	$7.9	0.01%
#117	Zimbabwe	37.1%	$7.8	0.01%
#118	Georgia	44.9%	$7.3	0.01%
#119	Congo, Dem. Rep. of the	15.3%	$7.2	0.01%
#120	Cambodia	28.6%	$7.0	0.01%
#121	Bosnia and Herzegovina	34.3%	$6.9	0.01%
#122	Namibia	45.8%	$6.6	0.01%
#123	Malta	45.2%	$6.6	0.01%
#124	Mali	37.3%	$6.4	0.01%
#125	Barbados	125.7%	$6.4	0.01%
#126	Armenia	51.3%	$6.4	0.01%
#127	Burkina Faso	42.9%	$6.1	0.01%
#128	Equatorial Guinea	43.3%	$5.9	0.01%
#129	Benin	41.0%	$5.9	0.01%
#130	Madagascar	45.7%	$5.5	0.01%
#131	Chad	48.3%	$5.3	0.01%
#132	North Macedonia	40.5%	$5.1	0.01%
#133	Niger	53.8%	$5.0	0.01%
#134	Nicaragua	37.2%	$4.9	0.01%
#135	Guinea	38.2%	$4.6	0.01%
#136	Kyrgyz Republic	56.0%	$4.5	0.01%
#137	Mauritania	82.9%	$4.3	0.01%
#138	Malawi	62.9%	$4.3	0.01%
#139	Togo	76.2%	$4.1	0.01%
#140	Montenegro	72.6%	$4.0	0.01%
#141	Rwanda	40.7%	$3.9	0.01%
#142	Maldives	68.0%	$3.6	0.01%
#143	Tajikistan	47.9%	$3.6	0.01%
#144	Eritrea	174.3%	$3.5	0.01%
#145	Moldova	29.7%	$3.4	0.00%
#146	Haiti	33.3%	$3.2	0.00%
#147	Bhutan	102.4%	$2.6	0.00%
#148	Sierra Leone	63.0%	$2.6	0.00%
#149	Estonia	8.3%	$2.6	0.00%
#150	Fiji	46.2%	$2.6	0.00%
#151	Suriname	72.8%	$2.5	0.00%
#152	Cabo Verde	124.5%	$2.5	0.00%
#153	Aruba	84.5%	$2.4	0.00%
#154	Botswana	12.1%	$2.3	0.00%
#155	Guyana	52.9%	$2.1	0.00%
#156	Burundi	58.4%	$2.0	0.00%
#157	South Sudan, Republic of	42.2%	$1.9	0.00%
#158	Belize	95.2%	$1.8	0.00%
#159	Eswatini	35.2%	$1.7	0.00%
#160	Antigua and Barbuda	89.5%	$1.4	0.00%
#161	Gambia, The	86.6%	$1.4	0.00%
#162	Djibouti	48.0%	$1.4	0.00%
#163	Afghanistan	6.9%	$1.4	0.00%
#164	Kosovo	17.0%	$1.4	0.00%
#165	Liberia	39.9%	$1.3	0.00%
#166	San Marino	77.9%	$1.3	0.00%
#167	Saint Lucia	64.3%	$1.2	0.00%
#168	Lesotho	44.5%	$1.2	0.00%
#169	Central African Republic	49.9%	$1.1	0.00%
#170	Guinea-Bissau	64.3%	$0.9	0.00%
#171	Seychelles	56.9%	$0.9	0.00%
#172	Grenada	63.5%	$0.8	0.00%
#173	Saint Vincent and the Grenadines	74.5%	$0.6	0.00%
#174	Saint Kitts and Nevis	60.5%	$0.6	0.00%
#175	Vanuatu	51.4%	$0.5	0.00%
#176	Samoa	50.3%	$0.4	0.00%
#177	Dominica	74.1%	$0.4	0.00%
#178	Hong Kong SAR	0.1%	$0.4	0.00%
#179	Brunei Darussalam	2.6%	$0.4	0.00%
#180	São Tomé and Príncipe	74.5%	$0.3	0.00%
#181	Comoros	21.0%	$0.2	0.00%
#182	Timor-Leste	6.1%	$0.2	0.00%
#183	Solomon Islands	9.4%	$0.1	0.00%
#184	Micronesia, Fed. States of	20.3%	$0.1	0.00%
#185	Nauru	58.3%	$0.1	0.00%
#186	Marshall Islands	25.2%	$0.1	0.00%
#187	Kiribati	20.6%	$0.0	0.00%
#188	Tuvalu	28.1%	$0.0	0.00%

In absolute terms, the most indebted nation is the United States, which has a gross debt of $21.5 trillion according to the IMF as of 2018.

If you’re looking for a more precise figure for 2019, the U.S. government’s “Debt to the Penny” dataset puts the amount owing to exactly $23,015,089,744,090.63 as of November 12, 2019.

Of course, the U.S. is also the world’s largest economy in nominal terms, putting the debt to GDP ratio at 104.3%

Other stand outs from the list above include Japan, which has the highest debt to GDP ratio (237.1%), and China , which has increased government debt by almost $2 trillion in just the last two years. Meanwhile, the European economies of Italy and Belgium check the box as other large debtors with ratios topping 100% debt to GDP.

Debt

Visualizing the Meteoric Rise of Bond ETFs

Bonds are a staple in every portfolio, but up until recently were hard to own. Here’s how bond ETFs changed that, reaching $1 trillion in global AUM.

Published

2 months ago

September 4, 2019

Jeff Desjardins

Visualizing the Meteoric Rise of Bond ETFs

Bonds are a staple in almost any investment portfolio — but up until very recently, they weren’t exactly the easiest thing to own.

Despite the bond market being bigger than the equities market, bonds mostly trade over-the-counter (OTC) and not on any centralized exchange.

In fact, traders mostly swapped bonds over the phone, negotiating prices and making deals. However, this “old school” approach came with several disadvantages, including high transaction costs, illiquidity, and a lack of true transparency in the market.

A New Way to Play

Today’s infographic comes to us from iShares, and it shows that over the last two decades, the bond market has been dramatically transformed and democratized from the “old school” approach that relied on phones, traders, and giant bond calculators.

The biggest factor in this transition: the use of exchange-traded funds (ETFs) in the bond market, which just hit a new global milestone of $1 trillion of AUM in June 2019.

Let’s look at the journey of how this rapidly rising segment of the market took off, the factors driving it, and what the future may hold for Bond ETFs.

Bond ETFs: Journey to $1 Trillion

Below is a year-by-year account of new innovations in bond ETFs, and how the usage of them has changed over time:

2002: New tech

A new financial technology, the ETF, shakes up the bond market for the first time – and the first fixed income ETFs launch in the United States.

2003: More variety

Just one year in, and there are already numerous types of bond ETFs that allow investors to fulfill different portfolio needs:

Government bond ETFs
TIPS ETFs
Corporate bond ETFs
Aggregate bond ETFs

2006: Achievement unlocked

The global bond ETF industry hits $25 billion in AUM.

2007: Bond ETF innovations

The bond ETF universe continues to expand as investors demand even more options:

Mortgage-backed security bond ETFs
Muni bond ETFs
High yield bond ETFs

2008: A new source of liquidity

Liquidity for individual bonds dries up during the 2008 Financial Crisis. However, bond ETFs step up to the plate by providing a new source of liquidity and volume increases, allowing investors to efficiently access fixed income markets.

2010: More precise strategies

The first term-maturity ETFs launch. These special bond ETFs specifically hold bonds that all mature in the exact same year.

2012: Achievement unlocked

The global bond ETF industry hits $250 billion in AUM.

2015: More product innovation

At this time, factor-based bond ETFs start to hit the mainstream. These use a rules-based approach to employ multiple investment factors, such as low volatility, quality, value, or momentum.

2016: Achievement unlocked

The global bond ETF industry hits $500 billion in AUM.

2017: Green bonds

Green bonds ETFs provide investors with the ability to invest in bonds that are tied to sustainability purposes.

2018: Market volatility and bond ETFs

In the second half of 2018, markets get volatile and investors turn to bond ETFs to help reduce their overall portfolio risk, specifically diversifying their exposure to stocks.

2019: Achievement unlocked

The global bond ETF industry hits $1 trillion in AUM, with now over 1,300 bond ETFs available.

The Path to $2 Trillion?

In just 17 years, bond ETFs have grown to be a significant part of the investment universe, reaching $1 trillion AUM in 2019.

Impressively, it won’t likely take long to double the last milestone. According to BlackRock, it’s anticipated that ETFs will hold $2 trillion in AUM by the year 2024 — just a few short years down the road.