A Discussion Paper on the Grey Area – The Ethical Problems Related

to Big Data Credit Reporting

Victor Chang and Jing Lin

International Buiness School Suzhou, Xi’an Jiaotong-Liverpool Universiy, Suzhou, China

Keywords: Big Data Credit Reporting, Ethical Concerns for Big Data, Controversies on Big Data Uses.

Abstract: With the rise and the development of the “credit society”, the credit reporting has played a central role in

evaluation one’s credit statues, including monitoring and updating creditworthiness of individuals. As the

emergence of big data, new tools enabling the credit reporting system to develop new level, by collecting the

online and offline data to establish more completely score system. This review paper is aimed to present the

difference between the new big data credit reporting and traditional credit reporting, and then explain

advantages offered by the new data management. Subsequently, ethical problems will be described due to

rising concerns. Being “kidnapped” by the credit reporting applications, users’ data will be collected and

disposed without prior permission. Some data processes may arise with the messy, unreasonable and fake data

resource problems to add more complexities to the existing services which are unable to cope with. As a

result, individual users could not verify the correctness of the data and did not know which data would be

more trustworthy to be verified for payment and billing. To be worse, users even do not know how to improve

their creditworthiness if they have done everything correctly. There are some issues about precision

marketing, since some data brokers will target the individuals who was vulnerable to the non-performing and

short-term loans. Last but not least, the algorithm of big data prone to evaluate the credit score by groups that

individual related to, rather than the individual’s own merits, which may lead to discrimination issue, and

accelerate the wealth gap problem.

1 INTRODUCTION

Every citizen in USA have a unique society security

number, which recorded all the credit activities,

include pay off the credit card debt, apply for the

mortgage or student loans (Komuves, 1997). There

are three main credit bureaus in the American,

Equifax, Experian and TransUnion, they are all

owned and operated by publicly companies, not

owned by any governments. According to your credit

history, amounts owed and debts on records, they will

generate an individual credit reporting, it can be

corresponded to your society security number, which

means once you were recorded, it would not

disappear. With this credit reporting, you can apply

for bank loans or jobs. If you were identified as an

individual with a poor credit record, the bank will

reject to give you loans and the employer will reject

you either. Before the wide adoption of big data

technology, the credit reporting is mainly used in the

financial field to evaluate applicants’ ability to repay

their loans (Mierzwinski and Chester, 2013), with

more social data and information, the credit reporting

system grew into the creditworthiness evaluation

system, which can be used in all aspects of life

(Miller, 2000). One of the big data reporting strengths

is the huge amount data resources that it can get

connected, queried and linked altogether. Another

strength is the machine learning and AI technologies

to support large scale data processing, analysis,

categorization and management. Unlike the

traditional data resource, the ‘thin-file’ customers

who have less credit history due to age, immigrant

status or recent credit record lacking, will get a fair

credit score in big data credit reporting (Bureau,

2012). Using the big data services, which can

combine as much as relevant credit data, such as the

social data, property and online activities. By cross

checking to find whether there are no bad records, the

thin-file customers can get loans by a lower rate.

However, as an idiom has rightly pointed out - the

water that bears the boat can be the same that

swallows it up. The rise of quantity, variety and

complexity in data can lead to chaos, if it is not

348

Chang, V. and Lin, J.

A Discussion Paper on the Grey Area – The Ethical Problems Related to Big Data Credit Reporting.

DOI: 10.5220/0006823603480354

In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 348-354

ISBN: 978-989-758-296-7

properly managed. In the data collection and

transformation, most people will face the all-or-

nothing decision. The user will not have access to

services if he/she rejects to upload the private

information, such as locations, or delivery addresses.

Although some application suppliers announce that

the application will not use the customers’ data for

profits, their authenticity is debatable. There are

already some cases of erroneous data problems, two

women’s credit reporting have been swapped because

they have similar personal information. In addition,

some researchers founded that there exist the

fabricated issues in the data input procedures. While

putting the subjective views aside, data problem may

be structured because of a certain level of error rate in

model operations (Pasquale, 2015). The Federal

Trade Commission have showed in the report that the

error rates up to 26%. The structural problem also

related to the machine learning processes, due to the

complex algorithms, the machine will decide how

much attention an emphasis it needs to give and

dynamically adjust the system itself regularly. It

would be difficult to perform manual regulations due

to millions or billions of users involved.

Additionally, machine learning and AI techniques

may not be robust enough to provide secure services.

Some developers can use the tools to aim at the

vulnerable, high-value targets for non-performing

loans, just like sell heroin to drug addicts

(Mierzwinski and Chester, 2013). Furthermore, the

machine learning can also assess the individual’s

credit situations by evaluating their circles of friends,

groups and communities of politics, religions, and

others. Instead of evaluating the individual’s own

merits, it is unfair to the vulnerable groups. As the

situation continues to deterioration, the wealth gap

will be widened, for the wealthy people will get more

service and better remedial measure to keep their

credit reporting more dignity.

2 LITERATURE REVIEW FOR

BIG DATA CREDIT

REPORTING

Hilda et al. (2016) indicated the benefit of big data

used in the credit reporting field, in his review, he

illustrated some new data processing methods, which

can be profitable in business. Singer (2012) explain

some new data source, such as the web browsing data

or user purchase records, can be used in the credit

evaluation process. The new data added in can help

the thin-file people get reasonable evaluation

(Bureau, 2012). While in the data collection, user will

be forced to make an all-or-nothing choice between

the acceptance of free services or refuse to be

recorded (Bilogrevic et al., 2014). The Federal Trade

Commission (2013) also points out that credit score

counting process with machine learning contain a

certain percentage of errors. Jacobs (2015) manifests

that the online data has inaccuracy problems. Hurley

and Adebayo (2016) claims that individuals could not

improve their credit score due to the non-transparent

issue. Furthermore, some “precision marketing” will

target the people who were vulnerable to the high

interest rates but short-term loans, which will indulge

the high-risk borrowers (Trusts, 2014). As the

machine will be prone to assess the individual’s

credits by the group-referential processing, the

discrimination issue will be deteriorated (Meyer,

2015). When such vicious cycles repeat more often,

wealth gap will be widened (Hurley and Adebayo,

2016).

Before 1980s, lending a loan is decided by

individual loan officers and specialists who assess

applicants, which can differ from person to person

(Citron and Pasquale, 2014). Then, the decision for

lending based on the automated credit scoring

systems developed by the Fair and Isaac

Corporation(FICO). Moreover, the credit scoring is

more useful, it uses sum of a person's apparent

creditworthiness to make underwriting decisions,

similar to "predict the relative likelihood of a negative

financial event, such as a default on a credit

obligation." (Yu, 2014). In America today, every

citizen has equipped with one unique social security

number, which record all the credit activities, such as

paying off credit card and loan, repaying the

education loans. Customers can get their credit

reporting from three main credit bureaus in America,

Equifax, Experian and TransUnion, they both

publicly-traded, not owned by government. For the

most of Americans, without a good credit score will

cause many inconveniences, such as fail to get a

mortgage, fired by employer, or even lose the

opportunity for education. As a result, it is essentially

important to pay attention to individual credit score.

Nowadays, Hilda et al. (2016) claimed that use of

big data has been adopted by more organizations.

This can be done by identifying patterns, clusters and

outliers that are not obvious by the traditional

methods. The ultimate goal is to bring profits to

business. As the big data technology has becomes

more mature, it can handle the massive amounts data

with high speed. The field of credit reporting has been

reforming.

A Discussion Paper on the Grey Area – The Ethical Problems Related to Big Data Credit Reporting

349

2.1 The Big Data Credit Reporting

Different from Traditional One

2.1.1 Data Source

The big data credit reporting is different from the

traditional one, the first part is data source, the big

data credit reporting can utilize the different kinds of

non-traditional data, which include the criminal

records, social media data, consumers’ retail

spending history, the online purchase records,

internet browsing history, or even an individual's

friend circle on social networking board (Singer,

2012). Although the traditional data sources still have

the basic of credit system, the new alternative tools

equipped with the big data has rapidly occupying the

market. Like the Experian, it has used big data to

exploit thousands of ‘universal customer profiles’

that can integrate both the online and offline data

(Tewksbury, 2013). As same as the Fair and Isaac

Corporation(FICO) the main planner of lending

decisions has exploit a new system combined with

non-traditional data to assess the thin-file borrowers,

for they lack the traditional data and difficulty to

evaluate. The new ‘FICO Score XD’ is the result of

the cooperation between the FICO and credit bureau

Equifax, they use the cable and mobile phone

accounts to predict the consumers’ creditworthiness

(Carrns, 2015). Nowadays, the most remarkable

credit scoring and underwriting company is the

ZestFinance, who helps two groups of people getting

loans. One group includes the people who cannot

meet the basic requirements of lending because their

FICO score is less than the 500, and other group who

have high loan cost and low credit score. It uses the

proprietary algorithms to analyse several thousand

data points per individual in order to arrive at a final

score (Lohr, 2015).

2.1.2 Machine Learning

The new method adopted by ZestFinance was based

on the machine learning, which is also the different

part with the traditional credit reporting system.

Machine learning is a method which can

automatically explore the data patterns and used the

pattern to predict the future data (Robert, 2012).

There are two styles of machine learning, supervised

and unsupervised. The supervised one try to find out

the relationship pattern between the target which the

data analyst already confirmed and the other data

points collected. Unsupervised try to predict the

target variable, assisting analyst understand how the

data was generated and discovered the pattern behind

them (Calders and Custers, 2013). The big data credit

reporting belongs to the use of supervised machine

learning tools.

2.2 The Big Data Credit Reporting’s

Characteristic and Advantage

With numerous data sources and machine learning

tool, the big data credit reporting has been rapidly

developed. One reason for traditional credit reporting

been replaced is that the big data credit reporting can

predict the thin-file consumers, the people who

cannot get access to the credit score, such as many

immigrants or recent college graduates with little or

no credit history, or people who have not activated its

credit account at least six months. With the big data

tool, equipped with huge data and high speed, these

people can also get the credit derives from their

normal life activities. Another reason is its foresight,

it combines as much as possible relative credit data,

such as the usage time of the same phone number,

connections on Facebook, a stable address or even the

use of proper capitalization in filling out a form, they

can assess the credit in every aspect. The last but not

the least reason is that it can protect the vulnerable

groups, for example, LexisNexis had created a new

credit score system called RiskView (Bureau, 2012),

this product include the traditional public record like

the foreclosures and bankruptcies, in addition, it also

include the educational history, professional licensure

data, and personal property ownership data, as a

result, these people who don’t have the traditional

credit score but have an professional license or pay

rent on time, or own a car, may get a better access to

credit system than they otherwise would have

(Ramirez, 2016). The Future of Privacy Forum

(Polonetsky, 2014) report also indicated that business

and government use the big data to protect and

empower the disadvantaged group, giving them the

access to job markets, disclosure the discriminatory

practices and improve the education and help those in

need.

3 THE ETHICAL PROBLEM

ABOUT THE BIG DATA

CREDIT REPORTING

3.1 The Big Data Credit Reporting

Different from Traditional One

When you use mapping software it might record all

your footmarks and sell it without your permission, if

SPBDIoT 2018 - Special Session on Recent Advances on Security, Privacy, Big Data and Internet of Things

350

you don’t give the application permission to access

your information, then you cannot use the function,

you cannot control what information would be

collected and often be forced to make all-or-nothing

decision between receiving free services or refusing

to be profiled (Bilogrevic et al., 2014). Not only a

fraction of people has such thoughts, the

investigations revealed that, seven in ten Europeans

are worried about that software operator will use the

personal data for profit or other uncontrolled use

(Social, 2011). Over the past few years, the online

applications have collecting the user’s information to

build the individual profiles, then selling them to the

advertiser and data broker to make profit, the users

have little control with the data collect process.

3.2 The Messy, Unreasonable and

Faked Data

Some aggressive big data enthusiasts claimed that we

should embrace the ‘messy data’, for the errors in data

manipulated process can help develop better pattern

result, the more mistakes there are, the more

preparations and well-equipped the system has gone

through (Alloway, 2015). However, the messy

problem had already affected the normal person life.

There is one case, the victim is Judy Thomas, who

sued Trans Union for regularly mixing her report with

Judith Upton. One day in 1996, Judy found there were

some mistakes since some bad debts appeared on her

credit report. After the investigation, she found the

bad debts was belonging to a woman named Judith

Upton, the one who has similar first name and same

birth year with her, and only one number different in

their Social Security numbers. Due to the operation of

one careless staff, Judy could not get the loan based

on her expectancy value, she struggled to appeal for

justice. Finally, Judy was awarded more than 5

million dollars by a Federal Court jury as a

compensation (Weisman, 2013). In this case, it

reveals problems with the quality of data in credit

reporting system as follows, first, erroneous data

could be caused by some careless problems, second,

there were fake and fabricated data, researchers found

that some investigators of the credit system even

fabricated derogatory information about individuals.

Apart from quality of data, the lack of third party

central regulation on credit agencies appear to be an

issue. While the credit rating agencies do not have the

direct relations with the customs, they lack incentives

to treat the individuals fairly, if profits and finding

vulnerable individuals appear to be their concerns.

There is also another challenge - it is difficult to

interchange with and between the credit rating

agencies. To sum up all the observations above, the

data problem is not just an anecdotal, it is structural

(Cetorelli and Gambera, 2001; Kenny, 2014). In a

detailed and comprehensive investigation conducted

by scholars and Federal Trade Commission, there are

almost 3,000 credit reports belonging to 1,000

consumers, it has been found that 26 percent had

“material” error problems which were serious enough

to affect the individuals credit scores. Even with the

conservative estimates, there were still 23 million

Americans having errors in their credit reports. Once

their credit scores amended, they will obtain credit

loan in lower prices (commission, 2013). On the other

hand, some researchers and industries might think the

big data can improve the situations, since more

information can be cross checked and validated for

better precision and accuracy. Some data may seem

logically related with the credit record, for example,

payment historical records matches the identity of the

right person. However, there are other “fringe data”

like the reading time of user notice to indicate the

individual’s degree of care might lack the correlations

with the creditworthiness (Yu, 2014). Additionally,

lots of evidence confirm that since thousands of data

were from the individuals on and off the site

activities, it might result in getting a high percentage

of inaccurate information. As Jacobs (2015) claimed

that mobile location data can be easily lead to

inaccuracy. Inaccurate data problems can deeply

affect the individual credit activities. While the big

data can improve on speed and interconnectivity, it

might less efficient to double check accuracy and

validity of the data, the problem may be worse.

3.3 Non-Transparency

The “kidnap service” and data source problem both

brought out one issue - individuals cannot get access

to the data process. The operation processes are

opaque to customers, like the case about Kevin

Johnson, who was a person with a good and decent

credit. He received a letter from the American

Express in one day of late 2008, which told him that

the credit limit of his credit card had been decreased

from $10800 to $3800. The reason given by

American Express was that, the market which Kevin

recently shopped by have some customers who have

bad credit history with American Express. While

Kevin casted around for an explanation, the American

Express did not want to share more details about that.

Even the Federal Trade Commission have sued the

three major credit rating agencies in 2000, because

they did not answer phones, which means the

customers have always been neglected. If users did

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not know how their scores been calculated and even

cannot complain about the unfair scores, the fairness

of credit reporting can be questionable. On the basis

of electronic privacy information centre, big data

credit evaluators are mainly "concerned about

collecting a large amount of information about

individuals" and the overall quality of the data may

be affected (Scoring, 2016). With non-transparent

problems exist, individuals cannot identify the unfair

credit scores and fail to improve scores or prevent

them from falling further (Hurley and Adebayo,

2016). The non-transparent problems exist in two

aspects, one is in the data collection and

transformation process, another is due to the machine

learning algorithms. Guzelian et al. (2015) explain

that the credit companies treat their data as the

commercial secrets. Therefore, the data collection

progress would be opaque, in case the competitor

found out data resources, the customers could not get

access to the data, they could not find whether the

data was accurate either. Even if the customers know

the data is accurate, it is difficult to find one error in

the millions of entries in the credit scorer is raw data

set. Since the machine learning process is designed to

find the relationship between data and target, the data

collected from customers will be transferred to the

language that computers can understand, the process

will be involved with mass aggregations and

combinations of data points. In this case, if the

machine learning algorithm was a “layman”, then the

layman could not understand how much a dataset

would need and what types of diagnosed emphasis for

further actions. As a result, it is not straight forward

to get some effective evidence to identify the

algorithm validity.

3.4 Heroin for Drug Addicts

Trusts (2014) shows the lenders will use the most

advanced technology and complex algorithm to target

the vulnerable persons, particularly those attracted by

the low-valued, short-term credit products with

usurious interest rates and highly adverse terms. The

experts at Upturn have also supported this view.

There is potentially one possible situation: While

some credit reporting system developers will not

interest in the system improvement or predicting the

consumer creditworthiness, they choose to put efforts

on finding vulnerable or high-value targets for non-

performing loans. The survey shows the target

disproportionately come from poor and minority

communities (Hurley and Adebayo, 2016). Angwin

(2014) identified that although no unambiguous

evidence showed the big data is used in the identified

the vulnerable borrower, some major data brokers

who work on credit reporting system have been

accused for bringing the "sucker lists" to the market,

the list which specifically target on vulnerable groups

of people, which may cause them distress or more

pain to their existing debts. The evidence can be

found from 2013 Senate Commerce Committee

report which have lists with title like "Hard Times",

"Burdened by Debt", "Retiring on Empty" and "X-tra

Needy" which were deliberately aimed at the

individuals who were trying to buy some

unfavourable financial products.

3.5 Discrimination

Facebook is another good example. It is a free social

networking website which can allow users to upload

their photos or videos, and post their comments, has

recently applied for one patent application. The patent

is related to one method for "authorization and

authentication based on an individual's social

network." which means the users’ social information

can be used to evaluate their credit score. The patent

application explains that, when someone applies for

loan, the lender can get access to the individual’s

social network profile and get the rank of his social

group, if the social group meet the minimum

requirement of the lender, then the lender can give a

loan to the applicant, otherwise, he will be rejected.

Criticasters have indicated that the tools can bring

about the new style of digital redlining (Meyer,

2015).

The machine learning may also produce results

that have inequity biases, because the person’s final

credit score is not based on the individual’s own value

but prefer on the basis of the relative group that the

system affirm effectively. As Barocas and Selbst

(2016) claim that when a model relies on the

generalization reflected in the data, the final result of

individual maybe statistically sound inference, so that

the result may be inaccurate. However, this may

happen when customers do nothing with it and they

cannot do anything to revert. Like the case of Kevin

Johnson mentioned earlier, the model will punish

individuals that in a particular environment like the

designated community or having some issues with the

political or religious grounds. The case of Kevin

Johnson is not a special case, in lots of other fields,

from the school admission decisions to the insurance

selling, the big data tool will judge the person by the

shared data rather than individuals' own and true

value, which have been proven to aggravate existing

prejudice (Lowry and Macpherson, 1988).

SPBDIoT 2018 - Special Session on Recent Advances on Security, Privacy, Big Data and Internet of Things

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3.6 Wealth Gap Widen

Schmitz (2014) claimed that the use of big data in the

credit reporting field can foster the discrimination,

due to the data analysis subjective rules the machine

will give an aid to the smartest customers with the

best services and best remedial measure, which will

widen the gap between the wealthy and poor

individuals. As the Hurley and Adebayo (2016)

explained in the report, the big data tools will increase

the risk of creating a system of "creditworthiness by

association", which combine the individuals’

families, religions, sexual orientation and other

relevant information. All information will figure out

the individual’s credit score, whether you can pay for

a load is not determined by how much you can earn,

but the group you are in, which will further aggravate

discrimination. Like the example of Kevin Johnson,

he became the proxies of sensitive information like

the race and vulnerable attributes. In this example, the

big data could not eliminate the prejudice, but

aggravate the existed bias.

4 CONCLUSION

In this paper, we report the grey area about the use of

big data, particularly for credit reporting system that

has biases and selection on vulnerable groups of

people. Big data enthusiasts argue that all the data can

be managed by credit reporting and the big data will

lead us to live happily and comfortably. While we can

find is that the data maybe objective, the process of

data categorization and evaluation can be subjective.

Moreover, the ways data will be collected or

diagnosed largely depend on the existing social

resources. Individuals who have contributed their

data to the credit reporting system, have no or few

effective actions to modify the “unreasonable” data.

The vulnerable people will be likely to be trapped in

a vicious circle by precision marketing or have been

considered as low credit groups due to potential

discrimination in income, social status, race, politics

or religion. On the other hand, the wealthy people can

possess the best remedial measures and excellent

services. While we need to use the big data to build

the completeness and soundness of the credit

reporting system. However, we should also pay

attention to ethical issues raised by the big data and

find betters to manage and treat each individual fairly

and equally.

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