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Loan Default Analysis in Europe: Tracking Regional Variations

using Big Data

Dr. Nancy Agens, Head,

Technical Operations, Phdassistance

In Brief

You will find the best dissertation

research areas / topics for future

researchers enrolled in Economics &

Finance. In order to identify the

future research topics, we have

reviewed the Finance (recent

peer-reviewed studies) on Data

Analysis. Multiple factors that affect

the bank stability and ensure that

proper documentation is maintained

Main objectives are to conduct stress

tests on the banks.

Keywords: Loan Default, Financial

Crisis, Europe, CECL, stress tet, Risk

Management, Bank.

I. BACKGROUND

Banks were never anticipated

to fail especially the large banks like

AIG (McDonald & Paulson, 2020). Its

collapse led to a complete failure of the

insurance company and one of the

main factors of the 2008 financial

crises. The main problem was that they

had given out too many loans and

guarantees to the borrowers even when

they did not have enough capital in the

reserves for the compensation. The

authorities did not consider this since

they didn’t realize that a

well-established firm could fall.

Hence, banks around the world

must now conduct regular analysis to

check the adequacy of the capital by

their regulatory bodies (Baudino,

Goetschmann, Henry, Taniguchi, &

Zhu, 2018). This has been put in place

to avoid another financial collapse.

The analysis must take into

consideration, the multiple factors that

affect the bank stability and ensure that

proper documentation is maintained. It

must also be ensured that the credits

are issued with enough capital as

reserves to withstand the loan defaults

and investment (The Basel Committe,

2006).

While American firms were

largely responsible for the 2008

financial crisis, Europe and the rest of

the world tool a large hit. Therefore, it

cannot be ruled out that European

firms will never fall since a large

portion of world population bank with

European firms (The Economist, 2019).

The European Banking Authority

(EBA) is responsible for the European

banks and comes under the jurisdiction

of the European Union (EU). It is

located in Paris and it develops rules

and regulations, which the banks in the

EU must ultimately follow. Its main

objectives are to conduct stress tests on

the banks in order to improve the

transparency in the financial system

and identify the flaws and mismatches

in capital and investments.

II. TESTS

The various tests and

accounting models that are available

are the Stress Tests, Credit Loss, etc

(Basel Committee Banking

Supervision, 2017). Bank stress tests

use simulation by examining the

balance of the firms and analyse the

financial stress that is available. This

will help in identifying capital,

investment, liquidity, etc. of the project

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and analyse the available capital.

Current Expected Credit Loss (CECL)

is a type of credit loss model that is

used to analyse the exchange of capital

and the losses arising from it

(European Systemic Risk Board, 2019).

Before the financial crisis of 2008, a

conventional method known as

Allowance for Loan and Lease Losses

(ALLL) were used, however, in this

type of model it does not adjust the

reserve levels as per the required

conditions. Instead, it depends on the

losses that incur but not realized. This

means that it will not be certain when

the cash flow will take place in the

future. This negative outlook of the

credits was not considered during the

financial crisis and the reserves were

not adjusted for future expected losses.

Hence, the improved CECL approach

identifies the credit loss by considering

the factors previously avoided (Cohen

& Edwards, 2017).

III. EUROPEAN PERSPECTIVE

A large portion of the existing

literature has focused on the United

States, while there are very few studies

that consider Europe. The credit

systems vary a lot between these two

regions since they have different

market structure and economic

conditions and since they have

different regulatory authorities (Chen,

2018). Also, the behaviour of the

borrower will not be the same between

the two regions. The major reason for

having fewer studies for Europe is due

to the unavailability of reliable and

consistent data for most European

countries (Mladovsky, Allin, &

Masseria, 2009). A repository known

as European Data warehouse (ED)

contains partial data that can fill the

gap to some extent, which gives the

researchers different opportunities to

explore the credit market in Europe.

The number of loan defaults do not

remain constant and has constant

variations among the corporate world.

The loan defaults rates of corporates

globally is shown in figure 1.

Fig. 1 Annual Global Default Rates For CLOs and Corporate Issuers

Source: Vazza et al.,(2020)

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IV. BIG DATA

The presence of large amount

of data in some countries brings in a

dilemma on how to process the data

since this brings about additional

complexities to the analysis. Hence,

machine learning algorithms and Big

data can be implemented so that the

model can be analysed without

complicated mathematical models. The

use of big data brings about

possibilities in creation of larger

databases without the issues of space

constraints and limitations. The simple

structure of the machine learning

algorithms can analyse the information

available in the ED and may attain

better description of the European

behaviour.

The ability of machine learning

algorithms to predict the financial

analysis makes them very much

efficient for the regulatory bodies to

monitor the finances. CECL and stress

tests can be performed using these

algorithms to get efficient results. The

data must contain various parameters

required for the analysis like Loan to

Value (LTV), Debt Service Coverage

Ratio (DSCR), etc. as the indicators of

loan credits. The data from the banks

must be updated regularly on a daily or

weekly basis so that every transaction

is accounted during the analysis. The

regulatory bodies must collect the data

and run the model in real time to

constantly monitor the parameters

(Drotár, Gnip, Zoričak, & Gazda,

2019). This is rather difficult since

European countries are diverse with

different types of banking cultures, but

overall they are similar when

compared to American banking

cultures. Normally, there will be many

missing values in the parameters since

they depend on bank corporates to

provide the type of data. Hence,

different banks would provide different

type of data which would completely

skew the analysis process. Hence, steps

must be taken to make uniform

collection of data among the European

banks.

V. CONCLUSION

The different type of analysis

has been seen and discussed for

European banks. Analysing the CECL

of the banks using machine learning

techniques through big data will

greatly avoid loan defaults. This will

avoid the failure of banks thereby

avoiding economic collapse.

REFERENCES

[1] Basel Committee Banking Supervision. (2017).

Supervisory and bank stress testing: range of

practices. BIS Working Papers. Retrieved

from https://www.bis.org/bcbs/publ/d427.pdf

[2] Baudino, P., Goetschmann, R., Henry, J.,

Taniguchi, K., & Zhu, W. (2018). FSI

Insights on policy implementation

Stress-testing banks – a comparative analysis.

BIS Working Papers. Retrieved from

https://www.bis.org/fsi/publ/insights12.pdf

[3] Chen, G. (2018). Loan Default Analysis: A Case

Study For CECL. Retrieved from

https://w3.zmfs.com/wp-content/uploads/201

8/05/ZConcepts_LoanDefaultAnalysis-ACas

eStudyforCECL.pdfhttps://w3.zmfs.com/wp-

content/uploads/2018/05/ZConcepts_LoanDe

faultAnalysis-ACaseStudyforCECL.pdf

[4] Cohen, B. H., & Edwards, G. (2017). The new

era of expected credit loss provisioning. BIS

Quarterly Review, March. Retrieved from

https://papers.ssrn.com/sol3/papers.cfm?abstr

act_id=2931474

[5] Drotár, P., Gnip, P., Zoričak, M., & Gazda, V.

(2019). Small- and medium-enterprises

bankruptcy dataset. Data in Brief, 25, 104360.

https://doi.org/10.1016/j.dib.2019.104360

[6] European Systemic Risk Board. (2019).

Expected credit loss approaches in Europe

and the United States: differences from a

financial stability perspective.

https://doi.org/10.2849/600179

[7] McDonald, R., & Paulson, A. (2020). What

Went Wrong at AIG? Retrieved February 4,

2020, from

https://insight.kellogg.northwestern.edu/articl

e/what-went-wrong-at-aig

[8] Mladovsky, P., Allin, S., & Masseria, C. (2009).

Health in the European Union: trends and

analysis. WHO Regional Office Europe.

Retrieved from

http://www.euro.who.int/__data/assets/pdf_fi

le/0003/98391/E93348.pdf

[9] The Basel Committe. (2006). Principles for the

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Management of Credit Risk. IFAS Extension,

1–33. Retrieved from

http://edis.ifas.ufl.edu/pdffiles/HR/HR02200.

pdf

[10] The Economist. (2019, September). The

economic policy at the heart of Europe is

creaking. The Economist. Retrieved from

https://www.economist.com/briefing/2019/09

/12/the-economic-policy-at-the-heart-of-euro

pe-is-creaking

[11] Vazza, D., Kraemer, N., & Gunter, E. (2020).

2018 Annual Global Leveraged Loan CLO

Default and Rating Transition Study.

Retrieved from

https://www.spglobal.com/en/research-insigh

ts/articles/sp-global-ratings-global-outlook-2

019