Loan Default Analysis in Europe: Tracking Regional Variations using Big Data – Phdassistance.com
                     LOAN DEFAULT 
ANALYSIS  IN EUROPE: 
TRACKING  REGIONAL 
VARIATIONS  USING 
BIG DATA
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S 
DISCUSSION
Outlin In Brief
e Background
Tests
European Perspect ive 
 Big Data
Conclus ion
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.
Backgroun
d
Banks were never anticipated to fail especially the large banks like AIG.
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 European Banking Authority (EBA) is responsible for the European banks and comes under 
 the jurisdiction of the European Union (EU).
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.
The various tests and accounting models that are available are the  
Stress Tests, Credit Loss, etc.
Test Bank stress tests use simulation by examining the balance of the  firms and analyse the financial stress that is available.
s This will help in identifying capital, investment, liquidity, etc. of the  project 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.
Contd..
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.
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.
The major reason for having fewer studies for Europe is due to the  
Europea unavailability of reliable and consistent data for most European countries.
n  
A repository known as European Data warehouse (ED) contains partial 
Perspecti  data that can fill the gap to some extent, which gives the researchers  
ve 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)
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.
The ability of machine learning algorithms to predict the financial 
 analysis makes them very much efficient for the regulatory  
Big bodies to monitor the finances.
Data 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 different type of analysis has been seen 
and  discussed for European banks.
Conclusio Analysing the CECL of the banks using machine 
n  learning techniques through big data will 
greatly  avoid loan defaults.
This will avoid the failure of banks thereby 
avoiding
economic collapse.
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