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Gaining from digital disruption:
The Thai financial landscape in the digital era
Bank of Thailand Symposium 2019
Thammarak Moenjak Vorapat Praneeprachachon
Tanatas Bumpenboon Pornchanok Bumrungruan Chompoonoot Monchaitrakul
► Competitiveness assessment of the Thai financial sector in four dimensions
► Competition in the Thai banking sector: A measure over time
► SME access to credit: An area for improvements
► Enhancing competitiveness of the Thai financial sector in the digital era
Outline
Using Global Financial Database framework,
competitiveness of a financial sector can be measured through
Depth
The size of
financial institutions
relative to the economy
Efficiency
The efficiency of
financial institutions
in providing
financial services
Access
The degree to
which individuals
can and do use
financial institutions
Stability
The stability of
financial institutions
Depth: How deep is the financial sector embedded in the economy?
Thailand is around ASEAN-5 average for the period in all measures
0
20
40
60
80
100
120
140
Singapore Malaysia Thailand Philippines Indonesia
Avg. = 87.42
% Domestic credit to private sector by banks to GDP(%)
0
20
40
60
80
100
120
140
160
180
Singapore Malaysia Thailand Philippines Indonesia
Deposit money banks’ assets to GDP (%)
Avg. = 99.34
%
0
20
40
60
80
100
120
140
160
Malaysia Singapore Thailand Philippines Indonesia
Liquid liabilities to GDP (%)
Avg. = 93.92
%
Source: World Bank
Efficiency: Can banks keep costs down and make good returns?
Thailand is in line with peers
0
1
2
3
4
5
6
Indonesia Philippines Thailand Malaysia Singapore
Bank net interest margin (%)
Avg. = 2.98
%
0
4
8
12
16
20
Indonesia Philippines Malaysia Thailand Singapore
Bank return on equity (%)
Avg. = 11.2
%
Source: EMEAP and World Bank
0
1
2
3
4
Singapore Malaysia Thailand Philippines Indonesia
Bank overhead costs to total assets (%)
Avg. = 1.81
%
0
1
2
3
Indonesia Thailand Philippines Malaysia Singapore
Bank return on asset (%)
Avg. = 1.27
%
Access: How well can households and firms access financial services?
Household access to financial services is reasonably high. SME access to financial services is somewhat lagging.
0
20
40
60
80
100
Singapore Malaysia Thailand Indonesia Philippines
Households with financial institution accounts
(% age 15+)
Avg. = 63.10
%
0
20
40
60
80
100
Singapore Malaysia Thailand Indonesia Philippines
Avg. = 50.24
Households that made or received digital payment
in the past year (% age 15+)%
Source: Global Findex and World Bank Enterprise Survey
0
10
20
30
40
Malaysia Philippines Indonesia Thailand Singapore
Firms with a bank loan or line of credit (%)
N/A
Avg. = 26.18
%
0
10
20
30
40
Malaysia Indonesia Philippines Thailand Singapore
Small firms with a bank loan or line of credit (%)
N/A
Avg. = 21.53
%
Stability: How much cushion do financial institutions have?
Thailand has high levels of cushion on average compared to peers
0
5
10
15
20
25
Indonesia Thailand Malaysia Singapore Philippines
Avg. = 17.70
Regulatory capital to risk-weighted assets ratio (%)%
0
10
20
30
Indonesia Thailand Singapore Philippines Malaysia
Common equity tier 1 ratio (%)
Avg. = 15.08
%
0
1
2
3
4
Singapore Malaysia Philippines Indonesia Thailand
Avg. = 1.99
Non-performing loans as a percentage of total loans (%)%
Source: EMEAP
0
50
100
150
200
250
300
Indonesia Thailand Philippines Malaysia Singapore
Liquidity coverage ratio (%)
Avg. = 163.85
%
Snapshot of Thailand’s financial sector compared to ASEAN-5 peers
* This table aims to provide a general assessment of Thailand compared to ASEAN-5 peers and neither does the information herein suggest a global nor regional benchmark. Differences among countries are largely attributed to, among other things, unique characteristics, infrastructures, and geography of countries selected and do not necessarily suggest a desired level of economic development or otherwise.
Below average
Somewhat average
Aboveaverage
Depth
Efficiency
Access
Stability
Households
SMEs
✓
✓
✓
✓
✓
Can competition help improve competitiveness?
Competition is supposedly a key driver for access and efficiency
but is not a sole factor
Competition
Innovations
Lower costs
Access
Efficiency
Measuring competition in the financial sector: H-statistic
H-statistic measures sensitivity of changes in revenues given changes in input costs
log𝑇𝑅
𝑇𝐴) = 𝛼 +
𝑖=1
𝑛
𝛽𝑖 log𝑤𝑖 +
𝑗=1
𝐽
𝛾𝑗 log 𝐶𝐹𝑗 + 𝜀
W1 : Funding cost
Interest expense
Total liabilities
W2 : Labor cost
Wage expense
Total assets
W3 : Branch cost
Premise and equipment expense
Fixed assets
► Consolidated financial statement from 15 Thai commercial banks
► Quarterly data from 2013 to 2018
Measuring competition in the financial sector: H-statistic
► If value of H-statistic closes to 1 players are close to being price takers (high competition)
► If value of H-statistic closes to 0 players are close to being price makers (low competition)
𝐻𝑟 =
𝑖=1
𝑛
𝛽𝑖
H-statistic Competition level
H ≤ 0 Monopoly or collusive oligopoly
0 < H < 1 Monopolistic competition
H = 1 Perfect competition
H-statistic measures sensitivity of changes in revenues given changes in input costs
H-statistic for the Thai banking sector calculated over time
0.760.73
0.0
0.2
0.4
0.6
0.8
1.0
1.2
2013 2014 2015 2016 2017 2018
H-Statistic of banking sector: banks and their subsidiaries
(Consolidated financial statement)
► Authors’ H-statistic is consistent with that of other studies
► The Thai banking sector is operating under monopolistic competition
A puzzle …
If the competition in the banking
sector is so high, why is SME
access to bank finance so low?
Maybe … ► Banks are competing for
the same fixed-size pie
► There are some inherent
inefficiencies in the
lending process
Distribution of bank branches
► 570,223 SMEs
received bank loans
serviced by 6,742
bank branches*
► Loans are generally
concentrated in areas
with high branch
density
Based on data from 1.29 million SMEs from 15 commercial banks and six SFIs,
banks concentrate their SME lending in Bangkok and the central region.
0% 4%
Chonburi
4.3%Bangkok
22.5%
Samut Prakan
3.7%
Chiang Mai
2.7%
Nonthaburi
Pathum Thani
3.2%
Distribution of bank customers
% of SMEs who received loans in the selected province
Bangkok
30.1%
% of bank branchin the selected province
0% 5%
* The number of branch is for 15 Thai commercial banks only
Smaller loans have been increasingly granted, and also to the less affluent segment.
However, Bangkok and vicinity still received the largest share.
0%
20%
40%
60%
80%
100%
H2 2015
H1 2016
H2 2016
H1 2017
H2 2017
H1 2018
H2 2018
Bangkok and vicinity Central Northeast South North
New customers who got loans, by region
0%
20%
40%
60%
80%
100%
H2 2015
H1 2016
H2 2016
H1 2017
H2 2017
H1 2018
H2 2018
<=1 1-5 5-20 20-50 >50
New customers who got loans, by income
Million baht
0%
20%
40%
60%
80%
100%
H2 2015
H1 2016
H2 2016
H1 2017
H2 2017
H1 2018
H22018
<100,000 baht 100,000 - 500,000 baht >500,000 baht
New customers who got loans, by loan size
Focused only by a few banks
Focused by many banks
0%
20%
40%
60%
80%
100%
H2 2015
H1 2016
H2 2016
H1 2017
H2 2017
H1 2018
H2 2018
Individual persons Juristic persons
New customers who got loans, by entity
However, as bank penetration into SME segment increased,
NPLs rose over time
0%
5%
10%
15%
20%
25%
30%
35%
1 2 3 4 5 6 7 8
No. of half-yearfrom loan origination
0%
4%
8%
12%
1 2 3 4 5 6 7 80%
4%
8%
12%
1 2 3 4 5 6 7 8
NPLs for SME loan contracts amount less than 100,000 baht
NPLs for SME loan contracts amount between 100,000 to 500,000 baht
NPLs for SME loan contract amount more than 500,000 baht
No. of half-yearfrom loan origination
No. of half-yearfrom loan origination
0%
4%
8%
12%
16%
20%
1 2 3 4 5 6 7 8
H2 2015
H1 2016
H2 2016
H1 2017
H2 2017
H1 2018
H2 2018
%NPL %NPL %NPL
%NPLNPLs for SME loan contracts
No. of half-yearfrom loan origination
SFIs have stepped in, and helped complement banks
in coverage of SME lending, geographically
0% 50% 100%
% of SMEs who received loans from SFIs relatively to banks
Bangkok and vicinity
SFIs are main lenders to
SMEs outside Bangkok
and vicinity
SFIs can help fill the access gap up to a point, quantity-wise.
Quality-wise, gaps, however, remain.
Banks
16%
SFIs29%
Banks and SFIs2%
No usage
53%
Small and medium enterprises
Banks
16%
SFIs
29%
Banks and SFIs
2%
No usage
53%
Small enterprises (99.5%*)
Banks
51%
SFIs
1%
Banks and SFIs
5%
No usage
43%
Medium enterprises (0.5%)
Quantity-wise Quality-wise
►Banks still need collaterals
►Processes are still time consuming
►SMEs still use inappropriate products,
e.g. credit card loans and personal loans
* Small enterprises are made up 99.5% of total SMEs
From BOT’s Regulatory Guillotine 2018: bottlenecks in SME loan origination
Bottlenecks
Manual process
Paper document
Lack of data
Others
Borrower inquiry
Prescreening and
data collection
Verification
Underwriting
Credit decision
Quality control
Funding
Collection or
default
START
Loan application Loan processingLoan disbursement/
collection
Bottlenecks lead to: Information asymmetry and frictions in process
► Higher costs and more time consuming to serve SMEs than to large corporates
► Incapability to assess SMEs accurately: Require collaterals
✓
✓
✓
✓
✓
✓
✓✓
✓
✓
✓
✓
✓
Model accuracyNeed collateral
✓
✓✓
✓
No market for collateral
Thailand 4.0
Can we use technology
and data to help unlock
the bottlenecks?
Harnessing technology and data to reduce pain points in SME financing
Borrower inquiry
Prescreening and
data collection
Verification
Underwriting
Credit decision
Quality control
Funding
Collection or
default
START
Loan application Loan processingLoan disbursement/
collection
Initiatives to alleviate bottlenecks
►Online inquiry
►Online comparisons
►Online applications
►Digitization
►Robotic Process
Automation
►Digitization
►Robotic Process
Automation
►Digitization
►Robotic Process
Automation
► E-Payments
►Robotic Process
Automatione-KYC
► AI&ML
►Data to
enhance credit
risk models
►Digitization
► Robotic Process
Automation
Key success factors:
► Interoperability: Facilitating data and payment flows
► A vibrant ecosystem: Filling in various niches
► Agile, forward looking regulators: Encourage innovations with acceptable risk tolerance
Solving the puzzle:It takes more than just the banks!
Banks New players
SMEs
Collaborate
Compete
► Leverage on technology
► Create digital footprints
► Interoperable infrastructures
► Regulatory sandboxes
► More flexible regulations and forward-looking mindset
► Digitization and
process automation
► Provide business solutions
e.g. cloud-based accounting,
payment gateway
► Analysis of behavioral data
from alternative sources
Regulators
► Digital funding sources with
lower operational costs
► Digital banks
► P2P lending platforms
► Crowdfunding
Key takeaways
► Thai financial sector compares reasonably with peers in the four dimensions
► Banks are already competing intensely, but SME access to credit remains
a sore point
► The use of technology and data has the potential to alleviate bottlenecks
in SME lending
In the digital era:
► Interoperable infrastructures would be key to capture network values
► New players could help fulfill the gaps by leveraging on data and technology
► Right incentive structures are critical, e.g. to induce SME adoption of technology
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