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