Dr.Pu: SVP and General Manager of BBD

1. Financial Risk Management in the Big Data Era
Practice Sharing for SMEs and New Economies
- Daniel Pu(Ke Qiang)

2. Contents
01 02 03SMEs Financing
Embarrassment Situation
Big Data’s Evolution in
SMEs Financing
The Most Innovative
Applications in SMEs

3. 01
SMES FINANCING
EMBARRASSMENT SITUATION

4. SMEs Financing Struggle be a Huge Hurdle of Development
4
80%
20% Struggle for financing and has
become a tremendous challenge.
Lucky “20%” - the SOE and large companies.
Leftover “80%” - the low income individuals and the SMEs
Benefits from

5. Obstacles to Capital Raising and Loaning
15.80%
21.70%
22%
23.50%
27.40%
28.30%
31.30%
41.10%
45.80%
0.00% 20.00% 40.00% 60.00%
Poor Customer Experience
Unacceptable Requirement
Broken Lending Commitments
In the Absence of Guarantors
Poor Prospects for Future
High Cost of Loaning
Financial Statements Unavailable
Lack of Collaterals
Long Cycle of Approval
In China, the main financing channel for SMEs is the bank, no doubt, with three out of four had applied for loans from banks,
reflecting a strong preference for and trust in the formal financial system. However, nearly 80 percent of SME entrepreneurs still
face many difficulties when they had borrowing needs that the banks could not fulfill. Indeed, the top reasons did not borrow
from banks esp. for a shorter-term credit was long cycle of approval and high requirement of collaterals.
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6. Persistent Issues by Traditional Credit Assessment
6
01
Accounting Data Dependence
Providing financial statements
with distortion is a long existing
problem in many SMEs.
02
Weak Timeliness
Clients information have been
collected manually in traditional
business pattern. It takes in a
long cycle and gets slowly update.
03
Insufficient Dimensions
Only Structured data have been
considered, while semi-structured and
unstructured data have not been used
efficiently to illustrate risks.
04
Information Asymmetry
Hidden financing and complicated
collateralization make it difficult to
assess credit risk of SMEs.
05
Outdated Modelling
Due to the lack of data and
limitation of methodology,
traditional credit risk score-
cards require frequent
optimization and verification
with its bad stability.

7. 02
BIG DATA’S EVOLUTION
IN SMES FINANCING
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8. Harnessing Big Data to Risk Management
As you are reading, the world’s data is exploding in
unprecedented velocity, variety, and volume. It is now
available almost instantaneously, creating possibilities
for near real-time analysis. While Big Data is already
being embraced in many fields, risk managers have yet
to harness its power. Big Data technology has
revolutionary potential. It can improve the predictive
power of risk models, exponentially improve system
response times and effectiveness, provide more
extensive risk coverage, and generate significant cost
savings. In a world of increasing complexity and
demand, the ability to capture, access and utilize Big
Data will determine risk management success. Big
Data technologies are set to transform the world of
risk management.
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9. New Data Source – Online Big Data
710 Millions
Netizens
China has the largest number of
netizens in the world, as many as
721 millions, with 51.9% internet
popularity. There are 656 millions of
mobile netizens, and the number
keeps growing.
413 Millions
Online Shoppers
Online retailers grew to 413 million active
users in China. Online shoppers are fighting
for the best deals with quick-clicking fingers
and this year, 120 billion RMB of the single-
day sales reached for Alibaba’s Nov 11 online
shopping festival.
Internet Popularity 51.9% Network Usage 58.2%
The credit checking system can make good use of the internet data from various sources by analyzing the data carrier's basic information,
transaction activity, financial or economic relations and credit mode. The big data brings brand new ideas to the construction of credit system.
Through a series process of data screening, matching, integrating and mining, the seemingly useless data will become valuable credit related
data, which highly improves efficiency and accuracy of credit evaluation. The big data makes it possible that every data can reflect credit.
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10. E-commerce Financing Platform: Ali Microloan
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Generate Data from Alibaba’s Own Platforms
Data from online platforms of Alibaba, including Taobao, Tmall,
and Alipay, have been used into loaning decisions through cloud
computing.
Customized Loaning Service to Meet
Specific Needs
Varied loaning services such as order loan, credit loan, supply
chain loan, operational service provider loan, etc, to meet
different customers’ needs.
Easier, Faster and Cost-saving
Customers could communicate with a specialist through online
chat or email for service; credit checking also conduct through
internet.
Unable to step outside of its
ecosystem and only stick to and
stand on existing customers.

11. Crowdfunding: Born to Serve the SMEs
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Investors always know less about the start-ups
or projects, in particular, for the risks
associated with the projects to be financed.
Given the higher failure rate of new
technologies or start-ups, the negative impact
of the asymmetric information could be
amplified in the case of crowdfunding.
Information Asymmetry
Most of the projects listed on crowdfunding
platforms are by companies or individuals that
are then unknown to much of the public.
From an investor perspective, equity
crowdfunding has no guarantee, internal or
external, of the repayment of the invested
principal, guarantees which are typically
required in the case of debt financing
High Uncertainty
￥7.9 Billions
received in the first half of year of 2016.
Equity investments don’t mandate the
repayment of principal, there is an increased
chance of fund requesters deceiving
investors and fraud risk in the case of project
creators exsits.
Fraud Risk
370
crowdfunding platforms that are
currently in operation in China

12. 大数据应用
12.75%
Average annualized rate of
return in 2015
The popularization of the internet, e-business and electronic payment make the P2P online
lending technically feasible, and the long-term financial repression for SMEs and low income
consumers in China offers the great demand for P2P growth. Under the financial repression,
the financial intermediaries have not efficiently resolved the issue of funding SMEs, while the
new model of P2P online lending helps bring the “private” lending to “public”, and greatly
lower the information asymmetry and cost, which is also a supplement to existing
commercial banking system.
Lack of credit check, no adequate regulations
high quality and low quality platforms co-exist
13.5 Million
￥1 Trillion
P2P loans in
2015
Investors
P2P Online Lending: Bring the Private to Public
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13. 03
THE MOST INNOVATIVE
APPLICATIONS IN SMES
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14. Macroscopic Monitoring for Nationwide Corporate Investment
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15. By tracking the 11 sub-industries from the 9 new
economic industries, NEI is able to forecast the
economic pattern.
Economic Pattern Forecast
To discover both the surplus and shortfall of
various industrial growth rate in different
regions. Reflects the actual growth rate of the
respective industries in different region.
Cross/Vertical Sectional
Referencing Index
Whole-New Indexing Parameter
With the combination of online data mining,
talent migration statistics and intellectual
property growth rate related data crawled, NEI
presents forecast of the short-term and long-
term economic pattern in China.
NEI Witness the Rise of the New Economies
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16. We Help banks, brokerage
firms, accounting firms, law
firms and regulators to find
hidden related party of a target
company. It also helps to
detect suspicious fund transfer,
loan fraud, transfer of benefits,
insider trading and hidden
litigation and enterprise
behaviors.
HIGGS is used to support the realization of actual commercial behavior
characterization of enterprise to help to understand a company’s latest
business status and to identify potential risks.
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Microscopic Monitoring for Big Data Enterprise DNA Diagram

17. Dynamic Due Diligence Solution
A real time enabled, dynamic due diligence solution which allows user
to discover a target’s relations up to 4th Tier.
HIGGS Credit delivers dynamic corporate due diligence within a few clicks
while it takes weeks even months in the past.
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18. 100 companies 20 companies 5 companies 1-2 companies
The Due Diligence based on
the database of enterprise
behavior
With the authorized data
and the internal data from
the bank
On-Site Due
Diligence
To optimize resources
allocation, reduce the
delinquency ratio of
mortgaged assets and
enhance the overall
profitability.
Dynamic Due Diligence to Facilitate Bank Credit Assessment
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19. HoloCredit portrays credit risks with extensible
modules.
The features of asset-lite and information
asymmetry of SMEs make credit evaluation a
persistent issue.
Based on enterprise behaviour data, HoloCredit
employs innovative modelling methodologies and
provides comprehensive risk assessment as a
holographic, modularized and extensible solution.
It constructs a hologram of risk DNA for a target
enterprise by multi-dimensional modules.
HoloCredit
Cores
Entrepreneur General
Information Module
Entrepreneur
Credit Module
Enterprise General
Information Module
Enterprise
Credit Module
Tax Module
Accounting Module
Related Parties
Module
External Environment
Module
Innovative and Extensible HoloCredit Model
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20. Model Performance Comparisons
A test was conducted which made comparisons of performance and risk ordering ability among banking
traditional models (including SME rating model and experts model) and HoloCredit rating model by calculating AR
(accurate rate) value.
• Results
Model performance comparison between traditional SME rating model and HoloCredit shows below：
Model AR
SME Rating Model 39.93%
HoloCredit 78.8%
HoloCredit performs even better than the risk rating sequencing model conducted by experienced experts：
Model AR
Experts Model 21.55%
HoloCredit 79.85%
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21. The first generation of
Holo rating model has
been launched in Bank
of Chongqing, and its
online credit product
was released in Jul
18th,2016.
HoloCredit, the SME and High Growth Enterprises analytics platform tailored for Bank of Chongqing encompasses multi-
dimentional data sources, including Taxation Bureau, entity registration, related parties, patents, recruitment, and macroeconomic
data, etc. The platform is able to assist the bank to captivate business opportunities of SMEs and High Growth Enterprises. The
innovative big data analyitics can also be applied to other sectors and effectively promote the development of SME finance in
China and potentially aboard.
HoloCredit has become the leading big data platform in the financial industry in China and increasing numbers of domestic banks
have commited to collabrate with BBD to develop their SME business.
HoloCredit: SME Risk Analytics for Bank of Chongqing
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22. The second generation of
our rating model
launched in Bank of
Guiyang in Nov 23rd, 2016.
To assist the Bank of Guiyang to gain a better understanding of enterprise risks and social impact across economic and financial
prospectives, BBD has created an innovative solution by our cutting-edge technology that helps them to establish the most
comprehensive data system by integrating entity registration data, tax data, utility data and social security data, and extending to
government data, and enterprise business behavior data, etc. The product has become an important tool that continously
improve their business.
HoloCredit: SME Risk Analytics for the Bank of Guiyang
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23. BBD Boosts the Financial Transformation
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24. THANKS
For Watching！
Data the Future