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Financial Risk Management in the Big Data Era
Practice Sharing for SMEs and New Economies
- Daniel Pu(Ke Qiang)
Contents
01 02 03SMEs Financing
Embarrassment Situation
Big Data’s Evolution in
SMEs Financing
The Most Innovative
Applications in SMEs
01
SMES FINANCING
EMBARRASSMENT SITUATION
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
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.
5
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Outdated Modelling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.
02
BIG DATA’S EVOLUTION
IN SMES FINANCING
7
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.
8
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.
9
E-commerce Financing Platform: Ali Microloan
10
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.
Crowdfunding: Born to Serve the SMEs
11
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
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
12
03
THE MOST INNOVATIVE
APPLICATIONS IN SMES
13
Macroscopic Monitoring for Nationwide Corporate Investment
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
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.
16
Microscopic Monitoring for Big Data Enterprise DNA Diagram
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.
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
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
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%
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
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
23
BBD Boosts the Financial Transformation
23
THANKS
For Watching!
Data the Future

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BBD Seminar - Dr.Pu - Financial Solution for SME v10

  • 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
  • 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. 5
  • 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 7
  • 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. 8
  • 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. 9
  • 10. E-commerce Financing Platform: Ali Microloan 10 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 11 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 12
  • 14. Macroscopic Monitoring for Nationwide Corporate Investment 15
  • 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 16
  • 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. 16 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. 18
  • 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 19
  • 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 20
  • 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% 21
  • 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 22
  • 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 23
  • 23. BBD Boosts the Financial Transformation 23

Editor's Notes

  1. The data behind decision