Financial Risk Management in the Big Data Era

Practice Sharing for SMEs and New Economies- Daniel Pu(Ke Qiang)

Contents

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02

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SMEs Financing Embarrassment Situation

Big Data’s Evolution in SMEs Financing

The Most Innovative Applications in SMEs

01SMES FINANCING

EMBARRASSMENT SITUATION

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SMEs Financing Struggle be a Huge Hurdle of Development

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

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Obstacles to Capital Raising and Loaning

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

0.00% 20.00% 40.00% 60.00%

15.80%

21.70%

22.00%

23.50%

27.40%

28.30%

31.30%

41.10%

45.80%

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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Persistent Issues by Traditional Credit Assessment

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Outdated Modelling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.

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02BIG DATA’S EVOLUTION

IN SMES FINANCING

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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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New Data Source – Online Big Data

710 MillionsNetizens

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 MillionsOnline 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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E-commerce Financing Platform: Ali Microloan

Generate Data from Alibaba’s Own PlatformsData 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-savingCustomers 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.

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Crowdfunding: Born to Serve the SMEs

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 Billionsreceived 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

370crowdfunding 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 regulationshigh quality and low quality platforms co-exist

13.5 Million

￥ 1 TrillionP2P loans in

2015

Investors

P2P Online Lending: Bring the Private to Public

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03THE MOST INNOVATIVE APPLICATIONS IN SMES

Macroscopic Monitoring for Nationwide Corporate Investment

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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 SectionalReferencing Index

Whole-New Indexing ParameterWith 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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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.

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.

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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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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.

HoloCreditCores

Entrepreneur General Information Module

EntrepreneurCredit Module

Enterprise General Information Module

EnterpriseCredit Module

Tax Module

Accounting Module

Related Parties Module

External Environment Module

Innovative and Extensible HoloCredit Model

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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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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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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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BBD Boosts the Financial Transformation

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THANKSFor Watching！

Data the Future