Daily Archives: 13th April 2017

Analysis of Similar Systems

Mining financial and social scores from social network data

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Image courtesy of cafecredit

Introduction

Britizen explores the concept of a social network where the social ‘credit score’ of members and the groups they belong to are co-dependent. This posts considers the academic literature related to developing financial and social credit scores from social network and online shopping platform data. As a result of this research we decided that in the UK it is more feasible to introduce a ‘social’ rather than ‘financial’ credit score social media application.

Analysis of similar Systems looks more closely at existing online social network systems.

Determining credit scores

In the UK, banks and other financial institutions use historical data to evaluate how ‘credit worthy’ an individual customer appears to be. This is used to determine the risk involved in making each loan, and the appropriate interest rate and or security required.

Credit reference agencies such as Experian[1] and Equifax[2] are used to share knowledge of consumer credit behaviour. The financial history of businesses is also recorded and shared via credit reference agencies such as Moody[3], Standard & Poor[4], and Fitch Ratings.[5]

In some countries (including China) micro businesses and consumers find it difficult to obtain credit from mainstream banks and financial instructions because these bodies do not hold such data about them (Kshetri, 2016; Shu, 2015; Wei et al., 2014) Data about online consumer behaviour can be gleaned from:

  • transaction data held by online shopping portals such as Alibaba[6] and Amazon.
  • online payments records from online payments systems such as Alipay[7]and PayPal
  • non-financial data gleaned from users posts and relationships on social media platforms such as Facebook[8]

China Rapid Finance claim to use both financial and ‘unstructured online social’ data[9] to predict whether a borrower is likely to repay a loan. Kapron (2016) suggests that it is unlikely that systems of this kind could operate in the US due to privacy concerns. in general the EU has stricter privacy legislation than the US; it is thought unlikely that use of unstructured social network data for financial credit scoring would be considered acceptable in the UK at present.

Sesame Credit uses a combination of five factors to assess credit. These include “users’ credit history, behavioral habits, ability to pay off debts, personal information and social networks” (China Daily, 2015).  Members can choose to publish their score. Gamification encourages competition between users as to who has the highest score; and incentivizes ‘desirable’ behaviour (making payments promptly). Users also have a direct incentive to recruit others to the platform: their own score increases with the number of their friends. Game theory can be used to model such effects.

In our focus group a Sesame Credit user commented that most Chinese people ‘just know that the more they consume, the higher mark they will get‘. Gamification in Sesame Credit appears to successfully modify user behaviour;  spending more to increase a personal score and obtain more benefits was a common thread.

Facebook have acquired a US patent[10] which includes using the credit rating of a user’s social network to determine whether to reject or proceed with a loan application (Lunt and Facebook, 2012). Kapron states that they have run and withdrawn a pilot experiment on these lines.

Academic literature includes Selde’s study which determined that Facebook data can be used to derive a useful financial credit score (Selde, 2013). Wei at al  also researched the potential effects of using social connections and data to determine credit scores; and suggest that low scoring individuals might be motivated to improve their behaviour in order to improve their scores.

Wei et al also concluded that there is a risk groups will become stratified (homophily) to protect their scores. In a different context; Kossinet and Watts carried out an empirical study into evolving social networks at a large university; and concluded that  the presence of strong ties between two strangers and a mutual aquaintance was stronger than the effects of homophily expressed as class, age, and gender in that situation. If this effect also applies to Britizen it could reduce the tendency of strong triadic closure to result in a very stratified network.

References

China Daily, 2015. Sesame Credit helps you open sesame – Business – Chinadaily.com.cn [WWW Document]. URL http://www.chinadaily.com.cn/business/2015-01/28/content_19431324.htm (accessed 3.4.17).

Kapron, Z., 2016. Measuring Credit: How Baidu, Alibaba And Tencent May Succeed Where Facebook Failed [WWW Document]. URL https://www.forbes.com/sites/zennonkapron/2016/03/17/measuring-credit-how-baidu-alibaba-and-tencent-may-succeed-where-facebook-failed/#332b551327c2 (accessed 3.2.17).

Kshetri, N., 2016. Big data’s role in expanding access to financial services in China [WWW Document]. URL http://dl.acm.org/citation.cfm?id=2906812 (accessed 3.2.17).

Lunt, C., Facebook, I., 2012. Authorization and authentication based on an individual’s social network. US9100400.

Selde, E., 2013. Study of credit scorecard using only Facebook data – Big Data Scoring.

Shu, C., 2015. Data From Alibaba’s E-Commerce Sites Is Now Powering A Credit-Scoring Service | TechCrunch [WWW Document]. URL https://techcrunch.com/2015/01/27/data-from-alibabas-e-commerce-sites-is-now-powering-a-credit-scoring-service/ (accessed 3.2.17).

Wei, Y., Yildirim, P., Bulte, V. den, Christophe, Dellarocas, C., 2014. Credit Scoring with Social Network Data. doi:10.2139/ssrn.2475265

[1] http://www.experian.co.uk/

[2] https://www.equifax.co.uk/

[3] https://www.moodys.com/

[4] https://www.standardandpoors.com/en_US/web/guest/home

[5] https://www.fitchratings.com/site/home

[6] https://www.alibaba.com

[7] https://global.alipay.com/

[8] https://www.facebook.com

[9] http://chinarapidfinance.com/

[10] US9100400

Related Academic Work

Introduction: What is an Online Social Network System?

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Social networks have existed for friendship, business and other reasons long before online groups were created. The Freemasons are thought to have originated in the Middle Ages. Boyd and Ellison (2007) assert that Community Memory was the first computerised bulletin board community in 1973 . Bulletin boards were largely replaced by web based Internet Forums; many of which have been superseded by the social network systems ubiquitous today.

Boyd and Ellison define Social Network Sites (SNS) as

” … web-based services that allow individuals to

(1) construct a public or semi-public profile within a bounded system,

(2) articulate a list of other users with whom they share a connection, and

(3) view and traverse their list of connections and those made by others within the system. The nature and nomenclature of these connections may vary from site to site.”

Typically:

  • SNS allow users to make their social networks public
  • large SNS enable users to maintain their connections with offline groups
  • users can contact new people via a shared third party connection with offline connections to both parties
  • user profiles are included
  • bidirectional confirmation of ‘friendship’ is required (eg Facebook)
  • some (such as Twitter) allow unidirectional ‘followers’ or ‘fans’
  • public messages can be posted on friend’s profiles
  • private messages are allowed between friends
  • initially SNS often attract homogeneous groups
  • some SNS are deliberately exclusive

Popular SNS in 2017 include Facebook, Twitter, and LinkedIn. Britizen is a conceptual design for a SNS who primary purpose is to influence British society.

 

Reference

Boyd, D.M., Ellison, N.B., 2007. Social Network Sites: Definition, History, and Scholarship. J. Comput. Commun. 13, 210–230. doi:10.1111/j.1083-6101.2007.00393.x

 

 

 

Business Plan, Revenue model

Britizen Revenue Model

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Image about monetizing Web 2.0
Dion Hinchcliffe

Britizen will be free to use for consumers; and funded by multiple income streams. The initial sources of revenue will be advertising and sponsorship.

Once Britizen is established there is potential to generate income from additional sources. The concept and software can be franchised or licensed to other governments. Anonymised user data can also be sold to carefully chosen partners.

Profits will largely be used to fund benefits for Britizen members; creating a virtuous circle in which more members are encouraged to join and existing members encouraged to stay as benefits increase. This is known as a cross network effect (Shuen, 2008 p.42). The value of an online social network to advertisers and sponsors increases with the number of activate members; generating increased income and profit which can be ploughed back into member benefits.

Part of the Britizen ethos is to support the British economy; all sponsors and advertisers will be British brands. Platform users will be encouraged to support British businesses.

A sample advertisement on a use timeline is shown in Figure 6 of the mockup design.

 

Early stages

Sponsors and advertisers will provide the initial external revenue sources. The value proposition (Kaplan and Norton, 2000) for these actors will be carefully formulated. Initial sponsors and advertisers need to be attracted to spend their advertising budget on Britizen instead of other social media platforms.

Britizen is a new concept; sponsorship opportunities will initially be focussed on the ‘Innovators’ of Rogers Adoption Model (Rogers, 1983); visionary and imaginative businesses who will produce exciting, attractive materials for the site. The next group of potential sponsors (Rogers ‘early Adopters’) should have strong brands and use innovative techniques to gain a social and economic edge. These businesses (such as Virgin and Innocent) are likely to be willing to form partnerships contributing to the success of the platform.  Early sponsors may be offered advantageous rates as inducements to partner with Britizen. The platform needs to generate sufficient income to minimise burn rate (Shuen, 2008, p.24), create a positive cash flow and sustain growth quickly, as HM Government cannot afford to continue providing a subsidy long term. Maximising profit is less important than establishing a strong consumer brand and customer value proposition at this stage; in the short term it is more important to attract large numbers of active users quickly.

The involvement of trend setting sponsors and advertisers will help Britizen appeal to the early user adopters of the platform, and benefit from exposure to these users.

Moving from early stages towards maturity

As the platform expands and matures established high profile businesses such as Vodaphone, Marks and Spencer, and major supermarkets will also be targeted as sponsors. Similar to Rogers ‘Early Majority’; these businesses need solid proof of benefits but are comfortable with innovative ideas. They offer strong customer service and support which appeal to the ‘Early Majority’ consumers the platform will aim to bring on board at this stage. They will be encouraged to extend the user benefits with special offers, discounts and tokens similar to those offered by Tesco Clubcard Rewards and Airline Miles Reward Programs. Such inducements are targeted to the increasingly wide user base.

Maturity

Britizen will continue to generate revenue from sponsors and advertisers as the platform matures and the number of users increases. As the platform develops a track record and large user base revenue will increase; allowing an increased amount to be spent on benefits attracting and retaining users as well as funding increased growth.

Evidence that the platform is successful is also a sound basis to franchise or license the Britizen concept to other governments. These governments may use the platform to encourage similar or different behaviours appropriate to their culture and situation.

Consumer data will be increasingly valuable as both the number of users and quantity of longitudinal data increases. As well as providing a rich source of data for government analysis; there is potential to sell anonymised date to universities and other research organisations. It is important that users build and retain trust in Britizen; such data sales need to be handled sensitively.

 

References

Kaplan, R.S., Norton, D.P., 2000. Having Trouble with Your Strategy? Then Map It [WWW Document]. Harv. Bus. Rev. URL https://hbr.org/2000/09/having-trouble-with-your-strategy-then-map-it (accessed 4.13.17).

Rogers, E.M., 1983. Diffusion of Innovations, 3rd ed. Collier Macmillan, New York.

Shuen, A., 2008. Web 2.0: A strategy guide. O’Reilly Media Inc., Sevastopol, CA.