Methodology
Classification System
The CCAF has developed a comprehensive classification system to classify, in a consistent manner, organisations by business models. The structure of the classification system is hierarchical and built on an activity-oriented conceptual framework that groups entities based on services offered directly to clients/users.
Table 1: Overview of CCAF alternative finance classification system for Digital Lending
Vertical (segment) | Level 1 (subsegment) | Level 2 (Business Model) | Definition |
Digital Lending | Balance Sheet Lending | Balance Sheet Business Lending | The platform entity provides an unsecured or secured loan directly to the business borrower |
Balance Sheet Property Lending | The platform entity provides a loan, secured against a property, directly to a consumer or business borrower | ||
Balance Sheet Consumer Lending | The platform entity provides an unsecured or secured loan directly to a consumer borrower | ||
P2P / Marketplace Lending | P2P / Marketplace Business Lending | Individuals and/or institutional funders provide a loan to a business borrower | |
P2P / Marketplace Property Lending | Individuals and/or institutional funders provide a loan, secured against a property, to a consumer or business borrower | ||
P2P / Marketplace Consumer Lending | Individuals and/or institutional funders provide a loan to a consumer borrower | ||
Debt-Based Securities | Debt-Based Securities | Individuals and/or institutional funders purchase debt-based securities, typically a bond or debenture, at a fixed interest rate | |
Mini-Bonds | Individuals or institutions purchase securities from companies in the form of an unsecured bond which is ‘mini’ because the issue size is much smaller than the minimum issue amount needed for a bond issued in institutional capital markets. | ||
Invoice Trading | Invoice Trading | Individuals and/or institutional funders purchase invoices or receivables from a business at a discount |
Table 2: Overview of CCAF alternative finance classification system for Digital Capital Raising
Vertical (segment) | Level 1 (subsegment) | Level 2 (category) | Definition |
Digital Capital Raising | Investment-Based Crowdfunding | Equity-Based Crowdfunding | Individuals and/or institutional funders purchase equity issued by a company |
Revenue / Profit Share Crowdfunding | Individuals and/or institutions purchase securities from a company, such as shares, and share in the profits or royalties of the business | ||
Real Estate Crowdfunding | Individuals and/or institutional funders provide equity or subordinated debt financing for real estate | ||
Community Shares | Community enterprise raising money from stakeholders/locals to support social benefit. This kind of share can only be issued by co-operative societies, community benefits society and charitable community benefit societies | ||
Non Investment-Based Crowdfunding | Donation-Based Crowdfunding | Donors provide funding to individuals, projects or companies based on philanthropic or civic motivations with no expectation of monetary or material | |
Reward-Based Crowdfunding | Backers provide funding to individuals, projects, or companies in exchange for non-monetary rewards or products |
An organisation is classified in a category when its service offerings meet the definition of that category. The classification process is not exclusive, i.e. one organisation might fall under multiple categories when it offers more than one service. This approach ensures uniformity of entity classification over time.
The classification system will be periodically revised, based on internal research and external feedback from the public, to reflect the industry’s dynamic nature and constantly evolving composition.
Data Collection
Platform-based data was collected utilising an online benchmarking survey hosted by the Cambridge Centre for Alternative Finance, Judge Business School. The survey was available in English, Chinese, Spanish, Portuguese, French, German, Russian, Korean, Japanese, Thai, and Bahasa Indonesian. Participating platforms were identified by the CCAF and its academic and industry research partners. For 2015-17, the Centre’s academic partners included The University of Agder (for the EU report), the University of Chicago Booth School of Business (for the Americas study), University of Sydney Business School, the University of Tsinghua Graduate School at Shenzhen and Shanghai Jiaotong University Law School (for the Asia-Pacific regional study) and Nesta (for the UK report). The CCAF also worked with over 30 industry partners globally, assisting with information gathering, survey distribution and data verification including; Lend Academy, the Australian, UK, German, Spanish, Chinese, Nordic, Japanese, Canadian, Swiss, Italian, American and African national crowdfunding associations, the P2PFA, the European Crowdfunding Network, CrowdfundInsider and the Korean FinTech Forum, to name but a few.
The benchmarking questionnaire was designed to obtain both quantitative and qualitative data on alternative finance platforms. This includes annual transaction volumes, geographic distribution, the number and activities of fundraisers & funders, most funded sectors, total start-up and business funding, platform launch and authorisation longitudinal data, loan performance data. Additionally, a series of more specific parameters were included such as female market participation rates, proportions of institutional funding, cross-border transactions, auto-selection rates and the industry’s perceptions towards existing and proposed national regulations.
Platforms could multi-select applicable models which best described their operations (e.g. Peer-to-Peer Consumer Lending, Reward-based Crowdfunding etc.) by following our long-established taxonomy (see Table 1 and Table 2 above).
Where platforms offered models which could be classified as ‘capital raising’ or 'digital lending' but did not fit into one of the available options, they could specify further information and provide a detailed breakdown of their activities. In these cases, the research team followed up with individual platforms where necessary to ensure the volumes were assigned appropriately to relevant models. Throughout the duration of data collection activities, the research team were available to answer any platform queries and were in available to assist in filling out the survey alongside the firm.
A number of prominent reward-based platforms were unable to participate in our research due to internal policies, which would have meant a severe under-representation for both this specific model type and for several individual countries. The data for these platforms was therefore obtained using widely-available web-scraping techniques written in Python and automated using scripting to give the most reliable and up-to-date information. Once this had been carried out, the data was verified using publicly available information and through random manual sampling, after which, it was added to the final survey database.
Data Verification
Once the survey data had been collected, a multi-stage verification process was carried out - cross-checking the volumes and other data fields for anomalies and inconsistencies, in addition to assigning platforms to the correct models.
In cases where there were discrepancies or missing values, the platforms were contacted directly to resolve these issues in the first instance. If anomalies persisted, in order to further validate the volumes given by the platforms themselves, the research team firstly compared and verified the survey data with publicly available information through its website, press releases and annual reports for example. Secondly, the research team contacted our industry research partners in specific countries and regions with on-the-ground insight into the local market situation to provide feedback and help us verify data and identify potential issues in the reported figures at an early stage.
Data Analysis and Representation
All collected data was encrypted, safely stored and made accessible only to the CCAF research team responsible for analysis. The privacy of all individual and company respondents was ensured by anonymising all the data gathered from the surveys. The CCAF does not disclose individual platform information or data in any of its analysis or reports. All survey data is analysed and presented in aggregate form, be it by alternative finance models, countries or regions.
Pure numbers, such as volumes and number of funders/fundraisers, were simply aggregated together by model and then grouped by country/region to give a total for each year where data was available. This allows for better representation of each parameter, where platforms with higher volumes carried greater weight when calculating the figure for those values. This also meant that platforms that had not submitted specific values would not be counted in the average and hence artificially lower the weighted parameters.
The same methodology applies when an answer includes multiple fields, such as service offerings, the value of each field is aggregated together by model. The share, if shown, of each field is then calculated by using aggregated total divided by the number of non-empty answers from respondents. Platforms that had not submitted specific values would not impact the level calculated using those non-empty values.