Bondora Investments Using Decision Trees – Review of Progress – Part 5

This is part 5 of a series of guest posts by British Bondora p2p lending investor ‘ParisinGOC’. Please read part 1, part 2,  part 3 and part 4 first.

The Management of Change

As mentioned in my earlier article on the construction of the decision Trees, my responsibilities when employed (yes, dear reader, I am now retired) included the successful proposal to create new teams to conduct Data Mining and produce and disseminate Metrics relating to the research activities. As on many other occasions, I was then charged with making my assertions real by staffing and then running said teams to realise the benefits I had stated should arise.

As part of my (rapid) learning in these activities, I came to understand the need to maintain processes until solid analysis could isolate and support changes. So in this review period, for those elements under my control, I have maintained certain actions within set parameters until I felt I could justify a change and then have maintained that changed process until the next time the data supported a further change.

Changes I Controlled

Given that my need to change my selection process was as a direct of seeing my money rapidly disappear (!) I limited my ongoing expenditure to the minimum purchase (5 Euros) allowed by Bondora and only made 1 purchase per selected Loan Application.

This continued throughout October 2014, when I felt that the downward trend in parts falling behind with payments was established and likely to continue. From the beginning of November 2014 onwards I increased the number of parts of any single loan application I would buy to 2, still of 5 Euros each. Note that for some application types with, for example, a higher (between 5% to 7%) indicated historical failure rate, or a very high (above 45%) interest rate; I still limited my purchasing to 1 part of 5 Euros.

This Purchasing policy remained in place until the beginning of April 2015 when my increasing confidence in the selection process, my increasing cash reserve and other factors described below, meant I felt able to increase the value of purchases (to include 10 Euro parts if I felt an application was sufficiently strong) and increased the number parts purchased of any particular loan. This latter element in particular allowed me to take advantage of events outside of my control that offered opportunities that had not previously existed, explained later in this article.

Errors in my Process
In the period October 2014 to the end of the year, I was updating the Trees twice a month. There was no detailed timetable, but the Trees did exhibit a greater degree of change in this time than was later the case. It was during the first update in December, week 51 of 2014, I noticed that the previous Tree had been built using corrupted data. It was only later in the review period that I noticed that this period – from weeks 48 to 50 inclusive – exhibited the last “spike” in defaults.

From the next update onwards (31st December 2014) I implemented a more rigorous update procedure and restricted the updates to 1 at the end of each month. I felt that this may enable changes in the Tree Structures to be more visible and so attract my attention to these changes and validate the process that had generate them, thus avoiding process errors. The fact that the datasets provided by Bondora were subject change without notice (and did so often) was an additional factor in the decision to have fewer, more rigorous build events.

I worried that fewer updates to the Trees would lead to out-of-date trees and more In Debt and Defaulting loan parts, but this has not become apparent either in daily use or this review process.
I have noticed that the Decision Trees are not static and do change over time. Sometimes – rarely – these changes occur at a high level and are very noticeable. However, the Trees have changed in a subtle way at lower, more compartmentalised levels. This is discussed later in this article.

Changes I could not Control

Whilst I have tried to maintain a tight control over my activity since starting to use the Decision Trees to guide my loan selection, there is the overall Bondora environment over which I have no control. As noted in the previous article (see part 1-3), Bondora is a dynamic environment and changes, whilst usually signalled in advance, cannot usually be planned for and just have to be accommodated when the reality of the change becomes apparent. Where possible I have noted the changes that have occurred. As part of this review, I have gone back over the last 9 months activity to try and relate these changes and how I believe they have, or may have, affected my results.

Portfolio Manager
The Portfolio Manager in place up to the end of 2014 was an automated, parameter-driven mechanism to allow investors to automatically invest in loans that meet the criteria set by the investor. From the start of 2015, Bondora made major changes to the Portfolio Manager, preceded by allocating a “Risk Segment” (running from low to high risk) to each Loan Application.

Whilst a Loan Application retained the previous Credit Score and associated Credit Group (essentially an income-related grading), these no longer played a part in the new Portfolio Manager, which no longer allowed Loan Selection by any criteria other than the new “Risk Segment”. Probably the most contentious element of the new Portfolio Manager was the loss of selection by Country. The use of Country was a critical element in the previous automated selection process for most ( if not all) investors, and its loss was not well received on the official forum.

In terms of my process of Decision Tree analysis, this changed nothing. All the previous data was still present and some new data was added about the New Risk Segment and the process associated with it. I have considered adding the new Risk Segment data to the Decision Tree analysis, but decided against this primarily as its introduction, occurring as it did some 3 months into my experiment, had the potential to dramatically alter the structure of the Decision Trees, creating a possible disconnect at this point.

A secondary reason in my decision was the fact that this data was itself the result of an analysis conducted by Bondora and for which there is no detailed discussion or publication showing how it has been arrived at. Whilst I am not surprised at the decision not to publish what is, after all, company confidential data, the output – a legend consisting of a 1- or 2-letter classification – is not an independently verifiable fact, it is merely the output from an analysis and shares this feature with my own Decision Trees.

The major difference between this and the Decision Tree output I have is the context that is provided by a full Decision Tree to those who wish to use it. IMHO, the discerning viewer can decide from the context of a complete Decision Tree whether the end point of a particular branching of the tree indeed describes a trend or is just a convenient mathematical activity that segregates the data, but reveals no trend. I offer the snapshot of Self Employment from the Decision Tree for Estonia as an example of this added value.

Decision Tree View Estonia Bondora

 

To me, the bigger picture describes a trend suggesting that the longer the applicant has been in the same employment, the less likely a default will occur. It also shows that the Decision Tree has found that those in the same employment for over 5 years can be further segregated by age, with all defaults occurring in a single age range (45 to 51). Furthermore, the sample size of the >5 years employment is 51 and the defaults, which all occur in the noted age group, amount to just 2 examples – a 4% default rate on the set of 51 as a whole. Is this further segregation a guide to investment or just a “Clump” in a larger data set? In the words of the immortal Clint Eastwood “You’ve gotta ask yourself one question: “Do I feel lucky?Well, do ya, punk?”.

Application Process

In last half of February 2015, Bondora introduced changes to the application process designed to allow applications to be assessed by Investors before all data had been collected and, where applicable, validated.

This had no immediate effect on the Decision Tree analysis, but did require minor amendments to the process. Many applications were taking up to 5 or even 6 attempts before they became fully acceptable and finally funded. Many of these rejections took place after funding was in place. They were then cancelled and re-submitted with updated data. It was important that such applications did not get counted as “Previous Applications”. This field does appear in some lower levels in a Decision Tree and therefore new data cleaning activities (explained in the previous article) had to be introduced into the process.

Server Capacity Issues at Bondora

Around the 2nd week in March, 2015, the servers at Bondora ran into capacity issues. This affected both the ability of the applicant to apply for loans and for investors to lend.

Aggregated effect of Bondora changes

Concurrent with the introduction of the changed application process and the server capacity problems, it is apparent from a chart provided by Peerlan that the new Portfolio Manager’s ability to fund loans collapsed, effectively to zero.

Portfolio Manager Funding from Peerlan - 2015-06-23 snapshot

When Bondora fixed their capacity problems, the mix of Loan Applications becoming available to manual investors had changed dramatically. Whilst this had no effect on the use of Decision Trees to select loans, it meant that many more loans became available to manual bidders. Many of these loans were Estonian, historically considered to be of higher quality.

This availability of more loans of potentially higher quality is reflected in my activity by the highest level of loan part purchases seen since the start of my use of Decision Trees. This higher number of purchases occurred even with the restrictions I had placed on myself regarding the level of purchases per Loan Application, mentioned earlier.

As I write this review, the new Portfolio Manager process has again changed, this time to run more often, with a target of running effectively all the time. This new process appears to have a dramatic effect during the 16th July, reducing opportunities for manual bidding on new Loan Applications essentially to zero, as the new Portfolio Manager process swept up all new listings.

New Loan Applications have appeared again the next day and a close reading of the Bondora “Guide to Investing” FAQ suggests that Loans that fail to be filled immediately should appear out of the back of the new process and become available to manual investing and this appears to be the case. This occurrence and the availability of loans on the Secondary Market (at a premium in most cases), leaves me feeling that my work to date has not been in vain. Time will tell!

Flip forward to the final part 6.

International P2P Lending – Loan Volumes July 2015

The following table lists the loan originations for July. Zopa originated 52M GBP and takes the lead for that month in Europe. Prosper reached 4 billion US$ in loan origination since inception. I do monitor development of p2p lending figures for many markets. Since I already have most of the data on file I can publish statistics on the monthly loan originations for selected p2p lending services.
Investors living in markets with no or limited choice of local p2p lending services can check this list of marketplaces open to international investors.
International P2P Lending Volume 07 / 2015
Table: P2P Lending Volumes in July 2015. Source: own research
Note that volumes have been converted from local currency to Euro for the sake of comparison. Some figures are estimates/approximations.
*Prosper and Lending Club no longer publish origination data for the most recent month.
Notice to p2p lending services not listed: Continue reading

Crowdcube Raises 6M from Numis

Crrowcube LogoUK platform Crowdcube today announced that it has raised 6M GBP of investment to further accelerate its growth. The investment is led by Numis, a UK stockbroker and corporate advisor. Tim Draper and London-based Draper Esprit have also joined this new funding round alongside existing backers Balderton Capital, one of Europe’s largest venture firms.

The investment will enable Crowdcube to accelerate growth, continue the expansion of its team, ramp up new product development including the creation of a new solution for companies going public, and invest further in its acclaimed marketing activities.

‘We’re on a mission to help more businesses raise the finance they need to grow, create jobs and deliver returns to investors. We’ve dominated the democratisation of seed-stage equity investment since we launched in 2011 and we’re determined to do the same for larger businesses. We want to put the Public back into IPO.’ commented Darren Westlake, CEO and co-founder of Crowdcube.

This round puts the Crowdcube at 51M GBP post investment.
Continue reading

Seedrs Raises 10M Series A

Seedrs logoUK platform Seedrs has raised a 10 million GBP series A round led by Woodford Patient Capital Trust plc and Augmentum Capital. The capital raised will be used to launch Seedrs in the US market.

Furthermore, Seedrs is to launch a 2.5 million GBP crowd funding campaign to give existing shareholders and new investors the opportunity to participate in the round. The details will be announced later.

Seedrs plans to expand its marketing efforts in the UK and Europe, increase platform development activities and launch its business in the United States.

This Seedrs valuation is now at 30 million GBP on a fully-diluted, post-money basis. I wrote a review of my experiences with investing on Seedrs in March. Continue reading

Rebuildingsociety offers one Business the First 25K Interest Free

Rebuilding Society LogoUK p2p lending marketplace Rebuildingsociety currently offers a promotion where one business will receive 25,000 GBP of their loan interest free. Loan applications received until 31 August 2015 will enter into the promotion.

Daniel Rajkumar, CEO of Rebuildingsociety.com, said: ‘Companies seeking loans now have peer-to-peer platforms as a mainstream alternative to the banks and I’m delighted to be giving a 25,000 GBP interest free loan to a business. Businesses seeking loan finance can approach us either through commercial finance brokers or directly and our platform will typically enable viable applications to get finance within 4 weeks.’

Interview with Eyal Elhayany, CEO of Tarya

What is Tarya about?

TARYA is the largest Israeli P2P lending platform built from the ground up, whose aim is to provide an online (24/7), financially beneficial experience for both borrowers and lenders. At the foundation of the platform is an advanced credit-scoring model relying on big data underwriting algorithms based on fraud prevention procedures

The platform founders have previously worked in both technology and regulation, and observing the state of the economic environment – local and global events and trends, we sense that conditions are ripe for fresh, more beneficial financial offerings to be accepted by the general public.

TARYA is headed by experts in fraud prevention technology and regulation, and has become a major player in the Israeli Crowdfunding transformation in order to supply consumer credit without bias. We are tackling regulatory, business and cultural challenges and – being the leading company – are excited and satisfied of the progress made so far.

What are the three main advantages for investors?

Diversification, diversification and diversification. The key to successful investments in P2P is diversifying the investor’s portfolio by offering:

  1. Automated Investing and Reinvesting – the Investor defines risk and return preferences, while the platforms algorithm allows for a hands-free experience.
  2. Varied borrower types – TARYA continues to develop partnerships with employers and social projects across the nation, thus providing both solid and risky borrowers, from different sectors of the Israeli economy.
  3. Low minimum participation amount in loans – The minimum amount for investing in a loan starts at only 50 NIS (equivalent to 10 Euro).

What are the three main advantages for borrowers?

TARYA being an online financial service, provides borrowers an efficient and up-to-date approach for loan application:

  1. Transparency – The process is performed online, without having to “wait-in-line” at the bank. Payments, interest rates and terms are presented up-front with an emphasis on “consumer protection”.
  2. A FinTech Experience – TARYA is a unique and pioneering initiative that facilitates the direct connection between borrowers and lenders, using an online platform. This platform bypasses existing credit entities – banks and credit card companies, and allows borrowers to obtain credit at significantly lower costs. Interest rates range from 3.5%-8.0% depending on the borrower’s credit rank. Of note, non-banking (credit cards) interest rates for borrowers average 11%.
  3. Business Partnerships – TARYA’s unique model grants upgraded loan terms for borrowers whose personal details are authenticated by their employers. This practice benefits all employees of the organization, who can receive loans at rates above their personal credit rating.

eyal-elhayanyWhat ROI can investors expect?

Lenders investing in diversified and micro-financed portfolios average between 5%-6% returns after fees. Lenders pay a fee of 1.0% on returns.

How did you start Tarya? Is the company funded with venture capital?

We believe the venture has the potential to change the structure of the credit market in Israel: It is widely accepted that the Israeli banking sector is concentrated and not competitive, especially in regard to the household and small businesses sectors. Since setting up shop in May 2014, TARYA has been growing in borrowers, lenders and partner organizations.

TARYA is funded by private equity. Since establishment, we’ve received several applications from VC and institutional investors.

Is the technical platform self-developed?

The platform is developed internally, built from the ground up with underwriting and credit rating processes that combine banking know-how, statistical modeling and technology professionals with extensive expertise in online fraud and information collection from social networks and other sources.

The diverse expertise of our team and the advanced technology is giving us a competitive advantage and will enable us to confront upcoming challenges head on. Continue reading