Clearly hard times haven’t hit your household.

I’m sticking with http://www.lendingclub.com. My loan portfolio there is yielding close to 9% after defaults. Also, today http://kiva.org announced they are launching microlending in the US.

this company is encouraging bad financial practices. not cool. Accel, Greylock I’m looking at you

I frequently post technical information here at TC, just to share knowledge with other developers, but sometimes I frequently see unnovation startups (in Umair Haque’s words) and it is something that I am curious to know about in how those venture investors get to pour money into such startups.

Wonga developers may be interested in checking out the following paper (if it isn’t the decision engine that you’re currently have) on using FIS (fuzzy-logic inference system) in loan decision-making. Wonga’s use of slides (on their main page) to indicate variables as amount and duration fits perfectly with the paradigm of FIS. FIS is a universal approximator, exactly as ANN (artificial neural network) is and they’re structurally the same (ie, FIS & ANN), however computationally different. You can design a MIMO (multiple inputs multiple outputs) or MISO (multiple inputs single output), where the latter is the one I suspect that you’re adopting if not FIS but ANN or SVM (support vector machine). The advantage of FIS over ANN & SVM is that you know your decision rules as a priori.

Abstract:
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Credit risk has always been a major concern for banks and other financial intermediaries. The uncertain domain of risk assessment has long been in need of a reliable and consistent system to help simplify the decision making process. In recent years fuzzy logic technique has been in its wide-ranging use in modeling of uncertainties, vagueness, impreciseness and the human thought process. This paper attempts to develop a credit risk analysis system based on the fuzzy logic using two structures: traditional fuzzy reasoning of all inputs that map to one single output and stage-wise fuzzy reasoning of input parameters in accordance with their importance. The analysis of case studies shows the consistency and effectiveness of the second approach in making correct decision.

Title:
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Loan risk analyzer based on fuzzy logic

The authors of the paper cited above are from School of Informatics at Bradford University, so it would very convenient to ask them a question since they’re in the UK.

I have used both Mamdani & Sugeno inference algorithms in my FIS for various previous projects and I think that Mamdani is the one that best suits your application rather than Sugeno. There are other inference algorithms for FIS that have appeared in the literatures over recent years which are more advanced (ie, more accurate), but their application domain mainly for control systems, however I am sure that they can be used in other areas such as decision support system for online loan such as yours.

http://blogs.harvardbusiness.org/haque/2009/06/wonga.html

http://blogs.harvardbusiness.org/haque/2009/06/wonga.html

I guess it is applicable in this case too (you want money fast – because you might not have credit – you pay more).

It’s shocking how successful this start-up could be but not surprising.

The need is out there, but there again there’s also a need for web 2.0 sites that can help someone buy heroin find a prostitute. I would not want to work in either one. And I probably wouldn’t want to work with any VC who is willing to back usury.

Sad day for Accel – I would have expect more from them.

With Wonga’s “selective” loan issuing policy I reckon they work to the same sort of model (eg you make the profit on the 3rd time a customer uses their service). So in my humble opinion companies like this are simply sharks. You can dress it all up in swishy web 2.0 logos, but at the end of the day Wonga are just the same as Provident and MEM. All companies that pray on the weak and encourage bad financial habits.

Sure we are all adults and are free to make our own choices in this world. But I personally think running a business like this is bad energy, and I think that people intelligent enough to setup their own business should be thoughtful enough to contribute something good to this world, not something utterly scummy such as this.

No words of good luck or congratulations from me either.

Cant wait for this service to get shut down.

I URGE folks to read their TOS about what happens on a default or how many days they give you in case you miss the payback date. In the U.S. many of these companies have the RIGHT to freeze your bank account.. which means you have no way to pay anything, not even get food for yourself, until they are paid in full. Once paid in full they release the bank account. It may seem harsh, but with so many people who default in this day and age it’s a “cover their rears” sort of policy. I can’t fault them for it, but dang does it add stress to one’s heart. Urgh.

Im not saying it is against the law to run this kind of despicable business, im just saying it is a despicable business.

I have 3 very simple rules I look at when evaluating a business opportunity. Is it legal, moral and ethical? This business fails the test.

Oh! Really? REALLY? hehe

What is this thing in your pocket called Credit Card then?

You are just plain funny here, geeks.

I absolutely understand your reservations about money lending, but I feel like usury in general needs to be considered more carefully than you’re allowing for.

If you’ve got a fundamental issue with profit being made on people requiring credit, it would seem odd to confine it just to this space. The amount repaid on a credit card over 5 years, or the exorbitant fees charged by banks for unauthorised overdrafts, would seem equally problematic.

The difficulty is that the economy, particularly now, absolutely relies on credit to allow consumption that in turn fuels growth and creates jobs. The key issue is, that credit which sustains consumers, not be structured in such a way that removes the possiblity of ever being debt free or finanically stable in the future. I feel this is the key criticism that can be levelled at credit cards, where consumers end up treating their credit cards like a second mortgage hitting minimum payments ad infinitum.

So do I think Wonga are loans sharks? In truth, I do not. In many ways I feel more comfortable with this kind of lending which is complete in a single salary cycle, than I do with the inescapable credit card burdens hanging over most of our heads! We’ve built a society around credit, now we need to be measured in our analysis of how to make it work for us in the future.