We can predict anything!

July 12, 2011

conversion, marketing, underwriting model, powerful analytics API, grow your thin file customersWhat?

We’re opening some private beta trials to the DeMyst API, a tool that leverages rich public data sources; think digital footprint, telecommunications usage, and much more, to predict risk or conversion when there is minimal consumer information available.  Initially targeted at lenders, a few client meetings quickly revealed that our tool had broad applications to anyone working with ‘thin file’ customers.  So, like any good group of bootstrapping engineers, we iterated a bit, and hence, the latest version of the DeMyst API, a tool capable of predicting anything, was born.

How does it work? 

The tool excels at predicting a ‘target’ with minimal inputs/identifiers. Of course, the more identifiers the better, but the reality is, there is a bunch of rich data out there in the public domain that with a bit of aggregation, some pretty geeky analytics, and kickass technology, allows us to produce either a standalone prediction or complementary attributes for your own scorecards.    The UX is painless; just upload a decent sized sample containing whatever identifiers are available (e.g. it even works with just email!), we’ll append nifty third party data, exert our ‘muscle’, and within minutes, produce an API with a custom prediction. Yes, minutes.

Sounds cool, prove it: 

From a proud founder’s perspective, we’ve been pretty blown away by how much lift is being created by the toolkit.   We can boast a  >90% hit rate and significant growth improvements.  To prove it, we’re offering a few private beta spots so you can test for yourself.

What’s the catch? 

There are two:

  • You must be brutally honest and willing to provide us with feedback to help us refine the product.
  • You must have a genuine interest in commercially using the product if you like it (at a preferred rate of course).

To show our appreciation, we promise to provide attentive support and assistance, some free consulting help, and a certain level of exclusivity as a lighthouse customer.  This of course all comes free with your early access to a slick new tool that could massively increase your distribution without impacting your current risk level.

Early Adopter?

To reserve your spot, click here .


Bolivia & Microfinance credit data

August 24, 2010

How will Bolivia’s credit bureaus evolve?

I have been doing a little reading on Bolivian credit data recently. I don’t know much, but here’s how it seems from the outside.

Bolivia is one of the poorest nations in Latin America. Their credit bureau is still relatively nascent as I understand it, with only ~40% of the population included in the government mandated databases, and I suspect not a huge amount of trade line depth (although this is speculation, perhaps they only have negative reporting). It is a public bureau, with participation mandated by the government. Further, interest rates are significantly higher than inflation rates … at around 40% … which either speaks to a high default rate or significant information asymmetries. Are these items related? Could better data sharing directly lead to growth? I suspect so, but there are hurdles to overcome.

It’s well reported that micro-finance leading to economic growth, however, micro-finance, like any lending activity, depends on sourcing information to evaluate creditworthiness. In much of microfinance, this information is derived from what economists call an “information shortcut” … i.e. a decentralized lending officer makes a judgement based on intangibles. As personal lending markets evolve, they tend to transition towards data driven rules / scores to codify the existing processes, which lowers the search costs (by reducing the lending officer’s role and spreading the learning curve across all borrowers), improves marketing effectiveness (to those who can borrow), and reduces the risk of fraud. Perhaps most importantly, credit data allows responsible borrowers to build a reputation. As such improved credit data sharing would enable micro-finance and more traditional banking, which would, in turn, contribute to Bolivia’s economic growth.

How will this occur? It turns out Bolivia has one of Latin America’s most vibrant and competitive microfinance sectors. Perhaps sharing data among these would be feasible. This paper demonstrates that predictive models on microfinance data can predict default risk based on currently collected attributes (however there are some issues with overfit & bias in this particular model – e.g. lending officer is likely an endogenous variable in predicting risk). The overall fragmentation of this market would suggest significant returns from data sharing. However there are some regulatory hurdles to overcome, and perhaps the loan amount and volume haven’t yet reached the point at which the significant investment required to gather and process these data is warranted.

I suspect the data owners will lead the charge, if permitted by the government. I.e. the aforementioned alternative data owners such as telcos are well positioned either to sell their data to lenders, or lend directly.

If there’s anyone out there who knows more about the Bolivian credit bureaus, or microfinance participation in bureaus elsewhere, please comment.

Mark Hookey

usa : +1 646 291 6884
aus : +61 415 605 468
skype : mhookey
blog : silne.com

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US mobile players finally enter the credit card business

August 3, 2010

As in much of the developing (or on this dimension more developed?) world, US telcos are exploring their options in the credit card space. Technology players are also developing related competencies.

Consider the implications not only for credit card originators and credit providers, but also for credit bureaus. No only do telcos already have a fully fledged credit department, they also have great insight in to consumer behaviour that can supplement the traditional credit score.

Many players (including Silne) are sourcing what is known as “alternative credit data” for this purpose. Telcos have some of this in-house. It takes significant modeling expertise to tease out the insight from this, but they will get there in time.

US consumer protection wiki extract

July 21, 2010

A summary of some but not all relevant consumer protection acts in the US (directly from wikipedia) :

  • “The Home Mortgage Disclosure Act (HMDA) of 1975, implemented by Regulation C, requires financial institutions to maintain and annually disclose data about home purchases, home purchase pre-approvals, home improvement, and refinance applications involving one- to four-unit and multifamily dwellings. It also requires branches and loan centers to display a HMDA poster.
  • The Equal Credit Opportunity Act (ECOA) of 1974, implemented by Regulation B, requires creditors which regularly extend credit to customers, which includes banks, retailers, finance companies, and bankcard companies, to evaluate candidates on creditworthiness alone, rather than other factors such as race, color, religion, national origin, or sex. Discrimination on marital status, welfare recipience, and age is generally prohibited, with exceptions, as is discrimination based on a consumer’s good faith exercise of their credit protection rights.
  • The Truth in Lending Act (TILA) of 1968, implemented by Regulation Z, promotes the informed use of consumer credit, by standardizing the disclosure of interest rates and other costs associated with borrowing. TILA also gives consumers the right to cancel certain credit transactions that involve a lien on the consumer’s principal dwelling, regulates certain credit card practices, and provides a means for resolution of credit billing disputes.
  • The Fair Credit Reporting Act (FCRA) of 1970 regulates the collection, sharing, and use of customer credit information. The act allows consumers to obtain a copy of their credit report records from credit bureaus that hold information on them, provides for consumers to dispute negative information held, and sets time limits after which negative information is suppressed. It requires that consumers be informed when negative information is added to their credit records, and when adverse action is taken based on a credit report.”

… Regulation Z is getting more air time recently following the financial crisis, with lenders seeking data to help evaluate a customers ability to borrow.

Japan credit study

July 10, 2010

Great paper from PERC from 2007


“At a 70 percent acceptance rate, a Japanese
lender using full-file credit reports would have a default rate that
is conservatively estimated to be between 9 percent to 26
percent lower than a lender using any of the incomplete or
negative-only credit reports currently used in Japan.”

Removing information asymmetries through more complete credit bureau files leads to more granular segmentation and cheaper access to capital for the responsible subset within a segment.

I’m not sure how much we can take away from the regression which correlates credit bureau development to GDP.