Big Data, Small Loans

Press

Kit

Big Data,

Small Loans

Big Data,

Small Loans

Private credit bureau coverage (% of adults)

100% 90%

80%

70%

60%

<50%

About 5 billion people worldwide still have no

access to credit products. This is exactly why the

Hamburg based BIG DATA startup Kreditech has

developed a completely new scoring technology.

Unlike established scoring methods, loan

appli-cations can be evaluated around the clock within

a few seconds using over 8,000 data points and

paid out worldwide.

Since its founding in early 2012 Kreditech is

expanding into Poland, Spain, Czech

Repub-lic, Russia and Mexico, has collected 10

mil-lion Euro venture capital and hired over 75

employees. Currently loans worth more than 14

million Euro are being issued annually.

From Application to Disbursement in less than 10 minutes.

00m 00s

00m 45s

01m 30s

06m 15s

Kreditech / Press Kit 2013

How data can be economically useful worldwide and how big-data scoring will soon make classical branch banking obsolete.

At the moment, not a day goes by without headlines on the topic of big data. The hash tag “bigdata” has had a permanent place on the top ten Twitter charts for months. Politi-cians, companies, data experts, and users – everyone is talking about it. But what exactly does this term mean? “Big data” means genera-ting insights and conclusions from enormous, heterogeneous volumes of data. The term itself is merely a description of something that in principle already dates back hund-reds of years.

Data Volumes will

double by 2014

If one considers the volume of all stored information from the be-ginning of humanity (e.g. stone tablets, books, compositions, films, etc.) up to the year 2010, this amounts to an overall data volume of 0.18 zettabytes (or 18,000,000,000,000,000,000,000 bytes). Between 2011 and 2012, the data volume amounted to 1.62 zettabytes. This means that in 2012 one billion new data sets were created every minute. The volume of information is not getting smaller – to the contrary! In order to make this abundance of data economically usable worldwide, special techno-logies are required to draw usable conclusions from this aggregate and obtain applicable information: big data.

It is, however, notable that the term “big data” has frequently received negative connotations since the beginning of the NSA scandal in June. While the media is attempting to sensitize society to the topic, the actual meaning of “big data” is scar-cely given any attention in practical everyday life. The economic utility does not only play a role for global corporations; it already affects all of us.

Today, more information can be collected and processed using technological developments in cloud computing with high-performance servers and extensive data centers than could have been imagined ten years ago. The challenge that big data presents is not the blind coll-ection of data but instead concerns evaluating this data meaningfully and quickly and making it usable.

Risk Assessment through

8,000 Individual Pieces of

Data

The technology company Kreditech, with its headquarters in Hamburg, uses big data to issue online loans in emerging markets worldwide. These are countries in which private individuals have either no or difficult access to consumer loans. Take Russia as an example: if an indivi-dual plans to take out a small loan from a bank for a car repair, then this person must provide a second (solvent) guarantor or borrow the money privately. This is nearly impossible to accomplish at short notice. This is due to the situation that there is no credit assessment for private persons and thus no indicator for the bank for the default probability and the risk associated

with issuing the loan. The expenses for individual risk assessment per customer would be too high. The benefits of big-data scoring become evident when one considers the number of private individuals worldwide who have had no credit assessment and thus no access to loans. In growth markets in par-ticular, there is still an extremely high number of households without their own credit score. This affects 75% of the population worldwide, or five billion people. The private banking sector is missing out on an enormous source of business, as an individual risk assessment cannot be achieved in every individual case. Big-data scoring allows a credit score to be determined quickly and reliably in order to be able to give these people fast and simple access to loans.

Here is where Kreditech’s plan co-mes in, which issues online loans in Poland, Spain, the Czech Republic, and Mexico with its service Kre-dito24, as well as in Russia under the brand Zaimo. Using a real-time scoring procedure, up to 8,000 diffe-rent points are evaluated to assess creditworthiness so that the appli-cant can receive their loan under a minute. A total of 250,000 applica-tions have already been processed internationally since the launch. The amount of the online loans varies between €50 and €2,500.

Big Data Is Disrupting

Established Banking

What is a credit score?

A credit score is a uniform index value for the

creditworthi-ness of a private individual. The better the value, the more

probable the repayment of the credit or the lower the risk of

default. The credit score is based on different factors, such

as place of residence, amount of income, previous payment

performance, age, profession, and additional factors.

Stan-dard credit bureaus such as SCHUFA or FICO consider

ap-proximately five factors in their scoring. Kreditech uses its

technology to assess several thousand factors to determine

a credit score.

The Business with Big Data

What exactly happens over the

course of big data scoring?

Kreditech uses these methods to analyze applicants and derive know-ledge about their creditworthiness. Through the combination of vari-ous data such as place of residence, last available cell phone position, information about the workplace, surfing patterns, and user behavior on the entry page as well as social media profiles, direct or indirect

criteria for the final assessment of the creditworthiness can be derived. In doing so, it is not the individual factors that play a role but instead the combination of all data points. This can be envisioned as a large mosaic composed of different small fields that first results in a complete and coherent overall picture when it is completed. Google and Facebook have essentially also used such algorithms and data analysis tools for years to present targeted advertising to the users. Big data is thus not only an essential resour-ce; it is also the basic material being used to create both new business models and entire economic sectors. The comparison in the current discourse featuring big data as the oil of the 21st century certainly remains to be proven. There is, however, already a growing economic dependency on data and its uses just as the dependence on other important resources which have once influenced the economic growth of industries.

74% of world population

is „unscored“

5 billion people without an

individual credit score

Established Scoring in

developed markets

5 scoring factors are used to

determine credit worthiness

Address Information stat. Data Length of credit history Capacity used

30%

35%

15%

10%

10%

Kreditech / Press Kit 2013

Payout after 48 min. Age: 18-34 45% one kid or more 54% single

average microloan customer average Kreditech customer

Our Average Customer

54%

46%

45% no kids 55% one kid or more 43% married 42% single Prepaid Card (2014) Mobile Applications 25% Online Applications 75% Level of interactivity Amount of credits Extending product range

Mobile Online Offline Payday Lenders Online Microlenders Retail Banking

1.200%

8.000

02.2012 Founding Kreditech

by Sebastian Diemer (CEO) and

Alexander Graubner-Müller (CTO)

09.2012 Kredito24.pl

Launch of first micro loan platform

for B2C market in Poland

02.2013 Kredito24.es

Launch in Spain

05.2013 Zaimo

Launch in Russia

as first country outside Europe

07.2013 Hafencity

Moving into new, bigger

1,000m

_office

09.2013 First Debt Refinancing

Kreos Capital

(5mn EUR/7mn USD)

11.2013 New Loans in Poland

up to 2,500 € and installments

01.2012 Foundation Investment

Heiko Hubertz, Felix Haas,

YoungBrains

$

$

$

$

$

$

$

$ $

$

05.2012 Seed Investment

Point Nine Capital, Stefan

Glänzer, Michael Brehm

01.2013 Series A Investment

Blumberg Capital, Point Nine

Capital, Samwer brothers (Global

Founders Capital), Heiko Hubertz

01.2013 Full Automization

of Big Data Scoring-Process

04.2013 Kredito24.cz

Launch in Czech Rep.

09.2013 Kredito24.mx

Launch in Mexico as first

american country

data points per application

new hires / week

country entities in Poland,

Spain, Czech Rep., Russia,

Mexico, Argentina,

Australia, Ukraine, Peru

& Dominican Rep.

NERF Guns

applications / day

∅

raised by most influential European Business Angels

revenue

growth

in 2013

10.2013 Break-Even in Poland

by less than 7% default

2014 Kredito24

Launch in Australia, Argentina, Peru,

Ukraine & Dominican Republic

2

10

90

14 Mio. USD

average decision time per application

43 sec.

1.000

issued loans in 2013

14mn €

credit applications until October 2013

MORE THAN

250.000

re

venue e

ver

y quar

ter curr

entl

y

DOUBLING

Kreditech / Press Kit 2013