When we go to a bank for a loan we hear from the bank clerk that after analysing all the required documents the scoring system will decide if we get a loan or not. So we provide all the documents and wait patiently for the effects of the terrifying ‘scoring.’ But what is ‘scoring?’
Scoring is a tool supporting decision making. It allows to automatically arrive at a decision on a specific action to be taken, e.g. granting a loan.
The roots of scoring lie in the financial line of business. A dependency was discovered between the client’s features and the probability that s/he will pay off the loan (or generally speaking that s/he will be a reliable client). This refers to a typical client, not one that tries to deceive us. Deceiving is a different topic.
For certain reasons even the most honest clients, willing to pay off the loan, are not able to. Most frequently it is due to risk materialization but the probability of such a risk to occur may be estimated basing on client’s features.
The insurance business may be used as an example (e.g. young males cause most car accidents). There are certain features determining an unreliable client, such as small salary or having 5 children. However, this does not mean that each person having 5 children is bound to be an unreliable client. Such a number of children does not necessarily indicate that it is a problem family or that they are facing poverty. There are wealthy and well-educated families that have numerous children as well. However, this feature makes it more probable that a client will be unreliable.
The entire scoring system is based on the assumption that no one is treated individually. People are treated as population as a whole. Moreover, data used in the scoring system is historic. That is why it is crucial that the pattern of features does not change too often.
Otherwise, the model will not be suited for new clients. Scoring may not be the only criterion on the client’s reliability; although it is a crucial part of the decision making system. How crucial? That depends. It is logical that while granting small loans the clients are numerous and thus treating them individually would not make sense (while treating them as a group is much more logical). The best example to illustrate that is the liability insurance that each car owner has to have (that makes it a thriving business). However, in the case of mortgage loans individual approach to clients is far more effective.
We are great fans of scoring. We have used it ourselves and we had the opportunity to create it for various companies.
The world has changed and our daily life is inextricably linked with our Facebook profile.
We believe that the Facebook profile is sufficient to asses our personality. That is why we create a up-to-date scoring solutions based solely on data analysis from the social media profiles.

We stand out from others because our analysis is based on real users and real data. We do not create hypothetical analysis.
We ALWAYS ask for the user’s permission to analyse his data.
We analyse it within seconds and send the result in a form of assessment by points to the client.
The user decides which data he wants us to analyse.
Your assessment will be a result of a comparison of your data with data of other people.
We assess only the available information. We do not interpret the data and we are not making up any histories.
And last but not least.
SBC Fintech London 2014
Pioneers Festival Vienna 2013
Our project: scoring basing on social media.
During the last decades various models were used but the standard way is providing scoring based on loan applications. The main disadvantage of the loan application scoring is the fact that you may not ask clients for too many details. Most of them will be discouraged and those who will patently answer will probably provide a lot of irrelevant information. But Facebook data are a completely different story. There you have sufficient data which just have to be extracted and processed. How to do it? That is our task.
Why you should have it
Why should anyone buy it? Because the standard scoring does not include the information that we have on our Facebook profiles. Why scoring in the first place? One may significantly decrease the number of unreliable clients not loosing those reliable ones in the process. A complex loan risk analysis may be conducted. Scoring can be also used whenever credibility is required, e.g. cash on delivery mailing or for assessing credibility of a person on a dating platforms.
We are able to assess every feature. All you need to do is to order our application.
maciej dolinski

Maciej Doliński - CEO

Founder and CEO of Friendlyscore.
1st software startup IPO.pl in 2004. Strong business experience
at Procter & Gamble and Militaria.pl.

emilian siemsia

Emilian Siemsia - CTO

Automation and Robotics graduate, University of Technology Wrocław.
I'm developing expert systems, rule-based programming, web applications, parsing and classification natural language texts. My passion is also Webdeveloping.
I am project manager and programmer.
gideon valkin

Gideon Valkin – COO

Gideon joined the FriendlyScore team after being an Entrepreneur in Residence at the StartupBootCamp FinTech accelerator. Previously, Gideon worked at Credit Suisse and Citigroup in global financial markets for 6 years. He holds an undergraduate degree from Harvard University and an MBA from Insead Business School.

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