Data

The information is an extremely valuable resource in the 21st century. The amount of information on the Internet increases each year and various methods are used to process these data arrays. Today we will focus on three concepts: artificial intelligence (AI), machine learning (ML) and deep machine learning (DML). There are still a huge number of methods that are used by data analysis specialists and experts, and the purpose of this article is not only to explain the features of these concepts, but also to show successful examples how some methods can be applied to solve practical problems to prevent fraud risk.

Last year Russia showed unprecedented speed of credit obligations growth. The total outstanding balance for all types of loans reached 14.9 trillion rubles. The speed of lending growth is still high which leads to new challenges for risk management. Debt refinance and consolidation programs offered by financial institutions and regulator’s actions aimed to control the debt burden are the evidence of general concern.

A bit earlier this year we published an article about how the psychotype affects credit service users financial behavior. In addition to that, in June, we launched a laboratory for the evaluation of non-personal data — psychotype.online . Today we turn back again to the topic of the psychotype influence on the borrower’s credit behavior and income assessment. Referring to our research, we will talk about various data types synergy to gather more information about the client.

Nowadays, online behavioural data is Terra Incognita. But as the industry learns how to gain better long-term insights, this type of data can still lead to the results in the short term.