Credit scoring, is used by lenders to ascertain the credibility of an individual for a loan, a credit facility or a financial product.
There are various credit scoring models used for retail credit assessment; higher credit scores indicate increased creditworthiness of individuals versus lower credit scores that are assigned to individuals with greater probability to default on their loan repayment.
Credit Score Assesement
Credit scoring plays an important role all through the individual’s financial life and not necessarily when applying for credit facilities. Credit bureaus or agencies in various European countries use different methods to assess credit risk
for individuals. On top on this, each financial institution possesses proprietary, country-specific credit policies and procedures and its own risk appetite. If rejected by one lender; it is possible to be accepted by another lender and your creditworthiness would still be the same.
It is no secret that EU citizens do not have a cultivated mentality towards having their creditworthiness assessed, monitored or “judged”. Something that is the case within the US citizens for over 50 years now. People think they are at the mercy of lenders or credit agencies, to this respect. However, this kind of an attitude is prevalent because of lack of information on how things work at the other end.
For executives trying to maximize value, it is important to utilize credit scoring models that are technologically advanced to include diversified pieces of information regarding the borrower’s financial standing, and more importantly adapt real time on the borrower’s financial reality. Basing the decisions on snapshots of credit events is outdated.
These are good news for the financial institutions’ heads of digital innovation. The innovative solutions that are made available can revamp the service offering and make it seem more appealing to individuals who are eager to enjoy the fruits of digital innovation (fully-fledged, quick, all-digital, customizable service offerings).
Innovative Methods for Credit Scoring
Currently, credit scoring methods are rigid. They utilize information that are incomplete representation of the individual’s creditworthiness
, since they are static and primarily based on negative credit events. The technological advancement of machine learning ensures that assessing credit worthiness can be more granular and behavioral in nature. Rather than focusing on credit events, machine learning takes into consideration the individual’s transactional behavior, which combined with advanced statistical methods, provide the borrower with a granular vision in the individual’s financial standing.
Finclude utilizes machine learning and learns from our day to day financial choices in order to provide individuals with a credit score that is more accurate and dynamic. Finclude aims to change our mentality towards credit scoring and to ameliorate our financial wellbeing
by promoting more fair assessment methods.
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