With credit risk assessment software such as PARC, the credit allocation process becomes more efficient, as the analysis processes will be faster.
The risk assessment process is analysed with greater rigour, thus preventing credit approval that could mean a high exposure to risk for the financial institution and a possible default scenario.
By automating credit risk analysis with PARC, both the risk of human error and associated costs are reduced.
The platform introduces a greater degree of trust between all those involved in the credit granting process by providing risk assessment in situations where credit was incipient or non-existent.
Compatibility with current systems
Software solution with fast integration into current systems. The solution is made available as an API REST with JSON messages. The endpoints allow individual or batch rating creation operations. The entire infrastructure resides on AWS, following the Well-Architected Framework standards which results in a serverless, high availability, high performance and secure solution.
The PARC rating model produces the probability of a loan going into default. This is the detection of a minority class. Studies show that the cost of misclassifying a bad credit as good is five times higher than the cost of classifying a good credit as bad. However, the platform allows the variation of parameters (amount, term and effort rate) to analyse the possibility of lowering the risk to acceptable levels, opening up the possibility of trading the loan.
