Risk Management

This page explains the use case of Risk Management System on Finscale.

Base behavior includes Automation of Underwriting with Analytics assessment at a unit level of financial product origination say loan origination. Secondly, the behavior includes assorted provisioning of financial product templates, (translated as an offer say a loan offer). Thirdly, the behavior includes generating control methods and triggers for Risk Mitigation driven by systematic reporting, both inside and outside the system.

Summary:

  1. Automation of Underwriting for a single origination application: A decision journey screens and filters eligible applications and associates an assessment score based on a calibrated model. The bulk of originated applications are either auto-approved or rejected while flagging a few for supervision.

2. Product mapping with the originated application to translate into a product offer:

A matchmaking algorithm that depends on already configured products and the relationship between the scorecard of the stated origination application.

3. Identifying a library template of the variable configuration of a Product. Identification of static factors with dynamic values, E.g loan product defined with ranges of amounts in consideration such as Loan Amount, Approved Loan Amount, Disbursal Loan Amount

Ability to group these products for an entity to entity-based mapping.

Such as loan products offered to a specific group e.g in case of salary advances, Employers, or companies as entities. Another example is of women-oriented loan or income ranges. The loan product association with configurable factors are enhanced by the data model as below.

Please supply the data model here.

4. Reporting and information representational standards of such a machine

Type of Analytics Reporting Standards.

5. Identification of control methods and recovery triggers for Analytics management(both manual and auto)

A closed-loop process must be designed that is explained as follows:

Loan Portfolio at Analytics is tied to Loan Portfolio Below PAR, Above PAR, other such classification. Factors such as Loan Loss provisioning further requires establishing a sound relationship with control methods and their application. It is also required to identify and assess the role of machine learning models that supersede the manual rules intervention in a non-directional movement of data process.

6. The configurable subsystem of Analytics Management provides grounds for customizing different data models and entity to entity mappings.