The problem (is) too much information…being inundated with conflicting versions of increasingly complex events…
The glut of information (is) dulling awareness, not aiding it.
- Jerry Mander, on information overload
Traditional Approach to Risk Scoring
In today’s world, there is no shortage of data available for credit risk decision-making, however the challenge faced by lenders is that the data available is not consolidated to enable efficient decisions. In order to undertake detailed analytics over the behaviour of borrowers and their accounts, lenders need to be able to assess correlations between data points available across a range of systems.
Further, one of the biggest challenges that lenders face is in understanding the relevance of the abundance of data available to them, categorising them in such a way that enables the lender to be able to generate outputs and derive insights.
In spite of the dynamic nature of global markets that impact the borrower’s ability to repay debt on a continuous basis, these internal ratings are not as frequent as they should be. Typically these are undertaken annually or bi-annually. This results in the inability of the lender to be able to make credit risk decisions in real-time. Evaluating any interim changes in economic conditions places a dependency on both external and internal information (data), the timely availability of which is not systematically available.
Mosaik's Dynamic Risk Scoring Model
Mosaik’s approach to dynamic credit scoring, through the application of data analytics and machine learning/AI models, allows the lender to better analyse the risk of their borrowers in near real-time, and apply appropriate strategies in marketing, pricing, underwriting on one end of the spectrum, and enables better decision-making in bad debt write-offs on the other.
Ability to tap into a large variety/volume of data - external, internal and borrower-provider, supported by our Global Datahub integration. This allows the bank to gain a 360o view of the borrower ecosystem.
Respond to risk events in near real-time, and use our business-centric ML/AI models to run stress test simulations to better understand the impact of potential risks on both borrower and portfolio.
Use our open-ended technology architecture solution to integrate with business processes for alert management workflow, and integrate with other systems for better decision making for the bank.