Perspective:
Managing credit risk is always a complex problem to solve with ever changing market volatility and customer demographics. Here at iSteer, we help our client realize the potential of AI & ML in risk evaluation of their customer credit worthiness and lending personalization using the credit risk models, not only limited to user behavior but by adding geo-political and economic status of the terrain of consumers.
Business Challenges in Consumer Loans
Archaic form of risk evaluation:
Perhaps paper based, or traditional risk modelling techniques are resource intensive and resulting to many denials.
Virtual Data Layer:
Credit firms establishing centralized virtual data store combines omni-channel data leveraging SOA principles such as decoupling, reuse, and agility with key information governance principles, such as abstraction, shared semantic models and data standards, to enable IT to build and deploy a layered, enterprise-wide data architecture, this serves as a unified, enterprise-wide view of customer, demographics, Bureau, loans, marketing, utilities and other “Alternate data”
No utilization of “Alternate Data”:
Organizations not realizing the value borrower’s current situation or other extenuating factors, such as demographics, Utilities, Insurance details, resulting to loosing borrowers to competition business.
Individualized Approach:
Lack of more granular and individualized approach limiting the business not to oer the differential pricing of interest rates Omni-Channel experience
Lack of cohesion in the organization to treat th omni-channel borrowers and lack of providing the customer 360, leading to customer attrition. A data foundational layer such as data lake powered by TIBCO can be a solution for managing the disparate data coming from various channels for its correctness, completeness, accuracy and provision of real-time data services.
The Proposed Solution:
A data foundational layer such as data lake powered by TIBCO can be a solution for managing the
disparate data coming from various channels for its correctness, completeness, accuracy and provision
of real time data services.
Virtual Data Layer :
Credit firms establishing centralized virtual data store combines omni-channel data leveraging SOA principles such as decoupling, reuse, and agility with key information governance principles, such as abstraction, shared semantic models and data standards, to enable IT to build and deploy a layered, enterprise-wide data architecture, this serves as a unified, enterprise-wide view of customer, demographics, Bureau, loans, marketing, utilities and other “Alternate data”
Enterprise Data Lake
An enterprise data lake combines streaming and interactive analytics and operates on based huge volumes of the structured, semi-structed and unstructured data pertaining to this solution , in a single easy-to-manage distributed cluster. Helps to persist the data and run heavy machine learning risk models to , This can be an optional data layer and iSteer recommends when there are heavy analytical work loads should be considered on vide varieties of data
Data science platform
This layer anchors as advanced analytical engine for the model selection, model training , configuration and create necessary workflows. The probability of the default score, customer credit risk profiling , customer segmentation and many more outcomes will be produced using state-of-the-art-data science platform allows organizations to expand the data science deployments across the organizations with flexible authoring and deployment capabilities/
