[wpseo_breadcrumb]
CREDIT SCORING
Powered by AI, ML, and accumulated business know-how
Offer better customer experiences and unique selling points:
Shorten the TAT (turn around time)
Make everything online
Financial accessibility to certain customer segment
Better loan conditions leveraging unique data you have
ML modeling approach
When there is not enough data available for a ML based advanced modeling, we start providing loans based on a simpler score with available data.
It is essential to collect wide variety of customers’ data during these phase, therefore only highest risk users should be excluded.
Once enough data collected, credit scoring model, which predicts probability of NPL, will be built utilizing machine learning algorithms.
In real business use case, it is common to use hybrid version of rule-based logic and ML model results as credit scoring logic.
In order to keep up with constant changes in the business environment and technical advancement, it is important to keep monitoring the model performance and improve when necessary.
Fully tailored
Tailored to clients specific needs
Value
Tap the hidden value of your data
Expertise
Only expertise only we can offer
Hyper-localized
Data collection & credit assessment logic
Adaptable, Intelligent, and Self-improving Scoring System
We design, develop and deploy custom credit scoring systems to solve the unique challenges of your business and customers, while providing hassle-free customer experience.
Assess and score new customers’ creditworthiness based on traditional financial data and also non-traditional behavioral data.
Identify, classify, and target customers
based on activity data of behavioral
tendencies.
Diversify your business with additional products and services with accurate targeting and higher conversion rates.
Why choose our
Accumulated scoring experience and know-how in various financial markets
Flexible and customizable modeling method tailored to your unique business needs
Accumulated scoring experience and know-how in various financial markets
Technology Modules & Features:
Data collection
Direct questionnaires
Personal and demographic data collection through direct questionnaires to the customers. Used for customer profiling and classification, necessary for understanding specific customers overall status.
Third party data sources
For credit scoring usually includes credit bureaus, banks and other financial institutes to take a look at the customer’s financial state and capacity. It also could be used with telephone carrier company data, utility company to have better and more accurate look into the customers’ behavioral tendencies.
Device information
As a more alternative data source device/phone data could be utilized as well. This usually includes, device manufacture date, price etc and also phone calls, contact information, application data.
Data analysis
Data structuring
Personal and demographic data collection through direct questionnaires to the customers. Used for customer profiling and classification, necessary for understanding specific customers’ overall status.
Data labeling
Credit scoring usually includes credit bureaus, banks, and other financial institutes to take a look at the customer’s financial state and capacity. It also could be used with telephone carrier company data, utility companies to have a better and more accurate look into the customers’ behavioral tendencies.
Feature selection and extraction
As a more alternative data source device/phone data could be utilized as well. This usually includes device manufacture date, price, etc, and also phone calls, contact information, application data.
Model training
Our scoring uses multiple models such as Random Forest and Artificial Neural Network based on the business goals and nature of it. Then creates an ensemble model consolidating results from the utilized models.