How AI/ML-based credit scoring accelerated a lending application to become an industry leader
Financial industry use case
Scoring in Mongolia at the time
Implementing first AI/ML-powered credit scoring in Mongolia
Combining credit and behavioral scoring
- 96% repayment rate compared to traditional financial institutions being around 80%
- The application process under 5 minutes
- Market leader for micro-lending service
- Disbursing 180,000 loans monthly on an average
And Solutions’ Credit Scoring Solution
One of the capabilities of the AND e-wallet that SuperUp benefited from was the e-wallet’s capacity to handle high-frequency transactions. As the SuperUp has many mini-apps and services, the transaction volume was high,
and it needed a wallet that could handle such volume smoothly without failures.
The AND wallet is built with open APIs, which means it has robust integration capabilities with third-party systems such as bank accounts, other e-wallets, bank cards, and over-the-counter channels. As a result, users could top-up their wallets and transfer funds to any channel of their choice. Since the wallet was also integrated with bank payment gateways, users can quickly transfer money between e-wallets and bank accounts without any fee.
User profiling and demographic data collection through direct questionnaires to customers via multiple mediums.
Device information such as manufacturing date, price, phone calls, contact information, application data, etc., can serve as an additional and accurate data stream.
Third-party data sources
Along with the data from credit bureaus, banks, and other financial institutions, third-party data such as telephone data, utility bill data is also collected to have a better and more accurate look into the customers’ behavioral tendencies.
Collected data is sanitized and stored with the desired structure for efficient data operations.
Tagging the raw data with one or more contextual attributes for use in model training.
Feature selection and extraction
Identifying, manifesting, and ranking dataset features that make the most sense of data based on accuracy and sensitivity.
Our scoring solution uses multiple AI/ML algorithms such as Random Forest, Artificial Neural Network, KNN, etc., based on the business objectives, goals, and nature. It then creates an ensemble model consolidating results from the utilized models.
Self-learning and updating
The active learning model works continuously and is fed with new data sets for supervised/unsupervised learning to make the system more intelligent and accurate.
Our base gamification schema with a loyalty system helps businesses access and identify desirable customers by generating key features that are highly suitable for modeling.