May 30, 2019

• Yoma Bank implemented a credit scoring model for automated and intelligent lending through digital channels.
• The unsecured lending solution provides Yoma bank with a platform to combine big data analytics, machine learning and alternate data-based credit scores to make better lending decisions.
• The bank witnessed a strong growth in new customers and average transactions per customer, with a 70% repetition rate. 

Bangkok, – May 30th 2019 – Yoma Bank and Experian were awarded the Best Data and Analytics Initiative, Application or Programme at The Financial Technology Innovation Awards Programme 2019. The awards ceremony was held in conjunction with The Future of Finance Summit, the foremost annual meeting for decision makers in the financial services industry in the Asia Pacific region, held at the Shangri-La, Bangkok on May 30th 2019.

Yoma Bank implemented a credit scoring model for automated and intelligent lending through digital channels

Myanmar based Yoma Bank introduced ‘Smart Credit’, an unsecured lending solution delivered through a mobile banking app, processed by a decision engine powered by Experian. The system facilitates greater financial inclusion as it is enabled with machine learning based credit models, automated micro-lending credit processing, loan decision and disbursement that widen its reach of customers. The system provides the bank with a unique data and analytics-based insights in Myanmar as the country traditionally faces paucity of public data availability on customers.

“This analytics model has allowed us to make our unsecure loan product available to any Yoma Bank customers through the Bank’s mobile banking application. Leveraging on this innovative credit assessment and decision engine powered by Experian, the customers are in control of their borrowing privileges with Yoma Bank. This means that every customer that grows with Yoma Bank is rewarded by Yoma Bank,” said Mark Flaming, Chief Digital Officer.

The unsecured lending solution provides Yoma Bank with a platform to combine big data analytics, machine learning and alternate data-based credit scores to make better lending decisions

The system integrates traditional and alternate data sources including data from mobile operators, historic bank data, demographic and self-reported data and device data. Machine learning based credit scores facilitate processing of real time requests and intelligent credit decisions. The system gathers customer behavior data to enable predictive insights and incentivizes according to payment behavior. It is built on a scalable lending platform, designed to meet the needs of the geographically diverse Myanmar market.

The bank witnessed a strong growth in new customers and average transactions per customer, with a 70% repetition rate

The system facilitated the bank to achieve significant uptake as new customers, with a 70% repetition rate. Using digital channels, the customers recorded three times higher transactions. With its unique risk-based pricing model the bank is also able to incentivize good credit behavior with lower lending rates.