Data, AI and Lending in South-East Asia
By Karl Li
With the rise of digital banking and new financial players such Grab Finance, Ovo, Go-Pay, Ant Finance, WeBank entering the South-East Asian market, traditional lenders now need to invest heavily into technology to play catch-up to their internet-era counterparts.
Data is the new oil in the 21st century, and companies such as Ant Financial and Grab has the dominant core capabilities in data refining and analytics tools to make sense of the humongous amount of data their users generate daily. Legacy banks must start to consider building their own data capabilities to make sure they stay competitive in this digital banking revolution – with Artificial Intelligence (AI) playing a crucial role in this transformation.
Conventionally, credit lending largely involves using credit scoring to make lending decisions and fundamentally, all these credit models are based on the borrower’s transaction history and payment details from financial institutions. If the borrower does not have a sufficient amount of historical credit information to be “scored”, he often gets denied and the banks also lose potential revenue.
Today, with alternative data sources and advanced AI systems, lenders can now make accurate credit decisions, by adopting a more holistic approach to credit underwriting using the data from unstructured and semi-structured sources – like social media activity, mobile phone usage, location-based activity, etc. Many credit decisioning solutions exist in today’s market to help traditional banks quickly scale out their AI capabilities, such as Silot.ai, Trust Science and Tong Dun, by applying AI and machine learning to enable assessment of even the qualitative factors such as consumer behaviour and readiness to pay.
This AI revolution paves way for greater, quicker, and cheaper decisioning of new and existing borrower’s quality and thus, helping banks to secure a greater market share in this new digital banking era.
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