Bharath Vellore

WITH all the disruption stemming from Covid-19 over the past two years, how sound are credit risk models? This was one of the questions we sought to answer with a global research study that surveyed 100 industry decision makers across Asia Pacific. The results were more than a little unsettling – only 16 per cent of fintech and financial services organisations believe their credit risk models are accurate at least 76 per cent of the time. 

This state of great uncertainty in credit risk modelling is exposing the shortcomings of legacy approaches for credit risk decision-making that leverage limited data, workflow and automation – and often in separate systems, to boot. To really level-up decision-making, organisations need more data, more automation, more sophisticated processes, more forward-looking predictions, and greater speed-to-decisioning. To this end, they need artificial intelligence (AI), machine learning, and alternative data. The Singapore government recognises the importance of AI and has invested S$180 million into a national AI programme for the finance industry. In collaboration with the Monetary Authority of Singapore (MAS) and the National AI Office (NAIO) at the Smart Nation and Digital Government Office (SNDGO), the programme seeks to implement AI in the financial sector for the benefit of improved customer service and risk management.