Banking Today: Risk Management with AI
According to McKinsey, risk management standards are rising, making predictive analytics in banking a must-have.
With the rise of Big Data, financial risk modelling requires much expertise and thorough management. Errors in datasets can lead to near-fatal losses. Asia-Pacific bank, for example, lost $4 billion when applied an interest-rate model that contained incorrect assumptions and data errors. Needless to say, the stakes are high.
Flaws in risk management can impact not only financial institutions but borrowers as well.
As risk management issues mount, current approaches can no longer focus resources efficiently to make better data-driven decisions. The only solution is a system widen root-and-branch overhaul with AI.