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Decision science: from automation to optimisation

Decision science: from automation to optimisation

Despite its benefits, credit decisioning is severely lagging – average ‘time to decision’ for small business and corporate lending at traditional banks is between three and five weeks, while average ‘time to cash’ is nearly three months.

This isn’t about internal digitisation, which enhances how you work or connect with your employees. This is about your customers, anywhere in the world
Ana Marques, Novo Banco

Are such long lead times efficient and responsive in today’s fast-changing interconnected world? The answer is a resounding no.

Many banks have still not effectively tapped into the power of automated decision-making. Barriers to innovation, including budgets, legacy IT systems, availability of the right data, organisational silos and an inability to act fast when opportunities arise, are a few reasons banks are held back, even when they have a clear vision and end-goal.

This Risk.net whitepaper, commissioned by SAS, explores decision science and automation, and the efficiencies it brings, and offers insight into why automation – married with adaptable analytics – is now crucial.

 

Download the Risk.net/SAS white paper from Risk Library

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