A director at a board I was briefing asked me a simple question: what do I actually need to know about AI? I did not answer it well at the time.
And so this is the answer, written down. A short, board-facing book that reads the AI problem through the financial sector's risk discipline - the one built over decades and paid for in real losses. It is built on the MAS AI Risk Management Guidelines (AIRG), which I wrote, and it takes the generalist SID AI Guide for Boards in Singapore as its starting point. Comparison and extension, not a critique - it just follows the finance lens further than a general guide can.
Five questions, one for each thing a board owns. Approve the approach. Set the appetite. Get the roles clear. Understand enough to challenge. Review the approach. Each one turns the obvious question into another one a financial-sector board should be asking - who really owns the risk, what "good enough" means for the task, when a control has quietly been skipped - and shows where the answer for finance is not the generalist answer.
You do not need to send your directors on a prompt-engineering course. You need a handful of durable questions that do not expire when the next model ships. That is what this is - about thirty plus pages, five questions, and the detail behind each one, to take into the next board meeting.
Written for directors, especially in financial institutions, but useful to any board that takes AI risk seriously.
A director at a board I was briefing asked me a simple question: what do I actually need to know about AI? I did not answer it well at the time.
And so this is the answer, written down. A short, board-facing book that reads the AI problem through the financial sector's risk discipline - the one built over decades and paid for in real losses. It is built on the MAS AI Risk Management Guidelines (AIRG), which I wrote, and it takes the generalist SID AI Guide for Boards in Singapore as its starting point. Comparison and extension, not a critique - it just follows the finance lens further than a general guide can.
Five questions, one for each thing a board owns. Approve the approach. Set the appetite. Get the roles clear. Understand enough to challenge. Review the approach. Each one turns the obvious question into another one a financial-sector board should be asking - who really owns the risk, what "good enough" means for the task, when a control has quietly been skipped - and shows where the answer for finance is not the generalist answer.
You do not need to send your directors on a prompt-engineering course. You need a handful of durable questions that do not expire when the next model ships. That is what this is - about thirty plus pages, five questions, and the detail behind each one, to take into the next board meeting.
Written for directors, especially in financial institutions, but useful to any board that takes AI risk seriously.
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