TECH & AI
AI and in particular Gen AI , are the buzzwords of today when it comes to digital transformation at banks and other large financial institutions .
Of course , there ’ s no harm in displaying excitement at the possibilities Gen AI can unlock for the financial services industry , but the academic discourse is easier to discuss than the technology is to implement .
We speak to Malcolm deMayo , VP of Global Financial Services at NVIDIA , about the significant hurdles banks must overcome to successfully , effectively and safely implement AI .
The key challenges to AI adoption for banks The challenges to AI adoption at financial institutions are wide-ranging , they cause friction and slow financial firms down .
For Malcolm , it ’ s important to first take a step back . After all , AI has been baked into financial services operations for some time – predictive AI and machine learning in the form of early neural nets , like recurrent neural nets ( RNNs ), have been embedded by early AI adopters .
Unified data sets When it comes to the latest AI innovations , the first question banks should ask themselves is how good their data is .
“ Financial institutions are organised around products , which has led to the creation of data silos ,” Malcolm says .
Now , it ’ s about getting that data into a unified set – a curated dataset that can be accessed and analysed to drive actionable insights .
132 August 2024