A data-dri­ven approach is emer­ging across the ban­king sec­tor, affec­ting banks’ busi­ness stra­te­gies, risks, tech­no­lo­gy and ope­ra­ti­ons. Cor­re­spon­ding chan­ges in mind­set and cul­tu­re are still in pro­gress. Fol­lowing the cross-sec­to­ral report by the Joint Com­mit­tee of the Euro­pean Super­vi­so­ry Aut­ho­ri­ties (ESAs) on the use of big data by finan­cial insti­tu­ti­ons, and in the con­text of the EBA Fin­Tech Road­map, the EBA deci­ded to pur­sue a ‘deep dive’ review on the use of big data and Advan­ced Ana­ly­tics (BD&AA) in the ban­king sec­tor. The aim of this report is to share know­ledge among sta­ke­hol­ders on the cur­rent use of BD&AA by pro­vi­ding use­ful back­ground on this area, along with key obser­va­tions, and pre­sen­ting the key pil­lars and ele­ments of trust that could accom­pa­ny their use.

The report focu­ses on BD&AA tech­ni­ques and tools, such as machi­ne lear­ning (ML) (a sub­set of Arti­fi­cial Intel­li­gence (AI)), that go bey­ond tra­di­tio­nal busi­ness intel­li­gence to gain deeper insights, make pre­dic­tions or gene­ra­te recom­men­da­ti­ons using various types of data from various sources. ML is cer­tain­ly one of the most pro­mi­nent AI tech­no­lo­gies at the moment, often used in advan­ced ana­ly­tics due to its abi­li­ty to deli­ver enhan­ced pre­dic­ti­ve capabilities. …

Quel­le /​ Link: EBA report iden­ti­fies key chal­len­ges in the roll out of Big Data and Advan­ced Analytics

Wei­te­re Informationen.

EBA iden­ti­fies trust chal­len­ges from gro­wing use of Big Data and AI in finance

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