##rifiable. is there a logical relationship between input and output? does the model behave as we would expect where decisions are verifiable? in short, does it make sense? it will often be a matter of treading a golden middle way of the “ grey box ”, between the need for explanation on the one hand and performance on the other. what is important is understanding and critically assessing the methodology in question ; it is not a question of every single process having to be signed off by a human being. the use of artificial intelligence is monitored as part of existing banking regulation. i am therefore fairly critical of the idea of special approval requirements, as proposed by the european commission for credit assessments. where there is double regulation and there are dual supervisory processes, there is a risk of innovation being smothered and of supervisors losing the sense of what can and should be done that they draw from their daily work. 4 conclusion ladies and gentlemen, innovative technologies always offer enormous opportunities, not only for the individual bank but also for the financial system as a whole. we can and should think of innovation and stability together! supervisors remain technology - neutral in their work and operate within the framework of the existing rules. this gives you, as representatives of banks and fintech companies, planning certainty when it comes to investments in ml methods. and the faster you invest in ai, the greater your benefits will be in an intensely competitive market ; think process automation, speed, operational quality and cost efficiency. big data and ai are not only a powerful duo in terms of technology, they are also of central strategic importance for the financial sector. if brontosaurus is the past, then brontobyte is the future. ai and ml must be deployed in a way that leverages the potential offered by the vast amounts of data without ignoring the risks associated with their use. here, data competence is vital. ladies and gentlemen, the dialogue on opportunities and risks between supervisors and you – the banks, insurers and fintech firms – will certainly continue, and i look forward to being part of it. thank you very much for your attention.

machine learning in risk models – brontobytes and brontosauruses speech delivered at the bundesbank - bafin fintech festival 19. 05. 2022 | berlin | joachim wuermeling 1 introduction 2 data : quality, quality, quality 3 supervisory approaches : a question of characteristics and explainability 4 conclusion 1 introduction ms roth, ladies and gentlemen, the brontosaurus, a long - necked 30 - tonne herbivore, is one of the most well - known dinosaurs and surely familiar to many of you as well. perhaps it was a fascination with dinosaurs that led to one of the largest units of data volume being called the brontobyte. and just like the brontosaurus, the brontobyte is also awe - inspiring ; it seems gigantic and somehow unmanageable and inconceivable. the brontobyte comes after the gigabyte, terabyte, petabyte, exabyte, zettabyte and yottabyte. one brontobyte equals around 10 to the 27th power of bytes, or a 1 followed by 27 zeros. this volume of data would be equivalent to the us ( united states ) library of congress 100 trillion times over ; every person in the world would have 12, 500 copies of this library. this is big data in the truest sense of the word. however, we have not yet entered the brontobyte era. right now, we are in what is known as the zettabyte era : in 2016, annual global internet traffic exceeded one zettabyte. around 1 million such zettabytes make up a brontobyte. we ’ re still a fair way off that mark. who is supposed to process all this information? this is where artificial intelligence ( ai ) and machine learning ( ml ) come into play, because they are capable of managing these data volumes. and in the financial sector, banking supervisors – bafin ( bundesanstalt fur finanzdienstleistungsaufsicht ) and the bundesbank – have a vested interest in this. you see, digital technologies can make individual banks more stable, through better analyses and data management or the use of advanced analytics in risk management. thus, ai and ml can create added stability for the financial system as a whole. we, as supervisors, therefore wish to act as enablers of the digital transformation. we support the use of ai