. continuous processes for the entire ai lifecycle still have to be defined and scaled for business needs. that means that ai must be embedded in the process of acquiring and organising data, modelling, analysis and delivering analytics. the skills gap, particularly with regard to data science and machine learning expertise, is the foremost challenge. at this stage, non - human intelligence is far from replacing the human brain in any respect. computers are like school pupils dividing numbers mechanically without having understood what they were doing. 3 consumers should take care : they remain the risk - takers what makes this development so significant is the fact that it is not just occurring at the level of systemic institutions, markets and stock exchanges. with robo advisers, for example, ai can directly influence and control the daily financial decisions of customers and ultimately their personal wellbeing. society has barely begun to understand the economic, ethical and social implications of ai. while client interaction is made more convenient by mobile banking, chatbots or virtual customer assistants, banks can find out more about customer habits and provide them with tailor - made financing. consumers may be rated by ai when applying for a mortgage. pooling data points from internal transactions, social networks and other sources provides a more meaningful picture of banks ’ borrowers. but denials may be hard to understand. it may become even harder to challenge a decision made by algorithms. the proper functioning of the applications is not a given. simple flaws, cyberattacks and criminal behaviour render the systems extremely vulnerable. consumers should be cautious. they need to be protected. laws may have to be modified to cover new threats. responsibility and liability in the case of malfunctioning machines have to be clarified. 4 fintechs should not ignore the legitimate concerns of society and supervisors agile tech companies are driven by an admirable energy and inspiration. by nature, they take risks. they create an idea, build a prototype and try it out immediately in the real world. regulation, supervision, obligations and requirements must make them extremely nervous. but the wellbeing of society depends on rules. the public demands cybersecurity, data privacy, consumer protection and financial stability. fintechs should not brush aside the concerns of 2 / 4 bis central bankers'speeches their stakeholders. business can only flourish if it is broadly accepted by citizens. fintechs usually pick up specific elements of the work chain of finance or create new features. using technology, they modularise and customise products as a third

party or standalone provider. fintechs are part of the finance sector but are not necessarily supervised. as long as they carry out tasks for supervised entities, these institutions are responsible for the behaviour of the fintech. 5 ai needs new forms of supervision “ artificial intelligence ” may sound glamorous from a technological perspective, but in banking supervision, the well - established principle of " same business, same risk, same rules " has so far proved to be a sound standard for innovations. whether they employ ai themselves or outsource it to fintechs, from the supervisors ’ point of view responsibility remains entirely with the bank. for german supervisors, it governance and information security nowadays are equally as important as capital and liquidity requirements. all financial institutions should address the risks posed by new technologies. banks have to implement effective control environments needed to properly support key innovations. this includes the requirement to have appropriate processes for due diligence, risk assessment and ongoing monitoring of any operations outsourced to a third party. the european mifid ii includes the requirement that firms applying algorithmic models based on ai and machine learning should have a robust development process in place. firms need to ensure that potential risks are considered at every stage of the process. regulators increasingly have to apply ai - supported analytical methods themselves to recognise vulnerability patterns, scan lengthy reports or analyse incoming data. in any case, we must strike a balance between financial stability and avoiding barriers for potential new entrants, products and business models. alongside technological progress, regulators have to constantly reassess the current legal framework, supervisory models and resources. 6 central banks should embrace ai central banks have access to huge amounts of very valuable data stemming from market operations, supervision, payments and statistics. they are well positioned to tap the benefits of ai so they can enhance their ability to fulfil their mandate for price stability and the stability of the financial system. machine learning is already being used at the bundesbank in different narrow segments. the experiences of all users have been good without exception. while monitoring the technical progress, we are currently discovering further use cases and defining our ai foundation, strategy, organisation and processes. here is a list of examples, which is by no means exhaustive : in risk management, neural networks assess and evaluate the financial soundness of the markets. market research is supported by adopting web mining techniques and machine learning in content analysis, topic modelling and clustering of relevant articles. in statistics, machine learning enables new methods