20190604 Artificial Intelligence In Banking SA

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EU MonitorGlobal financial marketsArtificial intelligence in bankingJune 4, 2019A lever for profitability with limited implementation to dateAuthorOrçun Kaya 49 69 910-31732orcun.kaya@db.comArtificial intelligence (AI) is a significant step forward in the digitalisation andtransformation of modern businesses. In short, it refers to computers’ capabilityto acquire and apply knowledge without programmers’ intervention.EditorJan SchildbachInvestors are lining up to be part of the imminent change. AI attracted USD 24bn in investments globally in 2018, a twelvefold increase since 2013. US startups received the most attention, followed by Chinese, which already outpacedEuropean AI start-ups.Deutsche Bank AGDeutsche Bank ResearchFrankfurt am MainGermanyE-mail: marketing.dbr@db.comFax: 49 69 910-31877www.dbresearch.comDB Research ManagementStefan SchneiderWithin Europe, Germany, France and the UK are the frontrunners inexperimentation and in the implementation of AI. In light of intensified globalcompetition, the European Commission proposed a EUR 9 bn budget to fundAI-related projects between 2021 and 2027.Similar to earlier examples of information technology (IT) implementation infinancial services, AI promises great efficiency gains and potential revenueincreases. To date though, AI implementation in banking has been modest. AI isbeing tested for real-time identification and prevention of fraud in online bankingas well as in know-your-customer (KYC) processes. Robo-advisors are alsoevolving over time to become true AI solutions. Looking forward, regulatorymeasures around data privacy and concerns regarding cybersecurity mightcreate obstacles to AI use in banking. In addition, the highly regulated nature ofbanking may cancel out some efficiency gains of AI.AI’s potential contribution to bank profitability should not be underestimated.Empirically, AI has a significant positive impact on European banks’ return onassets (ROA). By increasing labour productivity, AI technologies couldstructurally reduce costs in the banking sector. Rapid implementation of AItechnologies is, therefore, central to fighting persistently weak profitability and toremaining competitive.

Artificial intelligence in bankingIntroductionHuge progress in computer hardware, software and internet technologies haveirreversibly changed our societies. It is now difficult to imagine an economicagent without computers, internet or mobile devices. The pace at which IT isevolving offers great opportunities to expand the client base, introduce newproducts or improve existing ones and to increase efficiency in a relatively shortperiod of time. On the other hand, if companies miss out on the current IT wave,they might be overtaken by events soon.Among the various IT breakthroughs of recent years, the advancement in AI isparticularly remarkable. In short, AI refers to computers having cognitive skillssimilar to humans, which could result in immense efficiency gains for firms andtheir clients alike. The financial sector has been one of the early experimenterswith AI technologies, not least due to its likely contribution to strongerprofitability. It is therefore essential to take a closer look at the potential role ofAI in banks’ digital transformation.Artificial intelligence: A giant step beyond standard IT applicationsMachine learning1There are various approaches to programmingcomputers so that they mimic human decisionmaking. Decision trees, ranking or prioritisingare among the more established solutions. Arelatively new approach is machine learning(ML). ML is a subset of AI and refers tocomputer programs that recognise patternsand make predictions based on them. Typicalexamples are internet platforms thatrecommend particular products or news storiesto users who might like them based onprevious preferences. By continuouslyanalysing new data and scenarios, ML toolsmake adjustments to decision-makingprocesses without being specificallyprogrammed to do so. They are therefore ableto learn from data. Subcategories of ML aredeep learning, as well as supervised,unsupervised and reinforcement ML.ML tools process vast quantities of datathrough neural networks. In short, these areprocesses which classify data on successivelayers. In doing so, they rely on theprobabilities of possible outcomes. They makedecisions based on the most likely outcome,even though it might turn out not be the perfectchoice in the end. However, neural networksinvolve a feedback loop. Depending on theaccuracy of the outcome from previous trials,they update their approach to perform betterthe next time.Source: Deutsche Bank ResearchTo date, IT solutions in the business world have by and large focused onautomating repetitive tasks that would otherwise require human involvement.The boundaries for these IT applications have been set by their developers, andby design these solutions have been limited in their capabilities. They havelargely been static and unable to comprehend or act on their own. Withtechnology evolving rapidly, however, this is increasingly changing.Artificial intelligence refers to the ability of computer programs to acquire andapply knowledge without human intervention and involvement. By observing theworld around them and analysing information autonomously, AI systems drawconclusions and take appropriate actions. They learn from their previousjudgements and, depending on the level of accuracy, improve their performanceover time.AI as a term was first coined at the Dartmouth Conference in 19561 and is notnew per se. In recent years, though, some breakthroughs in IT have allowedtremendous momentum in AI’s capabilities:i) The expansion of internet usage has led to huge amounts of digitalinformation being generated and stored. In about 10 years, the amount ofdata generated worldwide grew some 17 times. Forecasts point to anotherfivefold increase between now and 2025. This large volume of information,once cleaned and structured (i.e. big data), is at the core of data-drivendecision-making.ii) There has been a colossal increase in the processing power of computers. Astandard measure of that, the number of transistors, has increased 10 mtimes since the 1970s. The speed of central processing units, anotherelement contributing to processing power, rose by a factor of 6,750 over thesame period.2 This enables algorithms to process information at much fasterrates and contributes to the accuracy of their decision-making.iii) Other developments – such as the reduction in data storage costs,advancements in data mining processes or an increasing number of ITexperts – have further fuelled the feasibility and capability of AI. While the122 June 4, 2019See McCarthy, Minsky, Rochester, Shannon (1955).Moore’s Law states that the processor speed of computers, or the number of transistors on anaffordable central processing unit, doubles approximately every two years.EU Monitor

Artificial intelligence in bankinghard drive cost per gigabyte has come down from around USD 5,000 in 1990to some USD 0.025 today, the number of IT specialists grew by 50% in theeuro area between 2007 and 2017, for example.Big data as input, data identification methods such as machine learning and thegreater affordability of these tools have been the driving factors behind AI’srecent rapid success in understanding languages, recognising objects andsounds, and observing and solving problems autonomously.Artificial intelligence investments on the riseStrong growth in VC investments in AIstart-ups globally2USD bn2520151050131415161718**estimatedSources: OECD, Deutsche Bank ResearchPatents in AI: China outpacing the EU3% of total global patents in AI*4030201001015US1015EU-281015JP1015CN*AI-related patents are patents in cognition, meaning andunderstanding, large-capacity and high-speed data storage,high-speed computing, large-capacity information analysis.Thanks to its rapid evolvement in recent years, AI is being experimented withand implemented in several areas. Due to measurement issues, however,quantifying its deployment is hardly a straightforward task. Indeed, firms mightdeploy AI to increase efficiency in their processes, which is not directlyobservable for analysis. Moreover, it is sometimes difficult to differentiatebetween more standard IT solutions and sole AI applications. To partlyovercome these drawbacks, information on venture capital (VC) investments inAI start-up firms may be useful.3 In 2018, AI start-ups received a staggeringUSD 24 bn globally, up from less than USD 2 bn in 2013. Growth in VCinvestments over the past two to three years has been particularly strong. AIfirms have also increasingly become acquisition targets. Over the last 20 years,a total of 434 companies in the AI sector have been acquired, 220 of them since2016 alone.4Of the total VC volume in 2018, almost USD 15 bn went to AI start-ups in theUS, and another USD 6.5 bn went to Chinese firms. In 2017 and 2018, thenumber of VC deals flattened out. Yet the average volume of VC investmentssurged, an indication of VC flowing into more mature AI firms whose capitalneeds are larger than those of typical seed stage start-ups. In China, forexample, SenseTime Group, a computer vision and deep learning technologydeveloper, raised USD 1.6 bn in VC funding in 2018. With the new capital, thevalue of the company rose to over USD 6 bn, making it the world’s mostvaluable AI unicorn. In the US meanwhile, it is primarily large tech firms whichinvest in AI start-ups.To VC investors, AI appears to be a truly transformative technology withsignificant potential, like the internet and mobile revolutions in past decades.How do AI start-ups use the funds they receive? First observations indicate thatthey hire new AI talent (which proves to be costly and difficult to find) andexpand their services. Investors might therefore need to wait a while before theysee meaningful returns on their investments.Artificial intelligence and intellectual property rightsSources: OECD, Deutsche Bank ResearchA technological field usually is more useful and has a greater value for theeconomy in the years to come if there is a substantial increase in the number ofpatents filed in this particular field.5 There were some 20,000 patent applicationsin AI-related technologies in 2016, double the figure of 2010. Around 50% ofthat was accounted for by AI patents in computer vision. This technology ismostly used in self-driving cars and shows how intense competition in this areacurrently is. Of the total AI patent applications in 2015 (the latest available data3453 June 4, 2019The OECD identifies AI start-ups as firms whose business model focuses on i) “artificialintelligence”, “machine learning” and “machine intelligence”; ii) “neural networks”, “deep learning”,and “reinforcement learning”; and iii) “computer vision”, “predictive analytics”, “natural languageprocessing”, “autonomous vehicles”, “intelligent systems” and “virtual assistant”.See WIPO (2019).See Inaba and Squicciarini (2017) for a detailed explanation.EU Monitor

Artificial intelligence in bankingAI activity concentrated in a small set ofEU countries4% of total AI patent applications in the EU between2010 and 201521Within the EU, half of all AI patent applications originated in Germany andFrance. Together with the UK (16%) and Sweden (8%), four countries accountfor the lion’s share. Given that patents create a legal monopoly, they introduceimportant first-mover advantages. Considering potentially large economies ofscale, countries unable to implement AI now might be at risk of remainingbehind for a long period of time.31781716DEFRUKSEwith respect to countries), the US accounted for about one-third, a more or lessstable share since 2010. Within the US, it was the tech giants who filed thelargest number of AI patents. China made up 25% of the applications in 2015,up from 10% in 2010. Japan and the EU-28 each had a share of 14%, bothdown from around 20%. China increasingly seems to be replacing the EU andJapan in AI research and development with potentially significant implications inthe future.NLOther EUSources: OECD, Deutsche Bank ResearchATM to bank branch ratio in Europe*54321In light of intensified global competition in IT in general and AI in particular, theEuropean Commission proposed a budget to fund research and innovationprojects in Europe in March 2019. Horizon Europe is the successor of Horizon2020, which has a volume of EUR 77 bn to be spent between 2014 and 2020.Horizon Europe aims to allocate EUR 100 bn between 2021 and 2027. One ofthe main sub-categories of Horizon Europe is the Digital Europe Programme,which aims to invest EUR 9 bn specifically in high-performance computing anddata, AI, cybersecurity and advanced digital skills projects. Even though HorizonEurope represents an important step forward in enhancing AI technology inEurope, its ability to drive successful AI projects remains to be seen. Indeed, itspredecessor received 115,000 innovation and research proposals between2014 and 2016, yet only 14,000 proposals were selected for funding, a very lowsuccess rate. The high rate of over-subscription is evidence of strong demandfor funding. But the large number of rejected applications points to someunderlying problems. Alternative solutions, such as enhancing IT literacy at earlyages or improving IT infrastructure, might be necessary to increase the numberof high-quality AI and innovation projects.0879195990305091317*data includes Finland, Norway, Denmark, Sweden, Belgium,Spain, the Netherlands, Switzerland, Italy, the UK, France,and Germany till 1999 and all EU countries later on.Sources: World Bank, Humphrey et al (2003), DeutscheBank ResearchRInternet banking shows persistent growth6Users in % of adult population8060402000810EUSource: Eurostat12DE14FR1618UKEarlier examples of IT implementation in bankingBanks are usually early adopters of IT opportunities. This is true not just for theback office, where modern technologies have been used for a long time (e.g. toprocess payments), but also for the front-end. An example are automated tellermachines (ATMs), one of the earliest IT applications in banking. These devicesreplaced the repetitive tasks of bank employees in cash withdrawal and accountbalance checks. They made it easier for clients to access standard bankingservices while also making banks more efficient. Since the first ATM wasinstalled in London in 1967, they have become standard devices in branches. InEurope, their numbers have grown to three ATMs per bank branch in 2017, upfrom one ATM per four bank branches in 1987. With bank employees relieved ofroutine cash-handling tasks, they were able to take on other services such asrelationship banking (i.e. catering to clients’ individual needs) and offering otherbank services like credit cards, loans and investment products.6Online banking is another example of banks adopting client-facing new IT.Starting from the late 1990s, the use of internet as a medium for bankingservices has increased immensely. Direct or internet banks with very few or nophysical branches emerged. Virtually all banks started providing online bankingservices. In 2018, more than half of the adult population in the EU used internetbanking to check their account balances or transfer funds. In some countries,such as Denmark, internet banking penetration rates are particularly high (90%).In Germany, 59% of individuals used internet banking in 2018, up from only64 June 4, 2019See Autor (2015).EU Monitor

Artificial intelligence in banking35% in 2007. For clients who have little time to visit a branch, online bankinghas become the main tool for standard services.Mobile banking in Germany7% of respondents6040200UserNon-userBanking appUserPotential Non-useruserSources: Postbank Digital Study (2018), Deutsche BankResearchCustomerfocused-Credit scoring-Insurance policies-Client-facing chatbots-Know your customerOperationsfocused-Capital optimisation-Model risk management-Stress testing-Fraud detectionTrading andportfoliomanagement-Trade execution-Portfolio managementRegulatorycomplianceArtificial intelligence in bankingFor banks, data is essential to almost all business lines, from traditional deposittaking and lending to investment banking and asset management. Autonomousdata management without human involvement therefore offers greatopportunities for banks to improve speed, accuracy and efficiency. Potential AIapplications in banking can be classified into four broad categories: 1)customer-focused front office applications, 2) operations-focused back officeapplications, 3) trading and portfolio management, 4) regulatory compliance.7 Atleast for now, banks by and large are still experimenting with AI technologiesrather than fully implementing them in their processes. Customer- andoperations-focused AI solutions seem to be undergoing more intensiveexploration than others:Mobile paymentsAI implementation in bankingThe way that bank clients access the internet has changed as well. Germans,for example, are increasingly using their mobile devices for internet banking,and some 40% of them have a banking app on their mobile phones. Moreover,one-fifth of them also use their apps for mobile payment services. This isparticularly popular among younger, more educated and internet-savvyindividuals. With banks and their clients meeting on virtual platforms and evermore people using online services, banking is becoming less and less branchdependent.8i) AI is being tested for real-time identification and prevention of fraud in onlinebanking. Indeed, credit card fraud has become one of the most prevalentforms of cybercrime in recent years, which is exacerbated by the stronggrowth in online and mobile payments.8 To identify fraudulent activity, AIalgorithms check the plausibility of clients’ credit card transactions in realtime and compare new transactions with previous amounts and locations. AIblocks transactions if it sees risks.ii) AI is also being tested in KYC processes to verify the identity of clients. AIalgorithms scan client documents and evaluate the reliability of theinformation provided by comparing it with information from the internet. If AIalgorithms identify inconsistencies, they raise a red flag and a more detailedKYC check by bank employees is performed.iii) Another area where banks are experimenting with AI technologies ischatbots. Chatbots are digital assistants that interact with clients by text orvoice and aim to address their requests without the involvement of a bankemployee.iv) Banks are also exploring AI to visualise information from legal documents orannual reports, for example, and to extract important clauses. AI tools create-Regulatory technologymodels autonomously after observing the data and back testing to learn from-Macroprudential surveillancetheir previous mistakes to improve accuracy.-Data quality assurance-Supervisory technologySources: FSB (2017), Deut sche Bank Researchv) Some existing financial technology tools evolve as true AI solutions overtime, too. Good examples include robo-advisors that enable full automationin certain asset management services and online financial planning tools thathelp customers make more informed consumption and saving decisions. Asthese financial technology solutions mature, they increasingly use techniquesthat search data and find patterns in them autonomously.785 June 4, 2019For a detailed overview on how AI is being implemented in the individual categories of table 8,please see FSB (2017).See Mai (2018).EU Monitor

Artificial intelligence in bankingIn their quest to become more efficient, banks mostly seem to be exploring AIapplications to replace activities which are costly, laborious and repetitive. Thefocus is on operational risk management gains like fraud detection or improvedKYC and on opportunities for cost reduction like chatbots or robo-advisors.A real-life application of AI: Deutsche Bank’s Alpha-Dig platform9Using AI

Artificial intelligence (AI) is a significant step forward in the digitalisation and transformation of modern businesses. In short, it refers to computers’ capability to acquire and apply knowledge without programmers’ intervention. Investors are lining up to be part of the imminent change. AI attracted USD 24 bn in investments globally in 2018, a twelvefold increase since 2013. US start .

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