Agefi Luxembourg Janvier 2020 PAGES GRATUITES

Janvier 2020 13 AGEFI Luxembourg Banques & Finance By Michael VALENTA and Arnd HEßELER, zeb consulting* T he emergence of Big Data—or the ability to access previously unima- ginable amounts of data, in some cases in real time—has transformed the fi- nancial services industry in several funda- mental ways. This new phase of the information revolution has allowed regu- latory bodies to digitize their supervision and monitoring efforts and companies that have embraced these new technolo- gies are using analytical tools to make data-driven strategic decisions that pro- vide long-term competitive advantage. Especially in the financial sector, missing oppor- tunities to capitalize on this development can be detrimental, due in part to how Big Data has in- creased efficiency of regulatory authorities. Big Data technology is nowpart of the standard reper- toire for control and monitoring tasks of supervi- sory authorities. Understanding the opportunities and risks associated with digitalization using Big Data technologies, especially through the lens of global regulations in the financial services indus- try, will help companies develop competitive ad- vantages for the digital future. In the following paragraphs, these trends will be outlined paying particular attention to the global regulations im- pacting the financial sector. Digitization and Big Data There is no doubt that the financial sector is in a phase of upheaval, whichwill not only be shaped by stricter regulatory requirements, but will also be decisively transformed by technological progress. Digitization is now finding its way into all areas of the banking world and will funda- mentally change traditional organizational struc- tures of banks. In the spectrum of compliance activities—in addition to the digitization or au- tomation of auditing activities—the evaluation of huge data populations (i.e. «Big Data») is playing an increasingly important role. This formof eval- uation allows regulatory bodies to track activi- ties, highlight developments and thus make regulatory risks visible. Therefore, technical solutions that evaluate popu- lations of datamust take the three “V” dimensions into account. These dimensions put the «Big» in Big Data: - V olume (amount of data) - V elocity (speedwithwhich data is generated and transferred) and - V ariety (range of data types and sources) This framework paves the way for regulatory bodies to understand and regulate implementa- tion of Big Data in the financial services industry. Big Data and regulatory supervisors It was only a matter of time before banking reg- ulatory authorities turned their attention to in- formation originating from internal, digital data–i.e. Big Data populations. Insights gained fromdifferent types of data are intended to high- light strategic market developments or risks at a glance. In the regulatory context, data popula- tions provide information about customers, products and services that financial institutions should collect and analyze for their internal use. This is especially because business activities and their associated risks must increasingly be re- ported to authorities and audit companies. Big Data as «Big Opportunity” Due to automation, digitalization and the intro- duction of artificial intelligence in various process chains, the volume of available data pop- ulations has increased enormously in recent years. Today, Big Data presents enormous chal- lenges for companies in the financial services in- dustry, but also offers lucrative opportunities. The ability to effectively capture, maintain and analyze data can lead to better business strategy decisions and thus to long-term competitive ad- vantage. Data has become so important in the global economy that participants at the World Economic Forum in Davos 2012 even declared data to be a new class of asset—on par with tra- ditional assets such as currencies or gold. The real challenge in using Big Data for business purposes, however, is having the technical know- how and the right IT architecture that will allow businesses to extract actionable insights from a «sea of data.» What’s more, this becomes particu- larly difficult when data populations within an or- ganization’s IT environment come from different formats or sources—it must be possible to evalu- ate findings from different types of reporting sys- tems, dashboards and complex decision models. Government regulators are aware that financial institutions have vast amounts of data that can be used to identify compliance risks, perform analysis and correct deficiencies. However, banks, for their part, must have techniques that make large amounts of data readable, inter- pretable and analyzable even when these chal- lenges slow down productivity. Big Data analytics tools for compliance processes Big Data analytics platforms and tools offer solu- tions for identifying risks or problems in real-time and avoiding potential violations of regulations before they are discovered by inspection agencies and regulatory authorities. In this context, Big Data analytics solutions help, among other things: - Better examine the breadth and depth of appli- cable regulatory rules, - To analyze data populations of transactions or customer segments across the board (and not, as was previously the case, only in randomsamples) and - To comply with the requirements regarding the reporting processes of the supervisory authorities (e.g. requirements regarding the standardization of different data formats within the bank). Traditional vendor solutions of data analytics plat- forms and tools are costly, especially in early phases of implementation. A bank must be pre- pared to invest financial resources and suitable ex- perts who can handle data analytics platforms from both a technical and regulatory/legal per- spective. Suchmultifunctional experts are rare and expensive. A bank must therefore evaluate the cost-benefit effect of such investments or weigh up the extent to which it wants to invest financial re- sources in technical compliance tools or, if neces- sary, in business strategy solutions. Ultimately, these investments are necessary for banks to keep up with technical developments in analyzing BigData to the same or even further ex- tent as the banks’ regulators. Specifically, there are two classic compliance topics where the use of Big Data analytics tools has become indispensable. In these two areas, regulations have been incorpo- rated into national and international statues in re- cent years, which has brought them to the forefront of the conversation about using BigData in regulatory practices: - Money laundering—regulations to combat cor- ruption, bribery, robbery, extortion, drug traffick- ing, arms dealing or tax evasion, and - Customer Protection—regulations to protect in- vestors or consumers in the financial market Anti-Money Laundering Money laundering is the process of introducing illegally earned money or illegally acquired as- sets into the legal financial and economic cycle. Since the money to be «laundered» comes from illegal activities such as corruption, bribery, rob- bery, extortion, drug trafficking, arms trafficking or tax evasion, its origin must be concealed to avoid exposure. To combat this illegal activity, both national and international regulations have been established by supervisory authorities worldwide over the past 30 years. In accordance with these regulations, financial institutions must observe various risks and rules when carrying out their financial transactions. In order to sys- tematically monitor the huge data volumes of transactions executed worldwide for indications of money laundering, it is now standard practice for financial institutions to use software from the Big Data analytics spectrum that specialize in this sort of monitoring. Factors such as the size of the transaction volumes, the number of distribution channels or the smug- gling of assets across various geographical areas between thousands of market participants play an important role in this. Thus, anti-money launder- ing (AML) Big Data programs are designed to meet the following technical challenges: - Customer Due Diligence (CDD) andKnowYour Customer (KYC): Anti-money laundering pro- grams should use external information sources (e.g., internationallymaintainedAMLwatch lists, but also other information platforms such as Lex- isNexis, Thomson Reuters, D&B etc.) to check cus- tomer data for clues to identify risky (e.g. sanctioned) customer entities. - Graphical analysis capability: AML programs must have these capabilities so that, for example, illegal holding structures can be uncovered by modeling complex transactions between thou- sands of market participants - Transaction Monitoring System (TMS): Transac- tions carried out by the bank are continuously monitored by rules-based TMS. Such rules may include areas likemonetary thresholds, suspicious transaction patterns or corresponding deviations from regular transaction patterns. - Individual customer profile monitoring: AML programs must monitor the behavior of individ- ual customer profiles using specialized models. The aim is to identify individual behavioral pat- terns and deviation from those patterns that could indicate potential illegal activity. Customer Protection (Investment Suitability) In the aftermath of the 2008-2009 financial crisis, regulators around the world decided that rules and regulations needed to be tightened to protect clients with limited expertise in financial products and markets from greedy advisors and dealers. New regulations were introduced to further in- crease transparency around banking products and activities of experts like advisors and brokers. In the European single market, the EU introduced the Markets in Financial Instruments Directive (MiFID) 2004/39/EC in order to better protect banking clients. MiFID II in particular requires financial institu- tions to increase transparencywith regard to prod- uct composition and cost breakdown for the benefit of consumers. Naturally, this regulation has caused a significant volume increase in the corresponding data populations. Financial market participants must now process and analyze huge amounts of data to complywithMiFID, which re- quires, among other things, that transaction-re- lated data be recorded and stored throughout the entire life cycle of a trade. This is one example of howdifferent data formats from a variety of different sources can create complexity and the need for well-paid expertise. In addition to this, vaguely formulated legal rules and regulations have to be interpreted and com- plied with in a way that protects the financial in- stitution. Companies must invest in Big Data analytics programs and the expertise to support them in order to comprehensively monitor the data streams traded in this way and detect po- tential rule violations in real-time. Capturing all the information throughout the trading lifecycle not only helps with regulatory compliance, but can also provide important insights to increase transparency and find ways to be more effective and efficient in business strategy—further in- creasing the need to focus on Big Data. Outlook Even leading global companies today lack expert- ise on how to gain knowledge from data, manage risk and supply the market according to demand. While banks that invested in relevant BigData an- alytics systems at an early stage are nowusing the mass of data available for business strategy deci- sions, many institutions are still hesitant to use knowledge gained from data for compliance is- sues. Banks that are lagging behind this develop- ment may find this expensive in the medium to long term, but the expectations of regulators and other auditing bodies are increasingly based on findings fromdata populations. For a financial in- stitution to survive in today’s digitalizedworld, in- vestment in Big Data programs and the expertise needed to support those programs is paramount. * zeb consulting is a leading strategy and management consul- tancy for financial services in Europe. For more information, visit our website www.zeb.lu Big Data and Regulatory Compliance: Effects of new digital technology on regulatory compliance processes STRATEGY AND MANAGEMENT CONSULTANCY FOR FINANCIAL SERVICES IN EUROPE STRATEGY & BUSINESS MODEL y IT y FINANCE & RISK y RESTRUCTURING, MERGER & OPERATING MODEL y HR MANAGEMENT zeb was founded in 1992 and is one of the leading strategy and manage- ment consultancies for financial services in Europe. 1,000 employees work for the zeb group in 17 locations. In Germany, zeb operates offices in Frankfurt, Berlin, Hamburg, Munich and Münster (HQ). Its international locations are in Amsterdam, Copenhagen, Kiev, London, Luxembourg, Milan, Moscow, Oslo, Stockholm, Vienna, Warsaw and Zurich. Its clients include major European banks and private banks, regional banks as well as insurers. Several times already, zeb was classed and acknowledged as “best consultancy” for the financial sector in industry rankings. 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