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Enabling Digital Payments Transformation

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Enabling Digital Payments Transformation Enabling Digital Payments Transformation WHITEPAPER © 2017 Aerospike, Inc. All Rights Reserved 1 Enabling Digital Payments Transformation How real-time decisions are delivering business moments The Rise of Digitalization is Changing the Payments Industry The payments industry today is in a state of flux, with several technological, economic, and demographic factors cutting across the length and breadth of the value chain. The industry is witnessing rapid growth in innovations everywhere, thus making it more fragmented. Non-banking payment service providers in the form of financial technology startups and established non-payments technology giants have caused disruption and disintermediation in discrete parts of banking and the payments landscape. $2.3t 25% 73% 15% Global Payments revenue by Millennials using Mobile for Enterprises report payment fraud Incr. US ecommerce transactions 2019 Payment Source: AFP 2016 Source: McKinsey Source: Fico Source: US Census Bureau +10% $7.6 39 87% Cashless Payment growth 2015 of every $100 are at Risk out of 1000 transactions face of Payment industry being Source: World Bank Source: Forter attack disrupted by FinTech Source: PYMNTS Source: PwC Figure 1: Digitalization in Payments Industry Global payments are expected to exceed $2.3 trillion1 by 2019, with non-cash payments accounting for an increasing share of this massive market. Cashless transactions are growing by 10%2, and according to one source, are likely to represent over one million transactions every minute by 2020. The increase is mainly driven by accelerated growth in developing markets, primarily driven by digitization and alternate channels. Although cards remain the dominant and fastest growing payment instrument, the landscape is poised for rapid change and market disruption. The adoption of mobile payments, “Card Not Present” (CNP) transactions, and the emergence of non-banking payment service providers (FinTech) are among the many factors causing turbulence and disintermediation across the banking and payments landscape. According to the United States Census Bureau, United States e-commerce transactions grew by 15% in 2016. Digital payments which can be executed anywhere, anytime, from any device are naturally appealing to both buyers and sellers. The advantages however, are accompanied by additional risk, most notably 1 Source: McKinsey 2 Source: World Bank © 2017 Aerospike, Inc. All Rights Reserved 2 fraud and theft. An estimated 73%3 of enterprises report some form of suspicious activity that puts around $7.6 of every $100 transacted at risk. Confluence of Forces Are Driving Change The payments industry has historically been relatively insulated from disruption. An extensive web of laws and regulations, combined with high capital barriers to entry, have limited the number of industry participants and fostered decades of relative stability. This is changing. A confluence of trends in technology, business, the global regulatory environment, and consumer behavioral patterns are redefining how payment transactions are executed. Agile payment providers are exploiting technology innovations in mobile wallets, bitcoin, wearables, location intelligence, biometrics, open banking APIs, big data, advanced analytics, cloud computing, and tokenization to disrupt the status quo. At the same time, the business landscape is being transformed by a growing class of FinTech payment providers such as PayPal, Stripe, and Square, as well as by new digital payment options initiated by traditional financial services companies. Telecommunications companies, mobile device manufacturers, established Internet companies, and firms in other adjacent industries are also expanding the payment ecosystem. Governments around the world are embracing digital payments and updating regulations to promote non-cash payments and ensure consumer protection. In the United Mobile States, the Mobile Payments Bio-Metrics Industry Workgroup initiative is Predictable Performance Database bringing regulatory agencies such AI & ML / Data Science Tokenization as the Consumer Financial Wearables Protection Bureau, Federal APIs PSD2 in Europe Reserve, Federal Trade NPP in Australia Commission, and other agencies AML Risk Management together to harmonize and KYC, CFT standardize regulations. At the same ISO 20022 time, European Union governments Payment are enhancing security requirements Industry and spurring competition with the Need for Operational Efficiency Real-Time Payment Apps upcoming Payment Services Extreme Competition Directive 2 (PSD2). Pressure from New Markets & Adjacencies the interchange regulation will result Ecosystem Disruptions Digital Generation X-Border Payments Multiple Channels to shop & transact in revenue losses for card issuers. Digital Wallets Now Depository and third-party Ubiquitous providers (TPPs) are competing for New norm of loyalty both transactions and customers. Given the overlapping nature of business of these players, there has been a growing number of FinTech Figure 2: Confluence of Forces start-ups and payments providers venturing into partnerships and changing the payments arena, benefiting from the new technologies and market conditions while also leveraging alternative business models that complement traditional payments practices. Consumers are propelling the digital payments industry in all facets of everyday life — ride sharing, digital music, movie tickets, vacation rentals, and online auctions represent just a few of the generators of digital payments. As digitalization starts to encourage non-traditional players, payment service providers are looking at ways to create value - and most are betting on data. 3 Source: AFP © 2017 Aerospike, Inc. All Rights Reserved 3 Data - and the history of analysis, decision and action Every activity we do in our daily life – at home, at work, or at leisure - leaves a large trail of digital exhaust with an infinite stream of information. This data, aka Big Data, says a lot about our likes, dislikes, behaviors and, more importantly, predict what we might be inclined to do next. Though the data cannot define our unconscious decisions, these are storehouses where our past decisions can be analyzed and patterns detected. In the past, there was a strong need for operational visibility and businesses required strong reporting tools as feedback on their operational decisions. They traditionally employed batch analysis on static big data which was mostly historical. The outcome was primarily an indicator of “what is happening.” Businesses then had to manually drive the decision-making process. The next step leveraged Business Insights systems that employed a combination of online transaction processing (OLTP) to facilitate and manage transaction-oriented applications and online analytical processing (OLAP) to perform analysis on the data and provide means for trend analysis, data modeling and interactive dashboards. Suffice it to say that both the transaction and analytic functions were separate and operated in silos. The notion of Intelligent Business evolved with advancements in stream processing and the ability to deal with near real-time data. Enterprises used massive data lakes to store, massage and dynamically predict outcomes. Even though advancements in big data technology allowed for faster processing and for storing more structured and unstructured data sets, most of these systems still remained as decision support systems. Also, decisions only took enterprises part of the way. Most still needed to take prescriptive actions separate from the decision process. The simple fact is that most enterprise modus operandi, even for the diagnostic and ACTION predictive analytics, has been the traditional data DECISION warehouse method where all the data is stored, manipulated and patterns learned. It is possible to INSIGHT Business Moments store all this data in a data lake and analyze it later. - Combine this with some VALUE Business Intelligence Transactional Systems - Intelligent Decisioning near real-time and diagnostic analysis, they Business Insights - Rule based Algorithms - - Micro-batch Analysis of Transactional Data could drive some level of - Static and near-RT in Real-time predictive outcomes. - Decision Support - - Data lake AI/ML - Consolidated Reports - - More silos Proactive & Predictive The obsession with the Reporting - Static, Batch oriented - - Hadoop/Spark Automated - OLAP, OLTP volume of data and with - Reactive - Static reports - Transaction & Analytics silos mining large databases of - Batch oriented - Interactive dashboards data searching for the - Historical proverbial needle in the haystack are anchored in a DESCRIPTIVE PREDICTIVE TRANSACTIONAL world where data has DIAGNOSTIC enough longevity to make historical analysis relevant. Figure 3: Different Levels of Analytics to bridge data to action When the decision need is instantaneous, this approach fails. © 2017 Aerospike, Inc. All Rights Reserved 4 Transactional Analytics Creates New Value Transactional Analytics filled the rest of the journey. It consists of analysis of real-time data, decisioning and auctioning at the same instance. Advancements in artificial intelligence (AI) and machine learning (ML) technology has resulted in the convergence of transaction and analytics. The algorithms were also codified to dynamically adapt to the data. This proactive approach drove more autonomous decision making and allowed for enterprises to act on their decisions and drive towards “Business Moments”. Figure 4 – Transactional
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