Enabling Digital Payments Transformation · Enabling Digital Payments Transformation How real-time decisions are delivering business moments The Rise of Digitalization is Changing

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Enabling Digital Payments

Transformation

WHITEPAPER


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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.

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

1Source:McKinsey2Source:WorldBank

$2.3tGlobal Payments revenue by 2019Source: McKinsey

25%Millennials using Mobile for PaymentSource: Fico

+10%Cashless Payment growth 2015Source: World Bank

73%Enterprises report payment fraudSource: AFP

39out of 1000 transactions face attackSource: PYMNTS

$7.6of every $100 are at RiskSource: Forter

87%of Payment industry being disrupted by FinTech Source: PwC

15%Incr. US ecommerce transactions 2016 Source: US Census Bureau


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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 States, the Mobile Payments Industry Workgroup initiative is bringing regulatory agencies such as the Consumer Financial Protection Bureau, Federal Reserve, Federal Trade Commission, and other agencies together to harmonize and standardize regulations. At the same time, European Union governments are enhancing security requirements and spurring competition with the upcoming Payment Services Directive 2 (PSD2). Pressure from the interchange regulation will result in revenue losses for card issuers. Now Depository and third-party providers (TPPs) are competing for both transactions and customers. Given the overlapping nature of business of these players, there has been a growing number of FinTech 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.

3Source:AFP

Digital Generation Multiple Channels to shop & transact Digital Wallets Ubiquitous New norm of loyalty

Payment Industry

MobileBio-Metrics

Predictable Performance DatabaseAI & ML / Data Science

TokenizationWearables

APIs

Need for Operational EfficiencyReal-Time Payment AppsExtreme CompetitionNew Markets & AdjacenciesEcosystem DisruptionsX-Border Payments

PSD2 in EuropeNPP in AustraliaAML Risk ManagementKYC, CFTISO 20022

Figure 2: Confluence of Forces


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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 predictive analytics, has been the traditional data warehouse method where all the data is stored, manipulated and patterns learned. It is possible to store all this data in a data lake and analyze it later. Combine this with some near real-time and diagnostic analysis, they could drive some level of predictive outcomes. The obsession with the volume of data and with mining large databases of data searching for the proverbial needle in the haystack are anchored in a world where data has enough longevity to make historical analysis relevant. When the decision need is instantaneous, this approach fails.

INSIGHT

DECISION

VALUE

ACTION

Reporting- Staticreports- Batchoriented- Historical

BusinessInsights

- Decision Support- Consolidated Reports- Static,Batch oriented- OLAP,OLTP- Transaction &Analyticssilos- Interactivedashboards

BusinessIntelligence- Rulebased- Micro-batch- Staticandnear-RT- Datalake- Moresilos- Hadoop/Spark- Reactive

BusinessMoments

- Transactional Systems- Intelligent Decisioning

Algorithms- AnalysisofTransactional Data

inReal-time- AI/ML- Proactive&Predictive- Automated

TRANSACTIONALPREDICTIVEDIAGNOSTICDESCRIPTIVE

Figure 3: Different Levels of Analytics to bridge data to action


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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”.

Recent advancements in analytics, AI and ML represent a generational opportunity to move digital payment transactions beyond being simply convenient and cost-effective. By integrating analytics into the payment process in real time, each transaction has the potential to produce better business outcomes. Personalized products and services, more engaging customer experiences, threat detection, and fraud prevention are among the transactional outcomes that can ultimately grow revenues, increase customer loyalty, reduce risk, and respond faster to market changes. Systems of Engagement and Systems of Record To fulfill the promise of leveraging transactional analytics to achieve more profitable and rewarding business outcomes, an entirely new payment architecture is required — one that combines the virtues of transactional systems of record and analytics systems. Noted business author and strategist, Geoffrey Moore, coined the term "system of engagement" to describe a new generation of dynamic, personalized and interactive applications. In contrast to traditional systems of record, systems of engagement are ubiquitous, contextual, mobile, location-aware, and embedded in the way consumers and businesses interact.

Figure 4 – Transactional Analytics for Business Moments


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These new applications focus on people, not processes, and harness a perfect storm of mobile, social, cloud, and big data innovation. The user experience is delivered in the context of the daily lives and real-time workflows of customers, partners, and employees.

Limitations of Conventional Payment Data Architectures

The modern payments value chain is long and complex, with data flowing back and forth continuously. This data is living and ever-changing but locked in silos. Conventional payment data architecture relied on such batch and near real-time process analytics. The emphasis was on historical analysis coupled with learnings from a few siloed data real-time databases. As payment players look at the data they realized that most of the data does not come “rich” from the

Enterprise DataStores

EDW

RDBMSOperational Database

AnalyticsProcessingTransaction Processing

DataIntegration

InsighttoAction Delay

TransactionalSystems AnalyticalSystems

Figure 6: Conventional Payment data architectures

Figure 5: Payment Systems of Engagement


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get go. Though they see inconceivable amounts of data across all of their ecosystems, they realize that the length of time during which this data has value is rapidly shrinking. Prevailing data architectures:

• store transactional and analytical data in silos. • fail to bring transactional data together with historical data for analysis, decisioning and

action in real time. • are batch oriented

They lead to:

• high response times • outdated information, or incomplete information

They had an effect on their business outcomes:

• customer abandonment • unjustified payment denial • undetected fraud • missed cross-sell • bad customer experience.

This created a "gap" between the operational systems designed for efficient transaction processing and the analytical environment designed for efficient reporting and analytics. The gap introduced delays in, and limited immediate relevance of, decision-making processes because they could only be based on "after the fact" analysis. Many of today’s online applications have outgrown the traditional and basic ACID (atomic, consistent, isolated, durable) transaction of the relational era and have broadened it so that it can (1) be used across a widely-distributed system and; (2) be more of an ‘interaction’ where the transaction may include analysis that is real/near time and possibly even historical. Once completed, the transaction is then used to trigger other events and make decisions that affect literally the next transaction the user makes or internal activities such as business intelligence decision-making processes. The ability to deliver actionable intelligence within milliseconds, at the point of transaction, is severely constrained by conventional data architectures.

Intelligent Payment Systems of Engagement – From Many Micro-Decisions to One Modern systems of engagement (SoEs) are incorporating a new generation of application architecture that eliminates the wall between transaction processing and analytics.

Payment systems need to be managed in a continuum that allows for integration and customization of products and services based on the needs of individual customers. For electronic payments strategies to be successful, the analytics processing must be tightly integrated with transaction processing systems. This integration enables real-time interdiction, and drives actions that are called automatically, based on policy. Automated systems can provide a more comprehensive view of customer behavior by leveraging analytic calculations and algorithms to detect and flag suspicious payments activity and provide a cluster of “micro-decisions”. Furthermore, these capabilities deliver very low false positives.


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Figure 7: Transactional Analytics Gartner refers to this as "Hybrid Transaction/Analytical Processing" (HTAP). An HTAP architecture is best enabled by in-memory computing technology to enable analytical processing on the same (in memory) data store that is used to perform transaction processing. By removing the latency associated with moving data from operational databases to data warehouses and data marts for analytical processing, this architecture enables real-time analytics and situation awareness on live transaction data.

Figure 8: HTAP for Intelligent Payment systems

Enterprise DataStores

EDW

RDBMS

DecisionModel

AnalyticsProcessing

HighPerformanceTransactional Database

HybridMemoryNon-Relational

TransactionalAnalytics AftertheFactAnalytics

Analysis/Monitoring

Transactional DataforDecision AlgorithmsandRTMonitoring

Transactional ProcessingwithReal-timeIntelligent Decisioning


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Aerospike – Sustained Reliability at Scale Not all hybrid architectures are the same in terms of reliability, availability, speed, and data integrity. As workloads increase, the ability to predictably process high-volume, concurrent transactions while performing complex analytics against massive data sets in milliseconds becomes increasingly difficult. Prevailing relational and NoSQL databases with caching, as well as RAM-based in-memory databases, often fail, particularly when unanticipated peak loads occur. Without warning, response times can plummet, data can get lost, errors can occur, and systems can become unavailable. Aerospike is focused on a singular purpose. It is the industry’s first hybrid memory database incorporating revolutionary advances in database design. Its mission is to deliver a database for transactional analytics that powers SoEs with predictable performance — at any scale, with the lowest total cost of ownership. Aerospike is the only database that can reliably handle the demands of transactional analytics processing: Internet-scale data volumes, decisions at millisecond speeds, and operational efficiency. Aerospike’s Hybrid Memory Architecture combines solid-state drives (SSD) and DRAM to achieve the sustained performance that SoEs require – with a significantly smaller footprint. Aerospike’s Smart Client™ technology handles complex database management processes automatically so developers and operations staff can focus on the outcomes of the business, not administration.

Figure 9: Aerospike predictable performance at lowest TCO In summary,

• It provides payment teams with high-volume, automated request processing leveraging intelligent data assessment across multiple systems.

• It provides the scalability to process high volumes of requests simultaneously, perform

investigations, shape customer action and contact strategies, and reach resolution in milliseconds.

• It uniquely provides a seamless connection between risk analysis and the individual customer,

to decide the necessity, timing, channel and nature of action required to determine risk, reduce losses and foster customer loyalty.

HYBRID MEMORY ARCHITECTURE• No cache required – simpler architecture! Smaller Server Footprint• Patented Flash Optimization – Log structured File System• Record Oriented, Schema Free NoSQL KV Store

DYNAMIC CLUSTER MANAGEMENT• Highest Uptime & Availability (5 nines plus), Scalable• Automatic DB Cluster formation, self healing and dynamic sharding• Cross Data Center Replication (XDR)

INTELLIGENT CLIENTS – SMART CLIENTTM

• DB aware Clients, No load balancers required• Supports One-hop application to data • Broad language support (C/C++, Java,C#, Python, Go, Node.js,

PHP)• Rich API’s - Accelerated development

LOWER TCO • Up to 10x reduction in servers deployed• Demonstrated 10:1 price performance savings• Huge operational efficiency – “Set it and Forget it”$

REAL-TIME ENGINE• Multi threaded, massively parallel• DRAM or Hybrid DRAM/Flash for Persistence• Stable, Low Latency and high throughput under any condition• Deployable on Bare Metal, virtualized, containerized, or Cloud

RELIABILITY• Predictable performance Stable, Low Latency

and high throughput under any condition• Highest Uptime & Availability (5 nines plus),

Scalable

FASTER TTM • SmartClientTM provide DBA awareness, reduces

development time• Reduce your front-end on-boarding from months

to days


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Figure 10: Aerospike Hybrid Memory Database

Figure 11: Aerospike for Payments SoE

How Payment Providers Are Applying Transactional Analytics Payment providers as well as FinTech companies recognize that transactional analytics is no longer optional. They need to drive more efficiency in their operations and maintain the highest level of convenience and ease.

High velocity of transactionsTargeted engagement requires large numbers of transactions per second

Handle huge volumes of dataMust hold 100s of TBs per use case.

Non Volatile Memory (NVM)Allows you to store large volumes of data

with extremely high performance & consistency. Caching strategies routinely

fail with heavy write use cases.

Lower latencyResponses to queries must consistently

be less than 5 milliseconds.

ReliabilityHigh availability – must be up 24 x 7. No single point of failure.

Easy to scale & manageDatabase must not require high amounts of manual intervention.

Low cost of ownershipNeed to reduce server “footprint” while supporting many new use cases.

Open & interoperableNeed to leverage existing infrastructure & solutions.


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To survive and grow, payment service providers must continuously invent new use cases, leveraging analytics at the point of engagement to promote customer loyalty and profitability, while reducing costs and risk.

Figure 12: Transactional Analytics in Payments These use cases hinge on three aspects:

1. The need for speed - faster processing where decisions need to be made in the moment as the value of data erodes quickly, e.g.: fraud detection and prevention, real-time Identity check, Digital Wallet

2. The need for more data – requiring highly scalable systems as more data flows into the decision-making process, e.g.: Increasing the list of third party data sources that need to be curated, requirements for access to data to third-party providers (TPPs), increased mobile commerce.

3. The need to innovate – new services and new value-adds are increasingly data driven, e.g.: hyper-personalization, threats of Fin Tech.

Conclusion – Future-Proof Database for Systems of Engagement at 1/5th the Cost As the Payments industry evolves, the need to harness the data for smart decisions and actions is imperative to business success. Having a database that is uniquely suited for such needs is the first big step. Aerospike is the database that gives payment organizations the power to handle internet-scale data volumes and make the instantaneous decisions that customers expect.

Focus Area Use case Details Outcome

EfficiencyCommerce, P2P Fraud Detection &

Prevention

Detecting False positives in payment systems, across physical and online, for commerce as well as money transfer. Proactive

detection and response.

Low False Positives,Low lost $

P2P, Merchant Payments

Real-time Digital Identity Check

Check and validate digital identity in real-time as the transaction is being executed. multi-factor authentication, use social and third party data channels, access and act in real-

time

Risk reduction,Increase in Profitability

Commerce API for PSD2 PSP need to make customer data available to 3rd party (TPP) as APIs Compliance

Convenience Cash, P2P Digital Wallet Digital identity authentication, two-factor check for commerce, faster response to account inquiry,

Profitability, Customer Loyalty

Commerce Mobile Commerce catalog access for m-commerce, loyalty and rewards management POS targeting and offers

Upsell, Increased Channels

Innovation Commerce POS Targeting & Offers

Targeted, personalized offers and coupons at POS and me of purchase based on pro le data and shopping behavior Revenue

Hyper-Personalization Product and service innovations based on real-time customer insights, location and usage Revenue


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References 1. When to use Aerospike vs. Cassandra: http://www.aerospike.com/when-to-use-aerospike-vs-

cassandra/

2. Gartner Market Guide on HTAP enabling in-memory computing technologies: http://www.aerospike.com/lp/gartner-market-guide-htap-enabling-memory-computing-technologies/?utm_ls=Website&utm_lsd=AR_GartnerMarketGuideHTAP_DigitalPayments

3. Digital Payments Transformation eBook: http://www.aerospike.com/lp/enabling-digital-payments-transformation-eBook/?utm_ls=Website&utm_lsd=EB_Enabling-digital-payments-transformation-eBook-DBTA_DigitalPayments

4. Forrester Study on Hybrid Memory NoSQL Architecture for Mission-Critical, Real-Time

Systems of Engagement: http://www.aerospike.com/lp/forrester-study-hybrid-memory-nosql-architecture-mission-critical-real-time-systems-engagement/

5. How One Global Digital Payment Provider Drove Growth by Improving Fraud Detection http://s3-us-west-1.amazonaws.com/aerospike-fd/wp-content/uploads/2016/09/CS01-Fraud-Prevention.pdf

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Enabling Digital Payments Transformation · Enabling Digital Payments Transformation How real-time decisions are delivering business moments The Rise of Digitalization is Changing