DATA PLATFORMS IN FINANCIAL SERVICES THE NOSQL EDGE WHITE PAPER Data Platforms in Financial Services: The NoSQL Edge Copying or distribution without written permission is prohibited 2 Contents Application and Criticality of Data Processing at Scale in Financial Services …………….. 06 • Fraud prevention, personalized customer experience, and risk management • Explosion of data sources, data sets, and formats • Shift from batch to near real-time to instantaneous • Growth of web-scale applications Common Challenges in Data Processing in Financial Services ………………………………….. 09 • Data silos caused by organizational structures • The increasing need for real-time processing • Always-on availability and performance • Consistency of data • Transition from mainframes to distributed workloads • Processing data for AI/ML in real-time Data Handling and Processing in Traditional Architectures ………………………………………. 12 • Not designed for extreme real-time workloads • Concentrates on data integrity over performance • Vertical scaling • Built for persistence and batch processing • Not in-memory • Slower response when implemented for real-time feedback • Caching layers built on top of operational layers NoSQL Databases in the Changing World of Financial Services …………………………..……. 15 • Digital transformation in financial services and the changes imposed by it • What is a NoSQL database? • Types of NoSQL databases • Growth of NoSQL databases Where NoSQL Databases Fit in Financial Services ……………………………………………………. 20 • NoSQL databases are better equipped to handle larger data sets • Ease of handling both structured and unstructured data formats • High-performance handling thereby designed for real-time feedback at web-scale • Built to connect legacy systems with newer and faster front-end systems • Built to drive two-paced development of modern architectures • Can scale better horizontally as the data grows • Easier and faster to implement compared to traditional databases • Support for better performance handling Opportunities and Business Benefits That Can Be Derived from NoSQL Databases …... 24 • Improved fraud prevention and financial crime monitoring • Real-time digital ID validation • Personalized offerings using improved real-time data analytics • Targeted marketing • Pre-trade assistance Conclusion ……………………………………………………………………………………………………….…… 28 The Aerospike Advantage …………………………………………………………………………………...…. 30 • Top 3 Global Online Payments System: Fraud detection for digital payments • Top 3 Global Brokerage Firm: Intra-day trading for financial services • LexisNexis Risk Solutions/ThreatMetrix: Tracking digital identity Bibliography ………………………………………………………………………………………………………….. 33 Copying or distribution without written permission is prohibited 3 Copying or distribution without written permission is prohibited 4 Executive Summary The financial services industry is a crucial cog absence of real-time data processing, have in the functioning of an economy. The an impact on performance and intended industry acts as a force multiplier of economic business outcomes. activity from storing to creating wealth and providing access to credit. It has always strived to evolve with the changing needs of The emphatic acceptance of modern the consumer. platforms and technologies such as NoSQL databases (which are powering tech giants like Facebook, Amazon, and Google) has The digitization of financial services has been provided the opportunity to tackle these an ongoing process for the past few decades. challenges effectively. This report explores The transition has not always been smooth the various facets of NoSQL databases and and easy from legacy mainframe systems of how their growth has played a crucial role in the 60s and 70s to today’s cloud-based transforming several financial services financial services. Customers are increasingly business areas. FinTech companies such as adopting digital channels to access banking neobanks, online trading platforms, and services and other offerings. Neobanks have payment platforms are already incorporating launched that are completely digital, NoSQL databases in their architectures and without having any branch banking. are reaping the benefits of being agile and Financial services can no longer offer generic resilient. solutions that target a wide range of consumers. Be it an individual working in a multinational corporation or a student, a This white paper also investigates the several neighborhood business, or a large-scale advantages that NoSQL offers over enterprise catering to a global populace, the traditional databases for a financial needs of each client are unique. To address institution. The ability to handle structured this diversity, institutions are consuming data and unstructured data formats, ease of from multiple sources inside and outside the connecting legacy systems with new, enterprise. These include structured data speedier front-end systems, horizontal from sources like enterprise systems, market scaling, and faster implementation are some systems, and government databases, as well of the points that are explored in detail. It also as unstructured data from social networks analyzes how each of these advantages and media. translates to better performance in the various functions of a financial institution ranging from fraud prevention, better This white paper explores the criticality of customer service and customer 360, targeted real-time data processing in financial marketing, data analytics, and real-time institutions and the common challenges they digital identity verification. face. The growth in online transactions and web applications is driving a focus on fraud prevention, customer experience, and a new Financial institutions are increasingly looking digital imperative that requires new to move operational and delivery models from thinking on how data is stored, managed, physical to digital platforms as more and and processed. However, the challenges more customers prefer these channels. posed by legacy infrastructure, such as data NoSQL will play a critical role in enabling inconsistency, difficulty in transitioning from this shift and in ensuring the high quality of mainframes to distributed workloads, and the services that their new, digital clientele expects from them. Copying or distribution without written permission is prohibited 5 01. Application and Criticality of Data Processing at Scale in Financial Services Copying or distribution without written permission is prohibited 6 Application and Criticality of Data Processing at Scale in Financial Services Fraud prevention, personalized customer experience, and risk management Financial services firms have multiple The collection of large sets of data on requirements that their data processing consumer spend and behavior enables firms architectures must meet. They process large to offer personalized services catering to their amounts of data ranging from customer requirements. Financial firms can cross-market records, transactional data, and records of various products and services based on the multiple interactions with third-party providers projected needs of the consumer and their such as regulators, vendors, and clients. The real- spending capabilities. time processing and analysis of this data enables faster decision-making and provides a rewarding Along with better customer service, real-time experience to the consumer. processing also enables for better risk management. For regulatory purposes, financial Fraud prevention is an important aspect both institutions must measure and manage the in terms of financial and reputational damage. riskiness of customer portfolios, requiring a move Since there is a transfer of wealth involved in to real-time transaction processing for a large these transactions, it is very crucial for these part of what a bank does. organizations to ensure that there are no attacks by unscrupulous entities. Explosion of data sources, data sets, and formats For financial services enterprises, data resides in The rise of FinTech firms has also put pressure on multiple siloed environments such as data traditional players in the financial services warehouses, core applications, finance and risk industry to transform themselves digitally. solutions, analytics solutions, mobile Digital-only banks, online trading platforms, applications, and social media interactions, to and blockchain-based payment systems are name a few. Since this data is not meaningfully some of the new business models adopted by linked across these silos, it has become less FinTechs in financial services. These accessible, thereby compromising useful developments have now led to the traditional insights into customers, products, partners, sales players converting themselves into data-driven channels, and financial performance. enterprises that can compete with the more agile, web-based FinTech startups. Figure. 1 Data from Multiple Sources Leading to Better Insights New Business Models Data & Value Chain Processing Participants Large Amounts New Data Insights of Data (Raw) (Structured) & Values To do this, they have to process large amounts of data arriving from various sources such as images, audio, and video generated through mobile applications and other devices. This new data, combined with newer business models and participants in the value chain that increase the digitization of financial services, provides enterprises with opportunities to gain additional insight and value. Copying or distribution without written permission is prohibited 7 Application