Engineer Data in a Meaningful Way

1. Data Analytics Services & Solutions WHITEPAPER Engineer Data in a Meaningful Way

2. utilizing cutting-edge techniques in data collection, processing, storage, and analysis to extract valuable insights, improve decision-making, and deliver novel financial services that meet the changing needs of both businesses and consumers. Engineer Data in a Meaningful Way The capacity to engineer and utilize data in a meaningful way is crucial in the quickly developing field of financial technology (fintech). Fintech engineers are at the forefront of releasing the potential of enormous and complex financial datasets. They do this by Addressing challenges in data engineering for the banking industry Older systems have trouble keeping up: “In 2022, a startling 92% of the top 100 global banks continued to run their essential business processes on IBM mainframes, highlighting the industry’s longstanding resistance to change. In contrast, thanks to agile startups, fintech use increased to 80% in the same year. Legacy banking systems have trouble keeping up with the 40% yearly growth in big data volume, which makes the problem much more difficult. The stability and security of the infrastructure are seriously threatened by this. Financial institutions must take prompt action to solve this, either by improving processing capabilities or revamping systems. Inaction can be expensive because system failures for a worldwide bank might result in losses of up to $2 million every hour. Scaling data: amplifying business risks Second, where there is data, there is risk (particularly in light of the legacy issue we already highlighted). It is obvious that financial service providers must constantly ensure the security of the user information they collect and handle. However, according to ISACA International, just 38% of businesses globally are equipped to deal with the issue. Because of this, cybersecurity is still one of the most pressing problems in banking. Additionally, data security laws are becoming stricter. Businesses throughout the world that wish to gather and use user data now face some limitations thanks to the implementation of GDPR. This also has to be considered. 2

3. Engineer Data in a Meaningful Way Big data is becoming excessive It’s hardly surprising that organizations find it difficult to manage the volume and variety of data that they have to deal with. When attempting to distinguish between useful data and useless data, this becomes even more clear. There is still too much irrelevant data to filter through, even while the proportion of potentially usable data is increasing. This calls on businesses to strengthen their procedures for data analysis, get ready for even more data analysis, and, if at all feasible, find a new use for previously discarded data. Despite the aforementioned difficulties, the benefits of big data in banking more than outweigh any dangers. Data is a universal fuel that can drive your business forward because of the insights it provides, the resources it frees up, and the money it saves. Driving Business Success through Data Engineering: Real-World Case Studies Collaboration with Quantexa: Challenge: Establishing a comprehensive, interconnected view of entities across business lines, including unbanked parent and subsidiary relationships and individual-to- corporate connections. Approach: Leveraging the bank’s existing Quantexa solution platform and a dedicated team of 12, the project encompassed solution design, scope determination, and delivery planning. It involved data sourcing, ingestion, entity network resolution generation, graph scripting, scoring, task loading, and re-alerting. Impact: Upon deployment, the solution identified a significant number of client and non-client prospective connections and referral points. Notably, the newly created scores are now reusable, providing ongoing support for various use-case solutions. Denmark Bank: The biggest bank in Denmark is Danske Bank, which has a customer base of more than 5 million. The bank was having trouble managing up to 1200 false positives each day due to its fraud detection methods having a relatively low percentage, or barely a 40% fraud detection rate. They found this to be an alarming rate, necessitating prompt action. They then made the decision to collaborate with Teradata, a well-known database and analytics service provider, in order to use cutting-edge Big Data analytics to enhance their fraud detection methods, and they immediately noticed some notable outcomes. The bank observed a 60% decrease in false positives and predicted that it would soon reach an 80% level. The real positive rate also increased by 50%. J.P. Morgan: In terms of market value, it is also the most valuable bank in the entire globe. The volume of data it creates, which includes a sizable amount of credit card information and other transactional data of its clients, is inconceivable given that it has a customer base of over 3 billion. To deal with massive data, they have embraced big data technology, primarily Hadoop. They are now able to produce insights into customer trends by using Big Data Analytics, and the same reports are provided to their clients. These reports are prepared quickly and they can assess each consumer separately. Data engineering brings order to the systems’ daily barrage of chaotic input. As a result, it enables companies to improve data for usability. It is important in the following endeavors as well: • Aids in the discovery of best practices for streamlining the software development life cycle. • Increases information security and defends the company against cyber-attacks. • Broadens one’s understanding of the corporate world. 3

4. Engineer Data in a Meaningful Way Bank-Fintech Collaborations: empowering data engineering for financial innovation • To support its banking services outside of Australia, the Commonwealth Bank of Australia (CBA) has chosen Misys to provide cloud-ready, linked institutional banking technology solutions. With the help of Misys’ linked corporate banking system, end-to-end business operations for connected lending, trading, and cash management will be digitized. The arrangement includes CBA’s activities outside of Australia and will give the bank’s overseas branches in Asia, Europe, and the United States of America access to a single, straightforward platform. Supply chain financing was a potential investment for Deutsche Bank. The objective was straightforward: to establish a partnership that would enable them to integrate these supply chain solutions and technologies into the banks’ own products. In order to provide its business clients with discounting and reverse factoring solutions, the bank teamed with Traxpay. Partners: Plaid, a fintech business that specializes in data connectivity and APIs, has collaborated with Goldman Sachs, a worldwide investment banking and financial services organization. The partnership aims to employ Plaid’s data connectivity and aggregation capabilities to improve Goldman Sachs’ Marcus consumer-focused digital banking product. • • Implementation Benefits Enhanced Data Analysis To enable users to safely link their external bank accounts, Goldman Sachs Plaid’s technology into Marcus. Through this link, users of the Marcus app may examine all of their financial accounts, even those held with other banks. Customer improved since it was simpler for customers to access and manage their financial data from several one location, making it more practical and user-friendly. experience was Goldman Sachs might better understand its financial preferences behaviors by compiling and analyzing data from multiple accounts. This would allow it to better adapt its services and offerings. clientele’s integrated and accounts in 4

5. Engineer Data in a Meaningful Way Conclusion In the swiftly evolving realm of financial technology effective data paramount. Fintech engineers are at the forefront, harnessing vast and intricate financial datasets through cutting-edge data to derive insights and provide innovative financial services tailored to evolving business and consumer needs. However, challenges persist, notably in the banking sector, where a startling 92% of top global banks clung to legacy systems in 2022. This reliance on outdated technology impedes their ability to adapt to the explosive 40% annual growth in big data, endangering infrastructure stability and security. Moreover, data security concerns persist, as just 38% of businesses globally are adequately prepared to confront cybersecurity threats, exacerbated by stringent regulations like GDPR. Nevertheless, the benefits of big data in banking far outweigh the risks, driving insights, resource liberation, and cost savings. Real-world case studies demonstrate institutions’ strategies to address these challenges. Collaborations with firms like Quantexa have facilitated comprehensive of interconnected entities across business lines, resulting in numerous client and non-client connections and reusable scores to support various use-case Denmark’s Danske Bank partnered with Teradata to combat a high rate of false positives in fraud detection, achieving a 60% decrease and anticipating an 80% detection rate, with a 50% increase in the real positive rate. J.P. Morgan, the world’s most valuable bank, harnessed big data technology to gain rapid insights into customer trends through Big Data Analytics, elevating client services. Collaborations between and fintech companies, such as Commonwealth Bank of Australia’s partnership with Deutsche Bank’s collaboration with Traxpay, underscore the industry’s commitment to data engineering’s potential for innovation, propelling the financial sector into an era of unprecedented growth. (fintech), utilization is views techniques solutions. banks Misys and leveraging financial 5

6. Engineer Data in a Meaningful Way About the Author SMRITI LOOMBA • Senior Analyst - Corporate Op-Ex Smriti Loomba, an MBA graduate, has a rich experience of more than 7.5 years in secondary research, primary research, and research report writing. She has worked on multiple technology projects, covering Artificial Intelligence (AI), Blockchain, Machine Learning (ML), Quantum, and Big Data, among others. She helps clients grow their businesses by providing them with industry insights, growth opportunities, Go-To-Market (GTM) strategies, and marketing techniques. Disclaimer This document makes descriptive reference to trademarks that may be owned by others. The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or get commercially benefited from it or imply the existence of an association between SGA and the lawful owners of such trademarks. Information regarding third-party products, services, and organizations was obtained from publicly available sources, and SGA cannot confirm the accuracy or reliability of such sources or information. Its inclusion does not imply an endorsement by or of any third party. Copyright © 2023 SG Analytics Pvt. Ltd. www.sganalytics.com GET IN TOUCH Pune | Hyderabad | Bengaluru | London | Zurich | New York | San Francisco | Amsterdam | Toronto | Wroclaw 6