Shiv Pullepu

5 results

Payments Modernization and the Role of the Operational Data Layer

To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms. Driven by multiple factors—real-time payments, regulatory shifts like Payment Services Directive 2 (PSD2), heightened customer expectations, the power of open banking, and the disruptive force of fintech startups—the need for payment modernization has never been more pressing. But transformation is not without its challenges. Complex systems, industry reliance on outdated technology, high upgrade costs, and technical debt all pose formidable obstacles. This article will explore modernization approaches and how MongoDB helps smooth transformations. Approaches to modernization As businesses work to modernize their payment systems, they need to overcome the complexities inherent in updating legacy systems. Forward-thinking organizations embrace innovative strategies to streamline their operations, enhance scalability, and facilitate agile responses to evolving market demands. Two such approaches gaining prominence in the realm of payment system modernization are domain-driven design and microservices architecture : Domain-driven design: This approach focuses on a business's core operations to develop scalable and easier-to-manage systems. Domain-driven design ensures that technology serves strategic business goals by aligning system development with business needs. At its core, this approach seeks to break down complex business domains into manageable components, or "domains," each representing a distinct area of business functionality. Microservices architecture: Unlike traditional monolithic architectures, characterized by tightly coupled and interdependent components, a microservices architecture decomposes applications into a collection of loosely coupled services, each of which is responsible for a specific business function or capability. It introduces more flexibility and allows for quicker updates, facilitating agile responses to changing business requirements. Discover how Wells Fargo launched their next-generation card payments by building an operational data store with MongoDB . Modernizing with an operational data layer In the payments modernization process, the significance of an operational data layer (ODL) cannot be overstated. An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. The simplest representation of this pattern looks something like the sample reference architecture below. Figure 1: Operational Data Layer structure An ODL is deployed in front of legacy systems to enable new business initiatives and to meet new requirements that the existing architecture can’t handle—without the difficulty and risk of fully replacing legacy systems. It can reduce the workload on source systems, improve availability, reduce end-user response times, combine data from multiple systems into a single repository, serve as a foundation for re-architecting a monolithic application into a suite of microservices, and more. The ODL becomes a system of innovation, allowing the business to take an iterative approach to digital transformation. Here's why an ODL is considered ideal for payment operations: Unified data management: Payment systems involve handling a vast amount of diverse data, including transaction details, customer information, and regulatory compliance data. An ODL provides a centralized repository for storing and managing this data, eliminating silos and ensuring data integrity. Real-time processing: An ODL enables real-time processing of transactions, allowing businesses to handle high numbers of transactions swiftly and efficiently. This capability is essential for meeting customer expectations for instant payments and facilitating seamless transactions across various channels. Scalability and flexibility: Payment systems must accommodate fluctuating transaction volumes and evolving business needs. An ODL offers scalability and flexibility, allowing businesses to scale their infrastructure as demand grows. Enhanced security: An ODL incorporates robust security features —such as encryption, access controls, and auditing capabilities—to safeguard data integrity and confidentiality. By centralizing security measures within the ODL, businesses can ensure compliance with regulatory requirements and mitigate security risks effectively. Support for payments data monetization: Payment systems generate a wealth of data that can provide valuable insights into customer behavior, transaction trends, and business performance. An ODL facilitates real-time analytics and reporting by providing a unified platform for collecting, storing, and analyzing this data. Transform with MongoDB MongoDB’s fundamental technology principles ensure companies can reap the advantages of microservices and domain-driven design—specifically, our flexible data model and built-in redundancy, automation, and scalability. Indeed, the document model is tailor-made for the intricacies of payment data, ensuring adaptability and scalability as market demands evolve. Here’s how MongoDB helps with domain-driven design and microservice implementation to adopt industry best practices: Ease of use: MongoDB’s document model makes it simple to model or remodel data to fit the needs of payment applications. Documents are a natural way of describing data. They present a single data structure, with related data embedded as sub-documents and arrays, making it simpler and faster for developers to model how data in the application will be mapped to data stored in the database. In addition, MongoDB guarantees the multi-record ACID transactional semantics that developers are familiar with, making it easier to reason about data. Flexibility: MongoDB’s dynamic schema is ideal for handling the requirements of microservices and a domain-driven design. Domain-driven design emphasizes modeling the domain to reflect the business requirements, which may evolve over time. MongoDB's flexible schema allows you to store domain objects as documents without rigid schema constraints, facilitating agile development and evolution of the domain model. Speed: Using MongoDB for an ODL means you can get better performance when accessing data, and write less code to do so. A document is a single place for the database to read and write data for an entity. This locality of data ensures the complete document can be accessed in a single database operation that avoids the need internally to pull data from many different tables and rows. Data access and microservice-based APIs: MongoDB integrates seamlessly with modern technologies and frameworks commonly used in microservices architectures. MongoDB's flexible data model and ability to handle various data types, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. Scalability: Even if an ODL starts at a small scale, you need to be prepared for growth as new source systems are integrated, adding data volume, and new consuming systems are developed, increasing workload. MongoDB provides horizontal scale-out on low-cost, commodity hardware or cloud infrastructure using sharding to meet the needs of an ODL with large data sets and high throughput requirements. High availability: Microservices architectures require high availability to ensure that individual services remain accessible even in the event of failures. MongoDB provides built-in replication and failover capabilities, ensuring data availability and minimal downtime in case of server failures. Payment modernization is not merely a trend but a strategic imperative. By embracing modern payment solutions and leveraging the power of an ODL with MongoDB, organizations can unlock new growth opportunities, enhance operational efficiency, and deliver superior customer experiences. Learn how to build an operational data layer with MongoDB using this Payments Modernization Solution Accelerator . Learn more about how MongoDB is powering industries on our solution library .

May 15, 2024

Transforming Industries with MongoDB and AI: Financial Services

This is the fourth in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. In the dynamic world of financial services, the partnership between artificial intelligence (AI) and banking services is reshaping traditional practices, offering innovative solutions across critical functions. Relationship management support with chatbots One key service that relationship managers provide to their private banking customers is aggregating and condensing information. Because banks typically operate on fragmented infrastructure with information spread across different departments, solutions, and applications, this can require a lot of detailed knowledge about this infrastructure and how to source information such as: When are the next coupon dates for bonds in the portfolio? What has been the cost of transactions for a given portfolio? What would be a summary of our latest research? Please generate a summary of my conversation with the client. Until now, these activities would be highly manual and exploratory. For example, a relationship manager (RM) looking for the next coupon dates would likely have to go into each of the clients' individual positions and manually look up the coupon dates. If this is a frequent enough activity, the RM could raise a request for change with the product manager of the portfolio management software to add this as a standardized report. But even if such a standardized report existed, the RM might struggle to find the report quickly. Overall, the process is time-consuming. Generative AI systems can facilitate such tasks. Even without specifically trained models, RAG can be used to have the AI generate the correct answers, provide the inquirer with a detailed explanation of how to get to the data, and, in the same cases directly execute the query against the system and report back the results. Similar to a human, it is critical that the algorithm has access to not only the primary business data, e.g. the portfolio data of the customer, but also user manuals and static data. Detailed customer data, in machine-readable format and as text documents, is used to personalize the output for the individual customer. In an interactive process, the RM can instruct the AI to add more information about specific topics, tweak the text, or make any other necessary changes. Ultimately, the RM will be the quality control for the AI’s output to mitigate hallucinations or information gaps. As outlined above, not only will the AI need highly heterogeneous data from highly structured portfolio information to text documents and system manuals to provide a flexible natural language interface for the RMs, it will also have to have timely processing information about a customer's transactions, positions, and investment objectives. Providing transactional database capabilities as well as vector search makes it easy to build RAG-based applications using MongoDB’s developer data platform. Risk management and regulatory compliance Risk and fraud prevention Banks are tasked with safeguarding customer assets and detecting fraud , verifying customer identities, supporting sanctions regimes (Sanctions), and preventing various illegal activities (AML). The challenge is magnified by the sheer volume and complexity of regulations, making the integration of new rules into bank infrastructure costly, time-consuming, and often inadequate. For instance, when the EU's Fifth Anti-Money Laundering Directive was implemented, it broadened regulations to cover virtual currencies and prepaid cards . Banks had to update their onboarding processes swiftly, and software, train staff, and possibly update their customer interfaces to comply with these new requirements. AI offers a transformative approach to fraud detection and risk management by automating the interpretation of regulations, supporting data cleansing, and enhancing the efficacy of surveillance systems. Unlike static, rules-based frameworks that may miss or misidentify fraud due to narrow scope or limited data, AI can adaptively learn and analyze vast datasets to identify suspicious activities more accurately. Machine learning, in particular, has shown promise in trade surveillance, offering a more dynamic and comprehensive approach to fraud prevention. Regulatory compliance and code change assistance The regulatory landscape for banks has grown increasingly complex, demanding significant resources for the implementation of numerous regulations. Traditionally, adapting to new regulations has required the manual translation of legal text into code, provisioning of data, and thorough quality control—a process that is both costly and time-consuming, often leading to incomplete or insufficient compliance. For instance, to comply with the Basel III international banking regulations , developers must undertake extensive coding changes to accommodate the requirements laid out in thousands of pages of documentation. AI has the capacity to revolutionize compliance by automating the translation of regulatory texts into actionable data requirements and validating compliance through intelligent analysis. This approach is not without its challenges, as AI-based systems may produce non-deterministic outcomes and unexpected errors. However, the ability to rapidly adapt to new regulations and provide detailed records of compliance processes can significantly enhance regulatory adherence. Financial document search and summarization Financial institutions, encompassing both retail banks and capital market firms, handle a broad spectrum of documents critical to their operations. Retail banks focus on contracts, policies, credit memos, underwriting documents, and regulatory filings, which are pivotal for daily banking services. On the other hand, capital market firms delve into company filings, transcripts, reports, and intricate data sets to grasp global market dynamics and risk assessments. These documents often arrive in unstructured formats, presenting challenges in efficiently locating and synthesizing the necessary information. While retail banks aim to streamline customer and internal operations, capital market firms prioritize the rapid and effective analysis of diverse data to inform their investment strategies. Both retail banks and capital market firms allocate considerable time to searching for and condensing information from documents internally, resulting in reduced direct engagement with their clients. Generative AI can streamline the process of finding and integrating information from documents by using NLP and machine learning to understand and summarize content. This reduces the need for manual searches, allowing bank staff to access relevant information more quickly. MongoDB can store vast amounts of both live and historical data, regardless of its format, which is typically needed for AI applications. It offers Vector Search capabilities essential for retrieval-augmented generation (RAG). MongoDB supports transactions, ensuring data accuracy and consistency for AI model retraining with live data. It facilitates data access for both deterministic algorithms and AI-driven rules through a single interface. MongoDB boasts a strong partnership ecosystem , including companies like Radiant AI and Mistral AI, to speed solution development. ESG analysis Environmental, social, and governance (ESG) considerations can have a profound impact on organizations. For example, regulatory changes—especially in Europe—have compelled financial institutions to integrate ESG into investment and lending decisions. Regulations such as the EU Sustainable Finance Disclosure Regulation (SFDR) and the EU Taxonomy Regulation are examples of such directives that require financial institutions to consider environmental sustainability in their operations and investment products. Investors' demand for sustainable options has surged, leading to increased ESG-focused funds. The regulatory and commercial requirements, in turn, drive banks to also improve their green lending practices . This shift is strategic for financial institutions, attracting clients, managing risks, and creating long-term value. However, financial institutions face many challenges in managing different aspects of improving their ESG analysis. The key challenges include defining and aligning standards and processes and managing the flood of rapidly changing and varied data to be included for ESG analysis purposes. AI can help to address these key challenges in not only an automatic but also adaptive manner via techniques like machine learning. Financial institutions and ESG solution providers have already leveraged AI to extract insights from corporate reports, social media, and environmental data, improving the accuracy and depth of ESG analysis. As the market demands a more sustainable and equitable society, predictive AI combined with generative AI can also help to reduce bias in lending to create fairer and more inclusive financing while improving the predictive powers. The power of AI can help facilitate the development of sophisticated sustainability models and strategies, marking a leap forward in integrating ESG into broader financial and corporate practices. Credit scoring The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome by adopting alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI, while introducing the potential challenge of hallucination, represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness. The use of artificial intelligence, in particular generative artificial intelligence, as an alternative method to credit scoring has emerged as a transformative force to address the challenges of traditional credit scoring methods for several reasons: Alternative data analysis: AI models can process a myriad of information, including alternative data such as utility payments and rental history, to create a more comprehensive assessment of an individual's creditworthiness. AI offers unparalleled adaptability : As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust. Fraud detection: AI algorithms can detect fraudulent behavior by identifying anomalies and suspicious patterns in credit applications and transaction data. Predictive analysts: AI algorithms, particularly ML techniques, can be used to build predictive models that identify patterns and correlations in historical credit data. Behavioral analysis: AI algorithms can analyze behavioral data sets to understand financial habits and risk propensity. By harnessing the power of artificial intelligence, lenders can make more informed lending decisions, expand access to credit, and better serve consumers (especially those with limited credit history). However, to mitigate potential biases and ensure consumer trust, it's crucial to ensure transparency, fairness, and regulatory compliance when deploying artificial intelligence in credit scoring. AI in payments A lack of developer capacity is one of the biggest challenges for banks when delivering payment product innovation. Banks believe the product enhancements they could not deliver in the past two years due to resource constraints would have supported a 5.3% growth in payments revenues . With this in mind and the revolutionary transformation with the integration of AI, it is imperative to consider how to free up developer resources to make the most of these opportunities. There are several areas in which banks can apply AI to unlock new revenue streams and efficiency gains. The image below provides a high-level view of eight of the principal themes and areas. This is not an exhaustive view but does demonstrate the depth and breadth of current opportunities. In each example, there are already banks that have begun to bring services or enhancements to the market using AI technologies or are otherwise experimenting with the technology. Learn more about AI use cases for top industries in our new ebook, How Leading Industries are Transforming with AI and MongoDB Atlas .

April 4, 2024

Transforming Payments with Volante and MongoDB: A Modern Cloud Solution

In the ever-evolving world of banking and financial services, innovation and adaptability are key to success. Volante Technologies , a trusted cloud payments modernization partner to financial businesses worldwide, has been at the forefront of this transformation, empowering cloud-native payments solutions for over 125+ banks, financial institutions, and corporations across 35 countries to harness the power of digital payments and have the freedom to evolve and innovate at record speed. Volante's Payments as a Service and underlying low-code platform process millions of mission-critical transactions and trillions in value daily, so customers can focus on growing their business, not managing their technology. Real-time ready, API enabled, and ISO 20022 fluent, Volante’s solutions power four of the top five global corporate banks, two of the world’s largest card networks, and 66% of U.S. commercial deposits. In this customer story, we'll delve into how Volante, in partnership with MongoDB, has helped banks of all sizes modernize their payment systems, opening doors to new possibilities. The challenge Banks have long grappled with the constraints of monolithic infrastructure and legacy technologies that are a decade or more old and unable to handle the 24x7x365 digital demands of today's banking. In the fast-paced world of real-time payments – payments that clear and settle almost instantaneously using an underlying platform or network called payment rails – the need for speed and innovation is paramount. Corporate B2B banking, a lucrative revenue source, is also highly competitive. To win and retain customers, banks must offer improved and new payment services. Moreover, regulatory requirements demand that banks update their B2B payments systems to enable new payment rail standards such as FedNow and RTP as well as comply with new payment messaging standards like ISO20022 . The solution In response to these challenges, Volante Technologies, in partnership with MongoDB, introduced VolPay, a groundbreaking solution that redefined the way banks approach payments technology. This modern, cloud-native solution leverages MongoDB's cutting-edge technology to provide a modular, microservices API-based application. The benefits of this collaboration include: Modularity: Banks can now choose and integrate the services they need, making it a highly customizable solution. Innovative Tech Stack: By embracing modern technology, Volante's solution is resilient and able to meet the demands of today’s payments services and is future-proof as the landscape continues to evolve. Cloud Native: The solution is designed to operate in the cloud, enabling rapid deployment and scalability. Real-time: With real-time capabilities, banks can deliver 24x7x365 customer experiences that are critical in today's fast-paced digital world. Easy Integration and Extension: Volante's solution is easy to integrate with existing systems and extend as needed. Lower Total Cost of Ownership (TCO): The solution eliminates the need for costly "oil tanker" license upgrades, reducing both costs and implementation time. Global Connectivity: Banks can expand into new markets by connecting to over 100 global clearing and settlement schemes. MongoDB plays a crucial role in Volante's solution, providing a robust foundation for reading data, and ensuring high performance, scalability, and availability. The result MongoDB underpins the VolPay solution , a pioneering approach to payments technology. Unlike the monolithic systems of the past or generic middleware solutions, VolPay is an interoperable ecosystem of business services designed for payments innovation and transformation across the entire payments lifecycle including: Real-Time / Instant Payments, Global and Domestic Payments, ISO 20022 standardization, and more. Over 125+ global financial institutions take advantage of the cloud-native, API-ready solution. Built on a microservices architecture, VolPay is inherently real-time and ready to meet the demands of today's fast-paced payments environment. VolPay is available for deployment in various configurations across an organization’s modernization journey, from on-premise data centers to public cloud instances on major platforms like Microsoft Azure and AWS. Additionally, it is offered as a SaaS-managed service called " Payments-as-a-Service ." Customers looking to support their critical workloads in a self-managed environment can utilize MongoDB Enterprise Advanced , a comprehensive suite of products and services that put engineering teams in control of their self-managed MongoDB database, helping them drive security, performance, and operational efficiency. Those leveraging VolPay in the cloud can leverage the most advanced multi-cloud database service on the market – MongoDB Atlas – with unmatched data distribution and mobility, built-in automation for resource and workload optimization, and so much more. From on premises, to hybrid cloud and multi-cloud, MongoDB Enterprise Advanced and MongoDB Atlas deliver the scalability, high availability, and deployment flexibility today’s applications demand. In this transformative landscape, MongoDB plays a critical role as the archival (read) and transactional (write) database, ensuring performance, scalability, and high availability to meet the demanding transaction-per-second (tps) requirements of banks. In conclusion, the collaboration between Volante Technologies and MongoDB has ushered in a new era of payments technology, enabling banks to stay ahead of the curve and provide their customers with innovative, real-time payment experiences. This partnership has demonstrated that modern, cloud-native solutions can be implemented in a matter of months, offering a cost-effective and efficient alternative to the traditional, cumbersome systems that have held banks back for far too long. The future of payments is here, and it's being shaped by innovators like Volante and MongoDB. If you would like to learn more about why leading banks and payment providers choose Volante and MongoDB, take a look at the below resources: Volante Payments MongoDB for Payments Payments modernization – architectures shaping the future Volante Payments as a Service

November 8, 2023

MongoDB and BigID Delivering Scalable Data Privacy Compliance for Financial Services

Ensuring data privacy compliance has become a critical priority for banks and financial services. Safeguarding customer data is not only crucial for maintaining trust and reputation but also a legal and ethical obligation. In this blog, we will dive into why and how the financial services industry can adopt an approach to data privacy compliance effectively using BigID and MongoDB. Embracing a privacy-first mindset To establish a robust data privacy compliance framework, banks, and financial services must prioritize privacy from the onset. This entails adopting a privacy-first mindset throughout all aspects of their operations. Embedding privacy principles into the organizational culture helps create a foundation for compliance, ensuring that data protection is a core value rather than an afterthought. Understand the regulatory landscape Compliance with data privacy regulations is an ongoing process that requires a deep understanding of the applicable legal landscape. Banks and financial services should invest in a comprehensive knowledge of regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Digital Personal Data Protection (DPDP), and other relevant global and local regulations. This understanding helps organizations identify their obligations, assess risks, and implement necessary controls to ensure compliance. Ensuring compliance with regulatory requirements Data privacy compliance requirements vary based on specific regulations applicable to state, region or country. Organizations must adhere to these regulator requirements as its crucial to meeting legal obligations, maintaining trust and mitigating risks. Regularly Update Policies and Procedures: The data privacy landscape is constantly evolving, with new regulations and best practices emerging regularly. Banks and financial services should stay ahead of these developments to review and update their privacy policies and procedures accordingly. Regular audits and risk assessments should be conducted to identify gaps and ensure that the organization remains compliant with evolving requirements. Implement Data Discovery & Governance Frameworks: Effective data governance is a fundamental aspect of data privacy compliance. Banks and financial services should establish data governance frameworks with clear policies, procedures, and accountability mechanisms. This includes defining data ownership, identifying data across systems, implementing data classification, setting retention periods, and establishing secure data storage and disposal protocols. Regular audits and internal controls help ensure adherence to these policies and procedures. Streamline Consent Management: Transparency and consent are vital components of data privacy compliance. Banks and financial services should provide clear and easily understandable privacy notices to customers, outlining the types of data collected, the purposes of the processing, and any third-party sharing. Additionally, organizations should develop user-friendly consent mechanisms that enable individuals to make informed choices about their data. Fulfill User Rights and Data Subject Access Requests: All privacy regulations grant individuals various rights over their data, including the right to access, correct, delete, and restrict the sale of data. The fulfillment of data rights requires mechanisms such as customer self-service portals and automated workflows for data subject access requests. Conduct Privacy Impact Assessments (PIAs): Privacy Impact Assessments (PIAs) are essential tools for evaluating and mitigating privacy risks associated with data processing activities. Banks and financial services should regularly conduct PIAs to identify potential privacy concerns, assess the impact of data processing, and implement appropriate safeguards. PIAs enable organizations to proactively address privacy risks, demonstrate compliance, and enhance transparency in data processing practices. Prioritize Data Minimization and Purpose Limitation: Collecting and processing only the necessary personal data is a key principle of data privacy compliance. Banks and financial services should adopt data minimization strategies , limiting data collection to what is essential for legitimate business purposes. Furthermore, data should be processed only for specific, clearly defined purposes and not repurposed without obtaining appropriate consent or legal basis. By embracing data minimization and purpose limitation, organizations can reduce privacy risks and respect individuals' privacy preferences. Navigate Data Localization & Transfers: Data localization involves keeping data within the jurisdiction where it was collected. While this approach can help ensure data protection, it can also create challenges for businesses that operate in multiple countries. Implementing data localization practices ensures that customer data remains within the country's boundaries as well as adhering to cross-border data transfer requirements. Strengthen Security Measures: Protecting customer data from unauthorized access, breaches, and cyber threats is crucial. Banks and financial services should implement robust security measures, including encryption , access controls, intrusion detection systems, and regular security assessments. Ongoing staff training on cybersecurity awareness and best practices is essential to mitigate the risk of human error or negligence. Achieving privacy compliance with BigID and MongoDB Financial institutions need the ability to find, classify, inventory, and manage all of their sensitive data, regardless of whether it’s on-prem, hybrid-cloud, or cloud-based. Organizations must know where their data is located, replicated, and stored — as well as how it is collected and processed, it’s a momentous task — and requires addressing common challenges like siloed data, lack of visibility and accurate insight, and balancing legacy systems with cloud data. All while meeting a litany of compliance requirements. With a major shift towards geographically dispersed data, organizations must make sure they are aware of – and fully understand – the local and regional rules and requirements that apply to storing and managing data. Organizations without a strong handle on where their data is stored potentially risk millions of dollars in regulatory fines for mishandling data, loss of brand credibility, and distrust from customers. A modern approach relying on modern technologies like BigID & MongoDB helps to solve data privacy, data protection, and data governance challenges. BigID, the industry leader for data security, privacy, compliance, and governance, is trusted by some of the world's largest financial institutions to deliver fast and accurate data discovery, classification, and correlation across large and complex data sets. BigID utilizes MongoDB as the internal data store for the platform to help generate data insights at scale, automate advanced discovery & classification, and accommodate complex enterprise requirements. As technology partners, MongoDB’s document model and distributed architecture enable BigID to deliver a scalable and flexible data management platform for data privacy and protection. How BigID powered by MongoDB addresses privacy compliance challenges By taking a privacy-first approach to data and risk, organizations can address the challenges of continuous compliance, minimize security risks, proactively address data privacy programs, and strengthen data management initiatives. BigID, powered by MongoDB, helps organizations identify, manage, and monitor all personal and sensitive data activity to achieve compliance with several data privacy requirements. Organizations get: Deep Data Discovery: BigID helps organizations discover and inventory their critical data, including financial information. This enables organizations to understand what data they have and where it is located, which is an important first step in achieving compliance. Accurate Classification: With exact value matching, BigID graph based technology can identify and classify personal and sensitive data in any environment such as email, shared drives, databases, data lakes, and many more. Efficient Data Mapping: Automatically map PII and PI to identities, entities, and residencies to connect the dots in your data environments. Streamlined Data Lifecycle Management: Accurately find, classify, catalog, and tag your data and easily enforce governance & control – from retention to deletion. Fulfillment of Consent & Data Rights Request: Automate consent and data rights management with a privacy portal that includes a seamless U/X that manages data subject rights requests (DSAR). Centralize DSAR’s with automated access and deletion workflows to fulfill end-to-end data rights requests. Effective Privacy Impact Assessments (PIA/DPIA): Easily build seamless workflows and frameworks for privacy impact assessments (PIA) to estimate the risk associated with all data inventory. ML-based Data Access Management: For full compliance with specific requirements, BigID helps mitigate risk with significant open-access requirements to remediate file access violations on critical data across all data environments. Validated Data Transfers: Monitor cross-border data transfers and create policies to enforce data residency and localization requirements. Effective Remediation: BigID helps to define the remediation action related to critical data to provide audit records with integration to ticketing systems like Jira for seamless workflows. By adopting a privacy-first approach to data and risk, financial services organizations can tackle the challenges of continuous compliance, mitigate security risks, and enhance data management initiatives. BigID, powered by MongoDB, offers comprehensive solutions to help organizations identify, manage, and monitor personal and sensitive data activities, enabling them to achieve compliance with various data privacy requirements. Looking to learn more about how you can reduce risk, accelerate time to insight, and get data visibility and control across all your data - everywhere? Take a look at the below resources: Control your data for data security, compliance, privacy, and governance with BigID Data-driven privacy compliance and automation for new and emerging data privacy and protection regulation Protect your data with strong security defaults on the MongoDB developer data platform Manage and store data where you want with MongoDB MongoDB for Financial Services

June 21, 2023

Accelerating to T+1 - Have You Got the Speed and Agility Required to Meet the Deadline?

On May 28, 2024, the Securities and Exchange Commission (SEC) will implement a move to a T+1 settlement for standard securities trades , shortening the settlement period from 2 business days after the trade date to one business day. The change aims to address market volatility and reduce credit and settlement risk. The shortened T+1 settlement cycle can potentially decrease market risks, but most firms' current back-office operations cannot handle this change. This is due to several challenges with existing systems, including: Manual processes will be under pressure due to the shortened settlement cycle Batch data processing will not be feasible To prepare for T+1, firms should take urgent action to address these challenges: Automate manual processes to streamline them and improve operational efficiency Event-based real-time processing should replace batch processing for faster settlement In this blog, we will explore how MongoDB can be leveraged to accelerate manual process automation and replace batch processes to enable faster settlement. What is a T+1 and T+2 settlement? T+1 settlement refers to the practice of settling transactions executed before 4:30pm on the following trading day. For example, if a transaction is executed on Monday before 4:30 pm, the settlement will occur on Tuesday. This settlement process involves the transfer of securities and/or funds from the seller's account to the buyer's account. This contrasts with the T+2 settlement, where trades are settled two trading days after the trade date. According to SEC Chair Gary Gensler , “T+1 is designed to benefit investors and reduce the credit, market, and liquidity risks in securities transactions faced by market participants.” Overcoming T+1 transition challenges with MongoDB: Two unique solutions 1. The multi-cloud developer data platform accelerates manual process automation Legacy settlement systems may involve manual intervention for various tasks, including manual matching of trades, manual input of settlement instructions, allocation emails to brokers, reconciliation of trade and settlement details, and manual processing of paper-based documents. These manual processes can be time-consuming and prone to errors. MongoDB (Figure 1 below) can help accelerate developer productivity in several ways: Easy to use: MongoDB is designed to be easy to use, which can reduce the learning curve for developers who are new to the database. Flexible data model: Allows developers to store data in a way that makes sense for their application. This can help accelerate development by reducing the need for complex data transformations or ORM mapping. Scalability: MongoDB is highly scalable , which means it can handle large volumes of trade data and support high levels of concurrency. Rich query language: Allows developers to perform complex queries without writing much code. MongoDB's Apache Lucene-based search can also help screen large volumes of data against sanctions and watch lists in real-time. Figure 1: MongoDB's developer data platform Discover the developer productivity calculator . Developers spend 42% of their work week on maintenance and technical debt. How much does this cost your organization? Calculate how much you can save by working with MongoDB. 2. An operational trade store to replace slow batch processing Back-office technology teams face numerous challenges when consolidating transaction data due to the complexity of legacy batch ETL and integration jobs. Legacy databases have long been the industry standard but are not optimal for post-trade management due to limitations such as rigid schema, difficulty in horizontal scaling, and slow performance. For T+1 settlement, it is crucial to have real-time availability of consolidated positions across assets, geographies, and business lines. It is important to note that the end of the batch cycle will not meet this requirement. As a solution, MongoDB customers use an operational trade data store (ODS) to overcome these challenges for real-time data sharing. By using an ODS, financial firms can improve their operational efficiency by consolidating transaction data in real-time. This allows them to streamline their back-office operations, reduce the complexity of ETL and integration processes, and avoid the limitations of relational databases. As a result, firms can make faster, more informed decisions and gain a competitive edge in the market. Using MongoDB (Figure 2 below), trade desk data is copied into an ODS in real-time through change data capture (CDC), creating a centralized trade store that acts as a live source for downstream trade settlement and compliance systems. This enables faster settlement times, improves data quality and accuracy, and supports full transactionality. As the ODS evolves, it becomes a "system of record/golden source" for many back office and middle office applications, and powers AI/ML-based real-time fraud prevention applications and settlement risk failure systems. Figure 2: Centralized Trade Data Store (ODS) Managing trade settlement risk failure is critical in driving efficiency across the entire securities market ecosystem. Luckily, MongoDB integration capabilities (Figure 3 below) with modern AI and ML platforms enable banks to develop AI/ML models that make managing potential trade settlement fails much more efficient from a cost, time, and quality perspective. Additionally, predictive analytics allow firms to project availability and demand and optimize inventories for lending and borrowing. Figure 3: Event-driven application for real time monitoring Summary Financial institutions face significant challenges in reducing settlement duration from two business days (T+2) to one (T+1), particularly when it comes to addressing the existing back-office issues. However, it's crucial for them to achieve this goal within a year as required by the SEC. This blog highlights how MongoDB's developer data platform can help financial institutions automate manual processes and adopt a best practice approach to replace batch processes with a real-time data store repository (ODS). With the help of MongoDB's developer data platform and best practices, financial institutions can achieve operational excellence and meet the SEC's T+1 settlement deadline on May 28, 2024. In the event of T+0 settlement cycles becoming a reality, institutions with the most flexible data platform will be better equipped to adjust. Top banks in the industry are already adopting MongoDB's developer data platform to modernize their infrastructure, leading to reduced time-to-market, lower total cost of ownership, and improved developer productivity. Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post. Looking to learn more about how you can modernize or what MongoDB can do for you? 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May 25, 2023