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
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
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? Zero downtime migrations using MongoDB’s flexible schema Accelerate your digital transformation with these 5 Phases of Banking Modernization Reduce time-to-market for your customer lifecycle management applications MongoDB’s financial services hub