Securing Digital Transformation with MongoDB and RegData

Data security and privacy have long been paramount to the financial industry, but they are especially critical for institutions undergoing digital transformations or those implementing new technology. For example, the integration of artificial intelligence (AI) and machine learning (ML) into organizations’ infrastructure and offerings introduces security and privacy complexities, making it all the more essential for financial organizations to safeguard sensitive information while complying with regulations.

The consequences of a data breach are extensive and significantly impactful. These incidents have transformed from simple cybersecurity concerns into catalysts for financial losses, reputational harm, legal challenges, regulatory penalties, and a significant decline in consumer trust. Even with an increased focus on data security, organizations must adopt modern data architecture to effectively mitigate these risks.

For example, using a database solution like MongoDB with built-in encryption, role-based access control, and audit logging can help organizations safeguard sensitive data and respond proactively to potential vulnerabilities.

The challenge of data security in finance

Financial institutions face numerous challenges in protecting data integrity during modernization efforts. The increasing sophistication of cyberattacks, coupled with the need to comply with evolving regulations like the General Data Protection Regulation (GDPR) and the Digital Operational Resilience Act (DORA), creates a complex environment for data management. Institutions must also navigate technical sprawl, where diverse applications and data management systems complicate compliance and operational efficiency.

Addressing these challenges requires a holistic approach that integrates data protection into the core design of digital transformation initiatives. Financial institutions need to adopt robust data management practices, ensure the encryption of sensitive data, and maintain vigilant cybersecurity measures. Collaboration with trusted third-party vendors, adopting a privacy-first strategy, and complying with global data protection regulations are essential steps toward safeguarding data privacy in this rapidly evolving digital landscape.

Discover how the RegData Protection Suite (RPS), built on MongoDB, enables you to balance technological advancement with regulatory requirements.

The solution: MongoDB and RegData

MongoDB offers unparalleled reliability, scalability, and flexibility, making it an ideal choice for financial services. MongoDB enables financial institutions to combine operational and AI data in a unified interface and can be deployed on-premises with Enterprise Advanced or across any major cloud provider with MongoDB Atlas, multi-cloud, and hybrid cloud when needed. When combined with RegData's Protection Suite (RPS), organizations can effectively tackle the challenges of digital transformation. RPS is a cloud-native application security platform designed to protect sensitive data through advanced techniques such as encryption, anonymization, and tokenization.

Figure 1. Simplified architecture of the RPS solution.
Diagram showing the architecture of the RPS solution. The request gets sent into the integrations, such as file processor, API, Proxy, Mai services, and DB driver. The data then works with logging, reporting, and core configuration to send data to the engine which than produces the response.

Key Features of RegData Protection Suite:

  1. Core Configuration: Provides services and a user interface to configure the protection of data.

  2. RPS Engine: A sophisticated core engine equipped with various data protection tools. This module is the heart of the application and is responsible for all data protection. Consists of encryption, anonymization, tokenization, and pseudonymization

  3. RPS Reporting: A vital component focused on data protection oversight. It gathers and analyzes information on the business application activities protected by RPS to generate a range of valuable reports

  4. RPS Manager: Provides end-to-end monitoring capabilities for the components of the RPS platform.

  5. RPS Integration: RPS seamlessly integrates with various applications, ensuring that sensitive data is protected across diverse environments.

The synergy between MongoDB and RegData shines through in practical applications. For instance, a private bank can leverage hybrid cloud deployments to modernize its operations while maintaining data security. By utilizing RPS, the bank can protect sensitive information during cloud migrations and ensure compliance with regulatory requirements.

Additionally, as financial institutions explore outsourcing, RPS helps mitigate risks by anonymizing sensitive data, allowing organizations to maintain control over their data even when leveraging external service providers.

Embracing a zero-trust approach for gen AI applications

With the rise of AI (and particularly gen AI), banks are developing increasingly more AI- and gen AI-powered applications. While on-premise AI/gen AI model development and testing provides a high level of data security and confidentiality, it may not be within the bank’s budget to afford a production-grade GPU compute pool or one that is large enough to offer sufficient scalability and economy of scale. With this dilemma, banks have begun developing models in private clouds and then deploying on the public cloud to leverage its scalability and economy of scale.

MongoDB can serve as that unified operational data layer for a variety of data sources, structured, semi-structured, or unstructured that may also come in different forms (eg. tabular, geospatial, network graph, time series, etc.) for the model development, training, fine-tuning and/or testing. When the model is tested and found to be working, it can then be deployed to the public cloud to serve the AI/gen AI applications.

The figure below shows the high-level architecture of how a private bank implemented its gen AI application with MongoDB and RPS.

Figure 2. Gen AI data flow architecture focused on data protection.
Diagram showing the gen ai data flow architecture. On the lest side is the private cloud, where data is stored on legacy DB's, files, media, and MongoDB. This flows into discovery and categorisation, and then over to the API Gateway. The API gateway connects to the Public cloud.

The road to modernization

As financial institutions navigate the complexities of digital transformation, the partnership between MongoDB and RegData offers a robust solution for securing data. By adopting a comprehensive data protection strategy, organizations can innovate confidently while ensuring compliance with regulatory standards. Embracing these technologies not only enhances data security but also paves the way for a more resilient and agile financial sector.

Establishing a robust data architecture with a modern data platform like MongoDB Atlas enables financial institutions to effectively modernize by consolidating and analyzing data in any format in real-time, driving value-added services and features to consumers while ensuring privacy and security concerns are adequately addressed with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies.

Are you prepared to harness these capabilities for your projects or have any questions about this? Then please reach out to us at industry.solutions@mongodb.com or nfo@regdata.ch.

You can also take a look at the following resources:

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Built With MongoDB: Kraken Coding Revolutionizes Clinical Decision Support

Within the world of healthcare, clinicians often find themselves overwhelmed by the sheer volume of information they must process to provide optimal patient care. Text-based guidelines and procedures, while essential, can be cumbersome and difficult to navigate. Recognizing this challenge, John Shanks, co-founder of Kraken Coding , embarked on a mission to transform the way clinical staff access decision support information through its product, Clinical Branches. The birth of clinical branches Kraken Coding began its journey with the “Open Source Antimicrobial Stewardship” project, which aimed to provide decision support pathways for antimicrobial prescribing. Despite its limited functionality, the project gained traction. Early success, coupled with valuable feedback from users, inspired the team to develop a more robust solution using a modern database platform, MongoDB. The initial vision for Clinical Branches was simple yet powerful: to create a tool that would streamline access to critical clinical information. Shanks, who developed the original tool during his tenure as a hospital pharmacist, understood firsthand the challenges posed by high staff turnover and the difficulty of training new employees. Clinicians were inundated with lengthy documents that often went unread, leading to potential risks in patient care. The goal was to create a structured decision tree that would guide clinicians through the decision-making process quickly and efficiently. A unique approach to decision support What sets Kraken Coding apart from other solutions in the healthcare industry is its innovative use of data generated through the decision-making process. By abstracting text-based procedures into semi-structured decision algorithms, Clinical Branches not only provide immediate guidance but also collect valuable data that can be analyzed for continuous improvement. The team is currently working on an exciting project supported by the UNSW Tyree IHealthE Innovation Catalyst Awards, which aims to convert this data into semantic embeddings. By utilizing vector search technology, they hope to predict risks for patient admissions early in the patient journey. The role of MongoDB in scaling success A crucial element of Kraken Coding’s growth has been its partnership with MongoDB Atlas . Shanks initially chose MongoDB due to its simplicity and scalability. At university, the practical exercises and tutorials involved mostly SQL inner joins and database normalization. Any courses that did cover NoSQL did so only superficially, but after doing an online course using MongoDB, he saw how easy it was to set up an application and was happy to ditch SQL. With Clinical Branches, he knew MongoDB would be the perfect choice. The focus on simplicity and scalability aligned perfectly with the startup’s needs. As the company expanded its operations, particularly with the recent deployment of a new application server in Canada, MongoDB’s capabilities made it easy to add nodes and tackle data sovereignty issues without compromising sensitive information. Without MongoDB, the team would have to manually set up a database instance for a new geographic location, which can involve complex configurations to manage consistency, conflict resolution, and data synchronization across regions. With MongoDB, they can deploy to any region in Canada with a few clicks. One of the standout features that Kraken Coding has benefited from is MongoDB Atlas Vector Search , which has already shown promise in predicting optimal drug dosing based on historical patient data. Support from the MongoDB for Startups program The MongoDB for Startups program has played a pivotal role in Kraken Coding’s development. The Atlas credits provided financial relief for the early-stage venture, allowing the team to focus on building their product without the burden of high operational costs. Additionally, the free technical advice they received during the initial phases of their project was invaluable. A two-day technical consultant review ensured that their database indexing, design, and application routers were correctly configured before going live, saving the team about 50 hours of rework, by Shanks’ estimate, and ensuring the platform was scalable. Looking ahead: The future of Clinical Branches As Kraken Coding continues to innovate, the roadmap for the coming year is filled with exciting developments. The team is preparing to start phase one audits to register their software as a medical device, focusing on Bayesian inference and vector search to support optimal drug dosing strategies. They are also launching a new project that will utilize pathway answers in conjunction with patient information to predict likely patient outcomes. With their first pilot in Canada on the horizon, the future looks bright for Kraken Coding. Kraken Coding is at the forefront of revolutionizing clinical decision support through its innovative product, Clinical Branches. By leveraging modern technology and data-driven insights, the startup is not only improving the efficiency of clinical workflows but also enhancing patient care. If you’re working in healthcare and looking for a simple initiative that can have a big impact for your service, check out Kraken Coding . To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start guide . Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now .

January 21, 2025
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Empower Financial Services Developers with the Document Model

In financial services , having a proper data modeling strategy is crucial. The volume of data that banks collect and create is continuously growing, partly due to the expansion of digital banking and payments. Financial institutions rely heavily on data to power applications, analyze risks, and make decisions. The way data is modeled can significantly impact the performance, scalability, and accuracy of these systems, as well as the speed of development for new financial products or services. According to Celent, 62% of banks say the “competitive threat from fintechs and other challengers is increasing.” This is no surprise, as the convenience of managing all financial affairs in one centralized app or on a digital-first platform is pulling more and more customers away from traditional financial institutions. In this blog, we will explore why 24% of retail banks ranked “data platforms and management” as one of their top three IT spending priorities for 2024–2025 and how data modeling plays a critical role in this strategic focus. Data modeling and the document model In financial services, data modeling is a critical process that underpins effective data management, enabling institutions to harness the full potential of their data. This process involves identifying relevant financial data and determining how it should be visualized, including its structure, relationships, and management. A well-structured data model leads to better application performance, simpler implementations, and lower costs. It also enhances adaptability and maintainability over time. The principle that data accessed together should be stored together is fundamental for optimizing performance. MongoDB is a document-oriented NoSQL database that offers flexibility and scalability, making it an ideal choice for the industry. It runs with unparalleled reliability, security, and flexibility in a multi-cloud or on-premises environment. Unlike traditional relational databases, MongoDB allows for a more dynamic schema, enabling organizations to adapt to changing business needs without the constraints of rigid table structures. Document databases have the following key features: Document model: Data is stored in documents (unlike other databases that store data in structures like tables or graphs). The documents map to objects in the most popular programming languages, enabling developers to rapidly develop their applications. Flexible schema: MongoDB’s design enables developers to store data in a way that reflects the real-world relationships between entities, making it easier to manage complex data structures. Data distribution and resilience: Document databases are distributed, which allows for horizontal scaling (typically cheaper than vertical scaling) and data distribution. Queryability through an API or query language: Document databases have an API or query language that allows developers to execute the CRUD operations on the database. Developers have the ability to query for documents based on unique identifiers or field values. MongoDB’s document model enables an organization to design document structures that mirror its application’s access patterns. By embedding related data as subdocuments and arrays within a single document, it can make sure that data that is frequently accessed together is stored together. This alignment simplifies the mapping between the application and the database, enhancing both development efficiency and performance. In contrast to relational databases, document databases are better suited to the needs of modern applications because of their ability to store diverse data types (both structured and unstructured) in binary JSON (BSON) format. This flexibility essentially eliminates the middle layer necessary to convert to a SQL-like format, resulting in easier-to-maintain applications, lower development times, and faster responses to changes. As a document’s schema is dynamic and self-describing, developers don’t need to predefine it in the database. They can modify it at any time, avoiding disruptive schema migrations and improving their own productivity and experience. Watch now: Intro to Data Modeling for Financial Services and Insurance explains: The definition of data modeling Relational database management systems vs MongoDB Relationships: Linking vs Embedding Design Patterns MongoDB’s document model for financial services Leading financial institutions across the world are increasingly adopting MongoDB with seven of the top ten banks in the world (per the 2024 Forbes Global 2000) utilizing MongoDB in their application architectures. MongoDB Atlas is an integrated suite of data services centered on a document database designed to accelerate and simplify how users build with data. MongoDB enables banks to employ an iterative approach to banking modernization . This approach preserves legacy components for as long as they’re needed. By deploying MongoDB as an operational data layer (ODL) in a phased approach, banks can embark on their digital transformation journeys iteratively, without the risk of an all-or-nothing, rip-and-replace approach. Once the new architecture is in place, development teams can build new business functionality faster and scale new services to millions of users. Here are some of the ways that MongoDB helps financial institutions meet their industry data challenges: Open finance: MongoDB’s flexible schema, with the ability to handle various data types including structured and unstructured data, seamlessly integrates with modern technologies and frameworks, making it a great fit for orchestrating open API ecosystems. Personalized banking experiences: Retail banks aim to deliver hyper-personalized services, such as tailored financial advice or customized product recommendations. A flexible and accurate document model ensures platforms can integrate data from multiple channels (mobile apps, branches, and ATMs) for a seamless experience. Encryption and access control: MongoDB provides security capabilities like field-level encryption, role-based access control (RBAC), and auditing—key features to enhance data security and facilitate compliance with regulations like Financial Data Access (FiDA). By encrypting sensitive customer data both at rest and in transit, MongoDB helps keep data secure and tamper-proof throughout its lifecycle. Data sovereignty and global deployment: Financial data must adhere to strict geographic regulations, with certain jurisdictions enforcing the requirement that data remain within specific regions. MongoDB Atlas offers multi-cloud and multi-region deployments, enabling financial institutions to store data in specific regions while maintaining compliance with data sovereignty laws. Multidocument ACID transactions: Transactions in MongoDB feel just like transactions developers are familiar with in relational databases. With multi-document atomicity, consistency, isolation, and durability (ACID) transactions, developers can address transactional use cases across multiple documents within the same cluster. Payment system scalability and flexibility: MongoDB’s document database excels in unifying an organization’s data, from backend payment processing to customer interactions, surfacing insights to create a seamless, connected, and personalized customer journey. Payment systems must accommodate fluctuating transaction volumes and evolving business needs. MongoDB Atlas makes scaling as easy as setting the right configuration. It supports both horizontal and vertical scaling. Fraud detection: MongoDB’s document data model incorporates any kind of data—any structure, any format, any source—no matter how often it changes, enabling an organization to create a holistic picture of its customers to better predict transaction anomalies in real-time. MongoDB then enables it to process large amounts of data and analyze it in real-time to identify suspicious activity. Financial data management: MongoDB lets organizations capture and store financial and trade-related data together, respond to compliance and regulatory requests with confidence, and analyze pretrade communications to gain insights and detect errors. AI-driven interactive banking: MongoDB is designed to work seamlessly with leading AI frameworks, enabling banks to integrate and scale AI applications quickly and efficiently. MongoDB’s flexibility supports innovation by providing a scalable, developer-friendly environment that enables businesses to rapidly develop new financial services and products and scale to support millions of users. And, as a document-based database, MongoDB supports the flexible data modeling that is so crucial to the financial services industry. If you have any questions or would like to learn more about MongoDB and data modeling, feel free to check out the following resources: Intro to Data Modeling for Financial Services and Insurance Temenos Banking Cloud Scales to Record High Transactions with MongoDB Atlas and Microsoft Azure Our Solutions Library is where you can learn about different use cases for gen AI and other interesting topics that are applied to financial services and many other industries.

January 22, 2025