Luis Pazmino Diaz

4 results

MongoDB: Gateway to Open Finance and Financial Data Access

This is the second in a two-part series about open finance and the importance of a flexible data store to open finance innovation. Check out part one here! Open finance is reshaping the financial services industry, pushing traditional institutions to modernize with a data-driven approach. Consumers increasingly expect personalized experiences, making innovation key to customer retention and satisfaction. According to a number of studies 1 , there is an exponential increase of dynamic transformations in financial services, driven primarily by the impact of Banking-as-a-Service (BaaS), embedded banking services, and AI. All of these initiatives are mainly powered by API services intended for data sharing, and have become must-have technical capabilities for financial institutions. Open finance can also unlock massive opportunities for continuous innovation. As a result, financial institutions must provision themselves with the right tools and expertise to be fully aware of the potential risks and challenges of embarking on such a “data-driven” journey. Now, let’s dive deeper into an application of open finance with MongoDB. MongoDB as the open finance data store Integrating diverse financial data while ensuring its security, compliance, and scalability represents a series of considerable challenges for financial institutions. Bringing together data from a variety of backend systems entails a set of complex hurdles for financial ecosystem participants—banks, fintechs, and third-party providers (TPP). First, they need to be able to handle structured, semi-structured, and increasingly unstructured data types. Then, cybersecurity and regulatory compliance concerns must be addressed. What’s more, an increase in data-sharing scenarios can open up potential vulnerabilities, which lead to the risk of breach exposure and cyber-attacks (and, therefore, possible legal penalties and/or eventual reputational damage). Figure 1. The power of open finance. To implement open finance strategies, organizations must first determine the role they will play: whether they act as data holders, are in charge of sharing the data with TPP, or whether they will be data users, the ones able to provide enhanced financial capabilities to end-users. Then, they must choose the most suitable technology for the data management strategy—and this is where MongoDB comes in, functioning as the operational data store. Let’s explore how MongoDB can play a crucial role for both actors—data holders and data users—through an open finance functional prototype. Open finance in action: Aggregated financial view for banking users Figure 2 below shows a digital application from a fictional bank—Leafy Bank—that allows customers to aggregate all their bank accounts into a single platform. Figure 2. Architecture of MongoDB as the open finance data store. Four actors are involved in this scenario: a. Customer - User b. Data Users - Leafy Bank c. Data Holders - External Institution d. Open Finance Data Store - MongoDB Atlas Now let’s go through the steps from the customer experience. Step 1. Log in to the banking application Once logged in, the Leafy Bank digital banking application allows users to aggregate their external bank accounts. It is done behind the scenes, through a RESTFul API request that will usually interchange data in JSON format. For the Leafy Bank prototype, we are using MongoDB and FastAPI together, exposing and consuming RESTful APIs and therefore taking advantage of MongoDB Atlas’s high performance, scalability, and flexibility. Figure 3. Logging in to the banking application. Step 2. User authentication and authorization A crucial step to ensure security and compliance is user consent. End-users are responsible for granting access to their financial information (authorization). In our case, Leafy Bank emulates the OAuth 2.0 authentication. It generates the corresponding tokens for securing the service communication between participants. To achieve efficient interoperability without security issues, data holders must enable a secured technological “fence” for sharing data while preventing the operational risk of exposing core systems. Figure 4. User authorization. Step 3. Data exposure After the authorization has been granted, Leafy Bank will fetch the corresponding account data from the data custodian—external banks (in our fictional scenario, Green Bank or MongoDB Bank)—via APIs. Usually, participants expose customers’ financial data (accounts, transactions, and balances) through their exposed services in JSON format to ensure compatibility and seamless data exchange. Because MongoDB stores data in BSON, a superset of JSON , it provides a significant advantage by allowing seamless storage and retrieval of JSON-like data—making it an ideal backend for open finance. Figure 5. Data exposure. Step 4. Data fetching The retrieved financial data is then pushed into the open finance data store—in our case, in MongoDB Atlas—where it is centrally stored. Unlike rigid relational databases, MongoDB uses a flexible schema model, making it easy for financial institutions to aggregate diverse data structures from different sources, making it ideal for dynamic ecosystems and easy to adapt without costly migrations or downtime. Figure 6. Data fetching from data holder into MongoDB Atlas Data Store. Step 5. Data retrieval Now that the data has been aggregated in the operational data store (powered by MongoDB Atlas), Leafy Bank can leverage MongoDB Aggregation Pipelines for real-time data analysis and enrichment. To become “open finance” compliant, our Leafy Bank provides a holistic financial view and a global position accessible in a single application, thus improving individuals' experience with their finances. Furthermore, this set of features also benefits financial institutions. They can unveil useful insights for building unique services meant to enhance customers' financial well-being. Figure 7. Data retrieval from MongoDB Atlas Data Store. Step 6. Bank connected! In the end, customers can view all their finances in one place, while enabling banks to offer competitive, data-driven, tailored services. Figure 8. Displaying the bank connection in Leafy Bank. Demo in action Now, let’s combine these steps into a real-world demo application: Figure 9. Leafy Bank - MongoDB as the Open Finance Data Store. Advantages of MongoDB for open finance Open finance presents opportunities for all the ecosystem participants. On the one hand, bank customers can benefit from tailored experiences. For personal financial management, it can provide end-users central visibility of their bank accounts. And open finance can enable extended payment initiation services, financial product comparison, enhanced insurance premium assessments, more accurate loan and credit scoring, and more. From a technical standpoint, MongoDB can empower data holders, data users, and TPP to achieve open finance solutions. By offering a flexible schema , banks can adapt to open finance’s evolving requirements and regulatory changes while avoiding the complexity of rigid schemas, yet allowing a secure and manageable schema validation if required. Furthermore, a scalable ( vertical and horizontal ) and cloud-native ( multi-cloud ) platform like MongoDB can simplify data sharing in JSON format, as it has been widely adopted as the data interchange “defacto” format, making it ideal for open finance applications. Internally, MongoDB uses BSON, the binary representation of JSON, for efficient storage and data traversal. MongoDB’s rich extensions and connectors support a variety of frameworks to create RESTful API development. Besides FastAPI, there are libraries for Express.js (Node.js), Django (Python), Spring Boot (Java), and Flask (Python). The goal is to empower developers with an intuitive and easy-to-use data platform that boosts productivity and performance. Additionally, MongoDB offers key features like its aggregation pipeline , which is designed to process data more efficiently by simplifying complex transformations, real-time analytics, and detailed queries. Sophisticated aggregation capabilities from MongoDB allow financial institutions to improve their agility while maintaining their competitive edge, all by having data as their strategic advantage. Lastly, MongoDB provides financial institutions with critical built-in security controls, including encryption, role-based access controls (RBAC), and auditing. It seamlessly integrates with existing security protocols and compliance standards while enforcing privileged access controls and continuous monitoring to safeguard sensitive data, as detailed in the MongoDB Trust Center . Check out these additional resources to get started on your open finance journey with MongoDB: Read part-one of our series to discover why a flexible data store is vital for open finance innovation. Explore our GitHub repository for an in-depth guide on implementing this solution. Visit our solutions page to learn more about how MongoDB can support financial services.

April 1, 2025

Embracing Open Finance Innovation with MongoDB

The term "open finance" is increasingly a topic of discussion among banks, fintechs, and other financial services providers—and for good reason. Open finance, as the next stage of open banking, expands the scope of data sharing beyond traditional banking to include investments, insurance, pension funds, and more. To deliver these enhanced capabilities, financial service providers need a versatile and flexible data store that can seamlessly manage a wide array of financial data. MongoDB serves as an ideal solution, providing a unified data platform that empowers financial services providers to integrate various data sources, enabling real-time analytics, efficient data retrieval, and scalability. These capabilities are pivotal in enhancing customer experiences, providing users with a comprehensive view of their finances, and empowering them with greater visibility and control over their own data. By adopting MongoDB, financial services can seamlessly adapt to the growing demands of open finance and deliver innovative, data-driven solutions. Open finance's past and future As highlighted in a study conducted by the Cambridge Centre for Alternative Finance 1 , the terms 'open banking' and 'open finance' vary globally. Acknowledging these differences, we'll focus on the model displayed in Figure 1 due to its widespread adoption and relevance in our study. Figure 1. The three waves of innovation in financial services. The development of open finance started with open banking, which intended for banks to promote innovation by allowing customers to share their financial data with third-party service providers (TPP) and allow those TPP—fintech and techfin companies—to initiate transactions on their behalf solely in the context of payments. This proved to be an effective way to promote innovation and thus led to a broader spectrum of financial products adding loans, mortgages, savings, pensions, insurance, investments, and more. Leading to this new directive, commonly referred to as: open finance. If we take a step further—regardless of its final implementation—a third development called open data suggests sharing data beyond the traditional boundaries of the financial services industry (FSI), exponentially increasing the potential for financial services by moving into cross-sector offerings, positioning FSI as a horizontal industry rather than an independent vertical as it was previously known. Who and what plays a role in open finance? Among the different actors across open finance, the most important are: Consumers: End-users empowered to grant or revoke consent to share their data primarily through digital channels. Data holders: These are mainly financial services companies, and thereby consumer data custodians. They are responsible for controlling the data flow across the different third-party providers (TPPs). Data users: Data users are common third-party providers offering their services based on consumers’ data (upon request/consent). Connectivity providers: Trusted intermediaries that facilitate data flow, also known as TSPs in the EU and UK, and Account Aggregators in India. Regulatory authorities: Set standards, oversee processes, and may intervene in open finance implementation. They may vary according to the governance type. The interactions between all these different parties define the pillars for open finance functioning: Technology: Ensures secure data storage and the exposure-consumption of services. Standards: Establishes frameworks for data interchange schemas. Regulations and enforceability: Encompasses security policies and data access controls. Participation and trust: Enables traceability and reliability within a regulated ecosystem. Figure 2. High-level explanation of data sharing in open finance. Drivers behind open finance: Adoption, impact, and compliance Open finance seeks to stimulate innovation by promoting competition, safeguarding consumer privacy, and ensuring market stability—ultimately leading to economic growth. Additionally, it has the potential to provide financial institutions with greater access to data and better insights into consumers' preferences, allowing them to tailor their offerings and to enhance user experiences. This data sharing between the ecosystem’s participants requires a regulated set of rules to ensure data protection, security, and compliance according to each jurisdiction. As seen in Figure 3 below, there are two broad drivers of open finance adoption: regulation-led and market-driven adoption. Whether organizations adopt open finance depends on factors like market dynamics, digital readiness, and regulatory environment. Figure 3. An illustrative example of open finance ecosystem maturity. Even though there is not one single, official legal framework specifying how to comply with open finance, countries around the world have crafted their own specific set of norms as guiding principles. Recent market research reports reveal how several countries are already implementing open finance solutions, each coming from different starting points, with their own economic goals and policy objectives. In Europe, the Revised Payment Services Directive (PSD2) combined with the General Data Protection Regulation (GDPR) form the cornerstone of the regulatory framework. The European Commission published a proposal in June 2023 for a regulation on a framework for Financial Data Access 2 (FiDA) set to go live in 2027. 3 In the UK, open finance emerged from the need to address the market power held by a few dominant banks. In India, open finance emerged as a solution to promote financial inclusion by enabling identity verification for accounts opening through the national ID system. The aim is to create a single European data space – a genuine single market for data, open to data from across the world – where personal as well as non-personal data, including sensitive business data, are secure and businesses also have easy access to an almost infinite amount of high-quality industrial data, boosting growth and creating value, while minimising the human carbon and environmental footprint. 4 Build vs. buy: Choosing the right open finance strategy One of the biggest strategic decisions financial institutions face is whether to build their own open finance solutions in-house or buy from third-party open finance service providers. Both approaches come with trade-offs: Building in-house provides full ownership, flexibility, and control over security and compliance. While it requires significant investment in infrastructure, talent, and ongoing maintenance, it ensures lower total cost of ownership (TCO) in the long run, avoids vendor lock-in, and offers complete traceability—reducing reliance on external providers and eliminating “black box” risks. Institutions that build their own solutions also benefit from customization to fit specific business needs and evolving regulations. Buying from a provider accelerates time to market and reduces development costs while ensuring compliance with industry standards. However, it introduces potential challenges such as vendor lock-in, limited customization, and integration complexities with existing systems. For financial institutions that prioritize long-term cost efficiency, compliance control, and adaptability, the building approach offers a strategic advantage—though it comes with its own set of challenges. What are the challenges and why do they matter? As open finance continues to evolve, it brings significant opportunities for innovation—but also introduces key challenges that financial institutions and fintech companies must navigate. These challenges impact efficiency, security, and compliance, ultimately influencing how quickly new financial products and services can reach the market. 1. Integration of data from various sources Open finance relies on aggregating data from multiple institutions, each with different systems, APIs, and data formats. This complexity leads to operational inefficiencies, increased latency, and higher costs associated with data processing and infrastructure maintenance. Without seamless integration, financial services struggle to provide real-time insights and a frictionless user experience. 2. Diverse data types Financial data comes in various formats—structured, semi-structured, and unstructured—which creates integration challenges. Many legacy systems operate with rigid schemas that don’t adapt well to evolving data needs, making it difficult to manage new financial products, regulations, and customer demands. Without flexible data structures, innovation is slowed, and interoperability between systems becomes a persistent issue. 3. Data security With open finance, vast amounts of sensitive customer data are shared across multiple platforms, increasing the risk of breaches and cyberattacks. A single vulnerability in the ecosystem can lead to data leaks, fraud, and identity theft, eroding customer trust. Security vulnerabilities have financial consequences and can result in legal examination and long-term reputational damage. 4. Regulatory compliance Navigating a complex and evolving regulatory landscape is a major challenge for open finance players. Compliance with data protection laws, financial regulations, and industry standards—such as GDPR or PSD2—requires constant updates to systems and processes. Failure to comply can lead to legal penalties, substantial fines, and loss of credibility—making it difficult for institutions to operate confidently in a global financial ecosystem. These challenges directly impact the ability of financial institutions to innovate and launch new products quickly. Integration issues, security concerns, and regulatory complexities contribute to longer development cycles, operational inefficiencies, and increased costs—ultimately slowing the time to market for new financial services. In a highly competitive industry where speed and adaptability are critical, overcoming these challenges is essential for success in open finance. MongoDB as the open finance data store To overcome open finance’s challenges, a flexible, scalable, secure, and high-performing data store is required. MongoDB is an ideal solution, as it offers a modern, developer-friendly data platform that accelerates innovation while meeting the critical demands of financial applications. Seamless integration with RESTful JSON APIs According to OpenID’s 2022 research , most open finance ecosystems adopt RESTful JSON APIs as the standard for data exchange, ensuring interoperability across financial institutions, third-party providers, and regulatory bodies. MongoDB’s document-based model natively supports JSON, making it the perfect backend for open banking APIs. This enables financial institutions to ingest, store, and process API data efficiently while ensuring compatibility with existing and emerging industry standards. Flexible data model for seamless integration Open finance relies on diverse data types from multiple sources, each with different schemas. Traditional relational databases require rigid schema migrations, often causing downtime and disrupting high-availability services. MongoDB's document-based model—with its flexible schema—offers an easy, intuitive, and developer-friendly solution that eliminates bottlenecks, allowing financial institutions to adapt data structures dynamically, all without costly migrations or downtime. This ensures seamless integration of structured, semi-structured, and unstructured data, increasing productivity and performance while being cost-effective, enables faster iteration, reduced complexity, and continuous scalability. Enterprise-grade security and compliance Security and compliance are non-negotiable requirements in open finance, where financial data must be protected against breaches and unauthorized access. MongoDB provides built-in security controls, including encryption, role-based access controls, and auditing. It seamlessly integrates with existing security protocols and compliance standards, ensuring adherence to regulations such as GDPR and PSD2. MongoDB also enforces privileged access controls and continuous monitoring to safeguard sensitive data, as outlined in the MongoDB Trust Center . Reliability and transactional consistency Financial applications demand zero downtime and high availability, especially when processing transactions and real-time financial data. MongoDB’s replica sets ensure continuous availability, while its support for ACID transactions guarantees data integrity and consistency—critical for handling sensitive financial operations such as payments, lending, and regulatory reporting. The future of open finance The evolution of open finance is reshaping the financial industry, enabling seamless data-sharing while introducing new challenges in security, compliance, and interoperability. As financial institutions, fintechs, and regulators navigate this shift, the focus remains on balancing innovation with risk management to build a more inclusive and efficient financial ecosystem. For organizations looking to stay ahead in this landscape, choosing the right technology stack is crucial. MongoDB provides the flexibility, scalability, and security needed to power the next generation of open finance applications—helping financial institutions accelerate innovation while ensuring compliance and data integrity. In Part 2 of our look at open finance, we’ll explore a demo from the Industry Solutions team that leverages MongoDB to implement an open finance strategy that enhances customer experience, streamlines operations, and drives financial accessibility. Stay tuned! Head over to our GitHub repo to view the demo. Visit our solutions page to learn more about how MongoDB can support financial services. 1 CCAF, The Global State of Open Banking and Open Finance (Cambridge: Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge, 2024). 2 “The Financial Data Access (FiDA) Regulation,” financial-data-access.com, 2024, https://www.financial-data-access.com/ 3 Maout, Thierry, “What is Financial Data Access (FiDA), and how to get ready?”, July 16th, 2024, https://www.didomi.io/blog/financial-data-access-fida?315c2b35_page=2 4 European Commission (2020), COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS, EUR-Lex.

March 25, 2025

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. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. 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. Key Features of RegData Protection Suite: Core Configuration: Provides services and a user interface to configure the protection of data. 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 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 RPS Manager: Provides end-to-end monitoring capabilities for the components of the RPS platform. 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. 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: RegData & MongoDB: Securing Digital Transformation Streamline Data Control and Compliance with RegData & MongoDB Implementing an Operational Data Layer Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.

January 23, 2025

Better Digital Banking Experiences with AI and MongoDB

Interactive banking represents a new era in financial services where customers engage with digital platforms that anticipate, understand, and meet their needs in real-time. This approach encompasses AI-driven technologies such as chatbots, virtual assistants, and predictive analytics that allow banks to enhance digital self-service while delivering personalized, context-aware interactions. According to Accenture’s 2023 consumer banking study , 44% of consumers aged 18-44 reported difficulty accessing human support when needed, underscoring the demand for more responsive digital solutions that help bridge this gap between customers and financial services. Generative AI technologies like chatbots and virtual assistants can fill this need by instantly addressing inquiries, providing tailored financial advice, and anticipating future needs. This shift has tremendous growth potential; the global chatbot market is expected to grow at a CAGR of 23.3% from 2023 to 2030 , with the financial sector experiencing the fastest growth rate of 24.0%. This shift is more than just a convenience; it aims to create a smarter, more engaging, and intuitive banking journey for every user. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. Simplifying self-service banking with AI Navigating daily banking activities like transfers, payments, and withdrawals can often raise immediate questions for customers: “Can I overdraft my account?” “What will the penalties be?” or “How can I avoid these fees?” While the answers usually lie within the bank’s terms and conditions, these documents are often dense, complex, and overwhelming for the average user. At the same time, customers value their independence and want to handle their banking needs through self-service channels, but wading through extensive fine print isn't what they signed up for. By integrating AI-driven advisors into the digital banking experience, banks can provide a seamless, in-app solution that delivers instant, relevant answers. This removes the need for customers to leave the app to sift through pages of bank documentation in search of answers, or worse, endure the inconvenience of calling customer service. The result is a smoother and user-friendly interaction, where customers feel supported in their self-service journey, free from the frustration of navigating traditional, cumbersome information sources. The entire experience remains within the application, enhancing convenience and efficiency. Solution overview This AI-driven solution enhances the self-service experience in digital banking by applying Retrieval-Augmented Generation (RAG) principles, which combine the power of generative AI with reliable information retrieval, ensuring that the chatbot provides accurate, contextually relevant responses. The approach begins by processing dense, text-heavy documents, like terms and conditions, often the source of customer inquiries. These documents are divided into smaller, manageable chunks vectorized to create searchable data representations. Storing these vectorized chunks in MongoDB Atlas allows for efficient querying using MongoDB Atlas Vector Search , making it possible to instantly retrieve relevant information based on the customer’s question. Figure 1: Detailed solution architecture When a customer inputs a question in the banking app, the system quickly identifies and retrieves the most relevant chunks using semantic search. The AI then uses this information to generate clear, contextually relevant answers within the app, enabling a smooth, frustration-free experience without requiring customers to sift through dense documents or contact support. Figure 2: Leafy Bank mock-up chatbot in action How MongoDB supports AI-driven banking solutions MongoDB offers unique capabilities that empower financial institutions to build and scale AI-driven applications. Unified data model for flexibility: MongoDB’s flexible document model unifies structured and unstructured data, creating a consistent dataset that enhances the AI’s ability to understand and respond to complex queries. This model enables financial institutions to store and manage customer data, transaction history, and document content within a single system, streamlining interactions and making AI responses more contextually relevant. Vector search for enhanced querying: MongoDB Atlas Vector Search makes it easy to perform semantic searches on vectorized document chunks, quickly retrieving the most relevant information to answer user questions. This capability allows the AI to find precise answers within dense documents, enhancing the self-service experience for customers. Scalable integration with AI models: MongoDB is designed to work seamlessly with leading AI frameworks, allowing banks to integrate and scale AI applications quickly and efficiently. By aligning MongoDB Atlas with cloud-based LLM providers, banks can use the best tools available to interpret and respond to customer queries accurately, meeting demand with responsive, real-time answers. High performance and cost efficiency: MongoDB’s multi-cloud, modern database allows financial institutions to innovate without costly infrastructure changes. It’s built to scale as data and AI needs to grow, ensuring banks can continually improve the customer experience with minimal disruptions. MongoDB’s built-in scalability allows banks to expand their AI capabilities effortlessly, offering a future-proof foundation for digital banking. Building future-proof applications Implementing generative AI presents several advantages, not only for end-users of the interactive banking applications but also for financial institutions: Enhanced user experience encourages customer satisfaction, ensures retention, boosts reputation, and reduces customer turnover while unlocking new opportunities for cross-selling and up-selling to increase revenue, drive growth and elevate customer value. Moreover, adopting AI-driven initiatives prepares the groundwork for businesses to develop innovative, creative, and future-proof applications to address customer needs and upgrade business applications with features that are shaping the industry and will continue to do so, here are some examples: Summarize and categorize transactional information by powering applications with MongoDB’s Real-Time Analytics . Understand and find trends based on customer behavior that could positively impact and leverage fraud prevention , anti-money laundering (AML) , and credit card application (just to mention a few). Offering investing, budgeting, and loan assessments through AI-powered conversational banking experience. In today’s data-driven world, companies face increasing pressure to stay ahead of rapid technological advancements and ever-evolving customer demands. Now more than ever, businesses must deliver intuitive, robust, and high-performing services through their applications to remain competitive and meet user expectations. Luckily, MongoDB provides businesses with comprehensive reference architectures for building generative AI applications, an end-to-end technology stack that includes integrations with leading technology providers, professional services, and a coordinated support system through the MongoDB AI Applications Program (MAAP) . By building AI-enriched applications with the leading multi-cloud modern database, companies can leverage low-cost, efficient solutions through MongoDB’s flexible and scalable document model which empowers businesses to unify real-time, operational, unstructured, and AI-related data, extending and customizing their applications to seize upcoming technological opportunities. Check out these additional resources to get started on your AI journey with MongoDB: How Leading Industries are Transforming with AI and MongoDB Atlas - E-book 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. Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads ? ” presented by MongoDB Field CTO, Rick Houlihan.

November 26, 2024