Mastering Regulatory Compliance: MongoDB Developer Data Platform for Fintech Applications
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Designing Data Models For Regulatory Compliance In Fintech Applications Using MongoDB

Achieve flawless regulatory compliance in your fintech applications using MongoDB Developer Data Platform.


In today’s world of regulatory compliance, fintech applications must adhere to certain rules and regulations to ensure that they protect consumer data and maintain financial integrity. One of the key aspects of building fintech applications is using a powerful and scalable database, and MongoDB developer data platform is one of those databases that can easily handle complex financial data. 

In this article, we will look at how MongoDB can be used to design data models that adhere to regulatory compliance in fintech applications. If you are looking to hire MongoDB developers to build web applications, set up an appointment with us to hire vetted engineers now.

Understanding Regulatory Compliance in FinTech

Financial regulations are rules that financial institutions must follow to ensure fair and transparent consumer dealings. In the United States, financial regulators set the standards for financial institutions to comply with, including the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), and the Federal Reserve, among others.

The SEC has specific rules (Reg S-P) surrounding using, storing, and sharing of personal financial information. Financial institutions that comply with these rules must maintain records of customer information and implement policies and procedures to protect the confidentiality and security of this information.

In the case of fintech companies, regulations can vary depending on the type of financial activity they engage in. Some fintech companies may be subject to anti-money laundering rules (AML), while others must follow Know Your Customer (KYC) regulations. Securities and Exchange Commission (SEC) rules come into play regarding crowdfunding or peer-to-peer lending platforms. Whatever the regulations, a powerful and secure database becomes crucial.

What Compliance Regulations Risks Do Fintech Solutions Face?

Fintech solutions face several compliance regulations risks, including:

  • Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance – Fintech companies must comply with AML and KYC regulations to prevent money laundering and terrorist financing. Failure to comply can result in hefty fines and reputational damage.

In early 2023, Coinbase was fined $100m by DFS for failing to adhere to appropriate compliance standards.

  • Data privacy and security – Fintech companies collect and store sensitive customer data, making them a target for cyber attacks. Compliance with privacy regulations such as GDPR and CCPA is critical to avoid penalties and data breaches.

A recent data breach involving fintech company Revolut saw the personal information of over 50,000 individuals being accessed by an “unauthorized third party”.

  • Payment processing regulations – Fintech companies involved in payment processing, such as mobile payments and digital wallets, must comply with PSD2 and PCI DSS regulations.
  • Consumer protection regulations – Fintech companies are subject to consumer protection regulations, including the Fair Credit Reporting Act, Truth in Lending Act, and Electronic Funds Transfer Act. Any violation of these regulations can result in legal action and reputational damage.
  • Regulation of the financial services industry – Fintech companies may be subject to regulations such as the Securities Act, Investment Company Act, and Investment Advisers Act if they offer investment or advisory services.

It is, therefore, important for fintech companies to stay up-to-date with the latest compliance regulations to avoid risks and ensure continued success.

Why MongoDB Is a Good Option for Financial Compliance

MongoDB is a popular NoSQL database that handles large volumes of unstructured data. Compared to traditional SQL databases that require complex queries and data structures, MongoDB makes it easy to store large amounts of data and retrieve it with a minimum of complexity.

It is an ideal database for fintech applications because of its flexible data model, robust query language, and rich indexing capabilities. MongoDB is designed to handle unstructured and semi-structured data, which makes it a perfect fit for fintech applications that deal with complex financial data.

MongoDB is also designed to scale horizontally, which means it can easily support large-scale financial applications. This makes it a popular choice for fintech companies that need to store and process large amounts of financial data quickly and efficiently.

Designing a Data Model for Regulatory Compliance in Fintech Applications

When designing a data model for regulatory compliance in fintech applications, some key principles must be remembered. One is that the model should be flexible and scalable, which is where MongoDB comes in. You must also define the data structure and relationships required based on the regulatory compliance rules.

Here are some steps to follow when designing a MongoDB data model for regulatory compliance in fintech applications:

  • Understand the Regulatory Compliance Requirements

The first step in designing a data model for regulatory compliance in fintech applications is understanding the specific regulatory requirements. This includes understanding the types of data that need to be stored, the time it needs to be stored, and how it needs to be protected.

In the case of KYC regulations, you would need to store customer information such as name, address, and date of birth. You would also need to keep additional information, such as proof of identity and proof of address. Additionally, the model must define relationships between accounts, transactions, and customers.

In the case of AML rules, you would need to store information such as the origin of funds and transaction patterns to identify any suspicious activity. The data model must provide for the relationship of entities, such as individuals, organizations, accounts, and transactions.

  • Understand Data Sources

Once you have identified the regulatory requirements, the next step is understanding the data sources your fintech application will use. Fintech applications typically deal with various data sources such as transaction, user, market, and reference data. Understanding these data sources is critical to designing an effective data model that can handle the complexity of these data sources.

  • Define Entities and Relationships

The next step is to define the entities and the relationships between those entities. Entities can be people, accounts, transactions, credit ratings, etc. Define how each entity relates to others in the system. Each entity will have specific attributes or data points that must be collected and stored.

For example, transactions could be created from an account tied to a person. So, the account entity should have a relationship key to link the account to the person. There should also be a relationship between transactions and the account or person they belong to.

  • Determine Database Requirements

The next step is determining the database requirements based on the predefined business requirements. Data points collected and stored will dictate what data types are used. For example, MongoDB can keep, as BSON types, String, Number and Boolean, as well as arrays, dates, and embedded documents. Additional BSON data types can be used to customize data types to a specific application.

Consider data security requirements when designing the schema. Everything must be encrypted, including the fields shared over Enterprise Service Bus (ESB). Data access limitations should be defined, such as what user roles have access to what data.

  • Define indexes

The final step is to define indexes that will be used to optimize queries and improve performance. MongoDB supports a wide range of index types, including single-field indexes, compound indexes, text indexes, and geospatial indexes. Defining indexes is critical to ensure that your fintech application can handle large volumes of data and provide fast query response times.

Best Practices For Designing Data Models For Regulatory Compliance

Designing data models for regulatory compliance can be complex, but following some best practices can help ensure that the models meet regulatory requirements. Here are some best practices for designing data models for regulatory compliance:

  • Involve Compliance Professionals: It is crucial to involve compliance professionals in the design process because they have the expertise to identify the applicable regulations and the requirements for the data models.
  • Understand the Data Sources: Understanding the sources of the data is a critical step in designing data models. Data sources must be identified, and the data must be extracted, transformed, and cleansed to ensure accuracy.
  • Establish Data Governance: Data governance is the process of managing the availability, usability, integrity, and security of data used in an organization. A robust data governance framework can ensure that data models comply with regulations while being efficient and effective.
  • Identify Granular Data Elements: Identifying and documenting the most granular data elements relevant to regulatory compliance requirements is important. This level of detail helps capture all necessary data elements, reducing the risk of non-compliance.
  • Use Data Classification: Data classification organizes data into categories to ensure it is handled appropriately according to its sensitivity. Regulatory compliance requirements may dictate data classification, so data models should be developed to manage these categories.
  • Ensure Data Accuracy and Completeness: Data accuracy and completeness are essential in regulatory compliance. Data models must be designed to store and process the data accurately and completely, including timestamps and metadata.

By following these practices, organizations can develop data models that meet regulatory requirements and ensure they can provide reliable, accurate, and timely information to regulators.

A Cost-Comparison Report Between MongoDB And Traditional Relational Databases

Comparing the costs of MongoDB and traditional relational databases (SQL) depends on several factors, including:

  • Licensing Costs: Relational databases such as Oracle, Microsoft SQL, and IBM DB2 require licenses to be purchased, and additional licenses may be required for various features. These licenses may come with high upfront costs or ongoing subscription fees. MongoDB, on the other hand, is open-source, with additional enterprise features available through a paid subscription. Therefore, it may be more cost-effective than traditional SQL databases.
  • Hardware Costs: MongoDB runs well on commodity hardware, requiring fewer hardware resources than a comparable SQL database. This can translate to lower hardware costs, especially for businesses with large data volumes.
  • Scalability: MongoDB’s architecture is designed for horizontal scaling using sharding, which means that additional resources can be added with lower incremental costs. In contrast, traditional SQL databases may require expensive upgrades to handle additional concurrent users or large data volumes.
  • Administration and Maintenance Costs: MongoDB requires fewer resources for administration and maintenance as it has fewer legacy components and is easier to deploy, monitor, and upgrade. Traditional SQL databases can be more challenging to manage due to their complexity and the need for additional administrative resources.
  • Training and Support Costs: Many developers are familiar with SQL databases from years of industry use, but there may still be a learning curve when transitioning to using the NoSQL MongoDB database. Depending on how much work is required to implement it in the business correctly, additional training and support can add to the cost of using MongoDB.

How The Use Of MongoDB Can Significantly Modify Your Existing Environments?

MongoDB is a powerful database that can significantly modify existing IT environments. It provides a document-oriented data model, which means it stores data in collections of documents instead of using the traditional relational database structure.

This makes MongoDB highly scalable and flexible, allowing developers to quickly and easily create applications without worrying about complex database structures. Additionally, MongoDB’s built-in replication capabilities ensure high availability and reliability, so you can rest assured that your data will always be available when needed.

Finally, MongoDB’s intuitive query language makes it easy to access and manipulate data stored in the database. This makes developing applications that require complex queries or analytics operations simple. With MongoDB, you can quickly build sophisticated applications tailored to your specific needs without worrying about the complexities of traditional databases.

MongoDB is an incredibly useful tool that can improve your workflow and databases by providing scalability, flexibility, reliability and ease of use.


Banks and fintech have already succeeded in directly addressing areas where the bank is deficient without resorting to a platform switch. Long-term success is likely achieved by adopting a flexible core payment and banking platform and ecosystem that allows for frequent and rapid integration of new technologies.

The financial services industry is going through an exciting period as banks and fintech companies continue searching for partners to help them achieve their strategic goals.

MongoDB appears more cost-effective than traditional SQL databases, especially for businesses with large volumes of data that require horizontal scaling. This is due to lower hardware costs, fewer administration and maintenance requirements, and zero or lower licensing costs, among other reasons.

However, these cost comparisons will vary based on the specific requirements and circumstances of the business or project. Therefore, companies must conduct a detailed cost-benefit analysis when selecting a database and determine which is more cost-effective for their specific use case. If you are unsure as to how these variables should be assessed for your business, book a quick call with us to discuss your needs.

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