Recent Developments in Document Databases

The financial industry is one of the fastest-moving sectors in the world. Global events, such as political elections and economic reports, can immediately affect forex markets.

On a personalized level, the industry is also evolving through the rapid growth of fintech. Today, digital banking and financial services are having a transformative effect in developed economies and regions where billions of people have previously struggled to access banking services. As more consumers use fintech to manage their finances, more data can be collected and used to improve the services offered by fintech companies.

Much of this data, such as documents and emails, is also unstructured (without a predefined format or structure) and requires non-traditional databases. Document databases are fast becoming widely used across all industries as one of the most efficient ways to store vast amounts of new data types. In this post, we will cover what a document database is and how the recent developments can be applied to the fintech industry.

What is a Document Database?

A document database (a document-oriented database or a document store) is a type of NoSQL database that stores and sorts information on documents. These documents are records that typically store information about one object and any of its related metadata in field value pairs. One of the key advantages of document databases is that the values can be of various types and structures, including strings, numbers, dates, arrays, or objects. The documents can be stored in formats like JSON, BSON, and XML, making them highly flexible as they can be mapped to objects in the most popular programming languages. This allows developers to develop their applications quickly. Below is an example of what a document looks like and how it holds multiple data points on one object.

{

“_id”: 1,

“first_name”: “Tom”,

“email”: “tom@example.com”,

“cell”: “765-555-5555”,

“likes”: [

“fashion”,

“spas”,

“shopping”

Recent Document Databases Developments

Document databases are continuously evolving. Below are three of the most recent developments and how they can be applied to the fintech industry.

Increased Security

Data security is a top priority in the fintech industry, and the database used must be secure from cyber threats. A January 2025 paper on NoSQL database security in fintech outlined that while the speed of NoSQL databases is a critical advantage for fintech companies, the corresponding high operational velocity complicates NoSQL database security. The paper notes how all NoSQL formats, including document databases, are increasing their security through secure NoSQL configuration, implementing strong authorization mechanisms, embracing data encryption, and restricting network access to the databases. NoSQL databases can also implement dynamic data masking solutions to prevent unauthorized users from viewing sensitive data.

Cost Efficiency

As data consumption increases year-on-year, so does the cost to store it. The costs involved in running an effective data management system include storage space, data volume, access speed, and security protocols. Modern document databases have developed to become one of the most cost-efficient types of databases. Traditional databases scale vertically, which means that hardware must be added to accommodate any expanded storage capabilities. In contrast, document databases scale horizontally and can easily expand by adding more servers, which can be distributed across a system. The flexible schema of a document database also makes it cost-effective, as fewer resources are devoted to data modeling and database restructuring. The fintech industry is extremely competitive, with the cost of running such operations being a primary factor in why so many companies are forced to close down. As document databases evolve and become more cost-efficient, more fintech companies will use them as their data management system.

Machine Learning Applications

As all industries embrace machine learning (ML), there has become an increasing demand to find databases that can help support these applications. Document databases have evolved in recent years to sort and store data from ML datasets due to their flexible schema, which allows them to store unstructured and semi-structured data. Document databases can effortlessly integrate with natural language processing (NPL) programs by storing unstructured text data in documents.

This enables them to support NPL for tasks like sentiment analysis, topic extraction, and text classification. The ability of a document database to scale horizontally allows it to handle the vast amounts of data produced in ML applications. In fintech, ML is mainly used for pattern identification, which includes algorithmic trading, fraud detection and prevention, and data analytics services. As more fintech companies leverage ML, the capability of document databases will become increasingly useful.

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