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MongoDB for Financial Services

Build better customer experiences at a lower cost and at scale, on premises or in the cloud.
Are you using AI to unlock opportunities in payments?
Are you using AI to unlock opportunities in payments?
Don't miss this live webinar with speakers from industry analyst Celent and payments solutions provider Icon Solutions on:
  • Emerging use cases for AI with insights from Celent’s latest AI payments report
  • AI use cases in back, middle, and front office operations to support innovation and developer productivity
  • Where to start when looking to leverage AI technologies, in particular generative AI
March 6 | 10 a.m. GT
March 7 | 1 p.m. ET
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Build better customer experiences

Modernize legacy infrastructure

Modernize legacy infrastructure

De-risk the move from legacy, relational databases and mainframes to build better customer experiences, easily scale to millions of users, and significantly reduce costs.Modernize legacy applications
Protect customer information

Protect customer information

Whether on-premises or in the cloud, protect customer information with industry-leading encryption, access controls, and data protection protocols to integrate with your existing security practices and processes.Learn how MongoDB is secure by default
Achieve high availability and scalability

Achieve high availability and scalability

Scale effortlessly, perform flawlessly, and deliver the high availability today’s applications demand.Learn how Bendigo and Adelaide Bank simplified their architecture

Solutions for Finance

Build a competitive advantage with MongoDB
industry_finance

Core Banking

Core banking system vendors — like Temenos and banks with in-house core banking systems — use MongoDB to modernize their critical infrastructure.

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Payments

Create an enriched payments experience by consolidating, ingesting, and acting on payments data instantly, delivering value-added services and features.

general_features_management

Open Banking

Embrace existing Open Banking standards and future-proof your bank with a flexible data model ready for constantly changing API standards.

general_action_locked

Fraud Prevention

Analyze and detect fraud in real time and satisfy Know Your Customer (KYC) requirements by unifying your data from across the business.

general_content_users

Lending and Leasing

The data platform enabling faster innovation, automated workflows, and quicker time to value for new products to meet the needs of customers.

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ESG

Create a single view of all your ESG (Environmental, Social, and Governance) data and act on it in real time. With MongoDB’s flexible data model, you're also ready for changing ESG requirements.


Industry Accelerator Program

Learn about our mission-critical solution accelerators – from industry innovation days, access passes, and jumpstart programs – that drive industry innovation.
Learn more
NEXT GENERATION CARD PAYMENTS
“MongoDB handles more than 20 terabytes of data per day and supports over 80 microservices. We’re currently using the ODS to run our rewards and loyalty programs.”
Nadeem Kayani
EVP/CIO of Consumer Lending, Wells Fargo
Read the whole story
DEVELOPER DATA PLATFORM
“We found that MongoDB Atlas had a great way of structuring data that was so simple and easy to use for our developers.”
Rob Jackson
Head of Application Architecture, Nationwide (UK)
PAYMENTS PLATFORM
“We went live 2 months into the COVID pandemic. A flick of the switch and payments started flowing. We’ve had over 3 million transactions now. No issues, it just simply worked.”
Chris Clark
Principal Engineer, Macquarie Bank
SECURITY
“MongoDB ticked all the boxes with our governance and security crews.”
Dan Corboy
Cloud Engineer at Bendigo and Adelaide Bank

Industry Partnerships

MongoDB’s robust partner ecosystem goes beyond the major cloud providers, with global industry organizations integrating MongoDB into their financial services solutions.
Find the right partner for you
Temenos logo image

Temenos, a global core banking vendor, partners with MongoDB for their core banking system, Temenos Transact, which runs on MongoDB's developer data platform, to improve performance and scalability.

Featurespace logo image

Stay one step ahead of fraud with Featurespace’s world-leading, real-time machine learning ARIC™ Risk Hub that prevents fraud and financial crime, running on MongoDB.

BigID logo image

BigID, an industry leader for data security, privacy, compliance, and governance, integrates with MongoDB to generate data insights at scale, automate advanced discovery & classification, and more.

TRUSTED BY
Goldman Sachs
Nationwide (UK)
coinbase
Department for Work and Pensions
Freddie Mac

Learn more

Get valuable insights on how to leverage MongoDB for Financial Services.
View resources
Forrester Report: Next-gen data platforms in financial services
Forrester Report: Next-gen data platforms in financial services
Learn how IT decision-makers at global financial services firms plan on using next-generation data platforms.
Download the Report
MongoDB for Financial Services

MongoDB for Financial Services

Speak to one of our expertsContact Us

FAQ

Frequently asked questions about banking databases
How are databases used in banking?

Traditional relational databases have been a mainstay of financial services companies and their IT infrastructure for decades.

From generating bank statements to storing customer info, banks have traditionally relied on — and been limited by — a reliance on the relational database, SQL server, and other major RDBMS.But the digital economy demands more from a bank and its database system.

Today, a bank database has to be of a distributed nature with the ability to store data locally and in the cloud, handle a huge amount of varied sales information, customer information, debit transactions, multiple processors and more.

That’s why banks turn to NoSQL databases, like MongoDB.

Why do banks use relational databases?

The technology underlying the relational databases in use at many traditional banks was first developed in the 1970s.

Conceived long before the cloud computing era, they were never intended to support the volume, variety, or velocity of data hitting them today. They have not evolved to address the needs of always-on, globally-distributed deployments, and they also are not sufficiently agile to keep pace with modern digital product development and release cycles.

As a result, established banks have struggled to offer the frictionless and personalized digital experiences of fintech startups.

The business implications are sobering. In a survey of banking executives in the loan origination business, conducted by Fintech Futures and MongoDB, 43% of respondents said a poor digital experience was their primary challenge in acquiring and retaining customers, while 34% cited a lack of personalized offerings.

What is single view in banking?

Traditionally, multiple database systems (typically SQL databases like SQL server and other major RDBMS systems) and legacy architectures have created silos that make it impossible to derive true value from data.

A single view of your banking data— especially a customer's account data — across the enterprise, can provide a firmwide view of asset and counterparty exposure or a single view of your customer for fraud detection and Know Your Customer (KYC) requirements.

What is a database management system (DBMS)?

Database Management Systems (DBMSs) do the critical work of defining how data is structured, accessed, altered, and protected.

A Database Management System (DBMS) is a software program that provides Application Programming Interfaces (APIs) to an underlying physical data store (stored on disk, or even in RAM) for use by client applications.

Any application (with rare exceptions) that stores, access, and manipulates data stored on disk, uses a DBMS to manage that access, rather than directly interacting with the underlying data files.

The DBMS has three core components:

  1. Data storage engine
  2. Query / Update engine
  3. Schema management system

We use database management systems to create centralized, shared, and consistent interfaces to programmatically access data. Database management systems help provide a logical structure to the data with which we’re working, along with efficient storage and distributed access. Using a DBMS is faster, more secure, more powerful, and easier than directly managing data yourself.

Why do banks need a developer data platform?

In recent years, IT vendors have been trying to develop and offer solutions to address the flood of data that companies face from both inside and outside the business.

Cloud is the new norm, and cloud-native data warehouses are now massively parallel-processed. Data pipelines can handle terabytes of data. Storage has become cheap and fast. AI/ML applications have proliferated everywhere. And data-processing frameworks, like Spark, can handle large volumes of data.

To work with these changes, banks need a cohesive, integrated suite of offerings capable of managing modern data requirements for building applications across even the most sprawling digital estates, without sacrificing speed, security, or the ability to scale. Integration also ensures that operations and security don’t become their own resource-draining Frankenstein projects.

A developer data platform is an integrated set of database and data services that solves these issues. By removing much of the overhead of managing a data infrastructure, a developer data platform can serve as a mission critical database and also help boost developer productivity and innovation.

What are the different types of database management systems?

In theory, there is no limit on the different types of management systems that it is possible to create, but there are a few popular DBMSs worth mentioning.

  • Relational (RDBMS)

    • Database management systems that structure data in table form, with predefined relationships between tables, and a Structured Query Language (SQL) for reading and writing data.

  • Document (DoDBMS)

    • Database management systems that structure data in JSON-like documents, with a document-oriented query language like MongoDB Query Language (MQL) for reading and writing portions or all of documents.

  • Columnar (CDBMS)

    • Database management systems that organize data by column, for certain high-performance and disk-heavy use cases.

In addition to DBMSs that are specialized by schema and query type, there are also database management systems specialized in different storage types, like In-Memory Database Management Systems (IMDBMSs).

There are also cloud database management systems where a SaaS provider is responsible for managing the regular database maintenance tasks (such as updates, etc). MongoDB Atlas (based on MongoDB’s NoSQL database) is an example of this.

What is a distributed database management system?

A distributed database management system allows programmers and end-users to see a collection of physically separate databases and data as one system image.

Distributing your data across multiple databases gives more manageable scaling and can help with redundancy (depending on how you distribute your data).

MongoDB is the leader in a new generation of mission critical databases that are designed for scalability. With a technique called “sharding,” you are able to easily distribute data and grow your deployment over inexpensive hardware or in the cloud. One of the benefits of scaling with MongoDB is that sharding is automatic and built into the database. This relieves developers of having to build in sharding logic into the application code to scale out the system.