Wei You Pan
Dissecting Open Banking with MongoDB: Technical Challenges and Solutions
Thank you to Ainhoa Múgica for her contributions to this post. Unleashing a disruptive wave in the banking industry, open banking (or open finance), as the term indicates, has compelled financial institutions (banks, insurers, fintechs, corporates, and even government bodies) to embrace a new era of transparency, collaboration, and innovation. This paradigm shift requires banks to openly share customer data with third-party providers (TPPs), driving enhanced customer experiences and fostering the development of innovative fintech solutions by combining ‘best-of-breed’ products and services. As of 2020, 24.7 million individuals worldwide used open banking services, a number that is forecast to reach 132.2 million by 2024. This rising trend fuels competition, spurs innovation, and fosters partnerships between traditional banks and agile fintech companies. In this transformative landscape, MongoDB, a leading developer data platform, plays a vital role in supporting open banking by providing a secure, scalable, and flexible infrastructure for managing and protecting shared customer data. By harnessing the power of MongoDB's technology, financial institutions can lower costs, improve customer experiences, and mitigate the potential risks associated with the widespread sharing of customer data through strict regulatory compliance. Figure 1: An Example Open Banking Architecture The essence of open banking/finance is about leveraging common data exchange protocols to share financial data and services with 3rd parties. In this blog, we will dive into the technical challenges and solutions of open banking from a data and data services perspective and explore how MongoDB empowers financial institutions to overcome these obstacles and unlock the full potential of this open ecosystem. Dynamic environments and standards As open banking standards continue to evolve, financial institutions must remain adaptable to meet changing regulations and industry demands. Traditional relational databases often struggle to keep pace with the dynamic requirements of open banking due to their rigid schemas that are difficult to change and manage over time. In countries without standardized open banking frameworks, banks and third-party providers face the challenge of developing multiple versions of APIs to integrate with different institutions, creating complexity and hindering interoperability. Fortunately, open banking standards or guidelines (eg. Europe, Singapore, Indonesia, Hong Kong, Australia, etc) have generally required or recommended that the open APIs be RESTful and support JSON data format, which creates a basis for common data exchange. MongoDB addresses these challenges by offering a flexible developer data platform that natively supports JSON data format, simplifies data modeling, and enables flexible schema changes for developers. With features like the MongoDB Data API and GraphQL API , developers can reduce development and maintenance efforts by easily exposing data in a low-code manner. The Stable API feature ensures compatibility during database upgrades, preventing code breaks and providing a seamless transition. Additionally, MongoDB provides productivity-boosting features like full-text search , data visualization , data federation , mobile database synchronization , and other app services enabling developers to accelerate time-to-market. With MongoDB's capabilities, financial institutions and third-party providers can navigate the changing open banking landscape more effectively, foster collaboration, and deliver innovative solutions to customers. An example of a client who leverages MongoDB’s native JSON data management and flexibility is Natwest. Natwest is a major retail and commercial bank in the United Kingdom based in London, England. The bank has moved from zero to 900 million API calls per month within years, as open banking uptake grows and is expected to grow 10 times in coming years. At a MongoDB event on 15 Nov 2022, Jonathan Haggarty, Natwest’s Head of “Bank of APIs” Technology – an API ecosystem that brings the retail bank’s services to partners – shared in his presentation titled Driving Customer Value using API Data that Natwest’s growing API ecosystem lets it “push a bunch of JSON data into MongoDB [which makes it] “easy to go from simple to quite complex information" and also makes it easier to obfuscate user details through data masking for customer privacy. Natwest is enabled to surface customer data insights for partners via its API ecosystem, for example “where customers are on the e-commerce spectrum”, the “best time [for retailers] to push discounts” as well insights on “most valuable customers” – with data being used for problem-solving; analytics and insight; and reporting. Performance In the dynamic landscape of open banking, meeting the unpredictable demands for performance, scalability, and availability is crucial. The efficiency of applications and the overall customer experience heavily rely on the responsiveness of APIs. However, building an open banking platform becomes intricate when accommodating third-party providers with undisclosed business and technical requirements. Without careful management, this can lead to unforeseen performance issues and increased costs. Open banking demands high performance of the APIs under all kinds of workload volumes. OBIE recommends an average TTLB (time to last byte) of 750 ms per endpoint response for all payment invitations (except file payments) and account information APIs. Compliance with regulatory service level agreements (SLAs) in certain jurisdictions further adds to the complexity. Legacy architectures and databases often struggle to meet these demanding criteria, necessitating extensive changes to ensure scalability and optimal performance. That's where MongoDB comes into play. MongoDB is purpose-built to deliver exceptional performance with its WiredTiger storage engine and its compression capabilities. Additionally, MongoDB Atlas improves the performance following its intelligent index and schema suggestions, automatic data tiering, and workload isolation for analytics. One prime illustration of its capabilities is demonstrated by Temenos, a renowned financial services application provider, achieving remarkable transaction volume processing performance and efficiency by leveraging MongoDB Atlas. They recently ran a benchmark with MongoDB Atlas and Microsoft Azure and successfully processed an astounding 200 million embedded finance loans and 100 million retail accounts at a record-breaking 150,000 transactions per second . This showcases the power and scalability of MongoDB with unparalleled performance to empower financial institutions to effectively tackle the challenges posed by open banking. MongoDB ensures outstanding performance, scalability, and availability to meet the ever-evolving demands of the industry. Scalability Building a platform to serve TPPs, who may not disclose their business usages and technical/performance requirements, can introduce unpredictable performance and cost issues if not managed carefully. For instance, a bank in Singapore faced an issue where their Open APIs experienced peak loads and crashes every Wednesday. After investigation, they discovered that one of the TPPs ran a promotional campaign every Wednesday, resulting in a surge of API calls that overwhelmed the bank's infrastructure. A scalable solution that can perform under unpredictable workloads is critical, besides meeting the performance requirements of a certain known volume of transactions. MongoDB's flexible architecture and scalability features address these concerns effectively. With its distributed document-based data model, MongoDB allows for seamless scaling both vertically and horizontally. By leveraging sharding , data can be distributed across multiple nodes, ensuring efficient resource utilization and enabling the system to handle high transaction volumes without compromising performance. MongoDB's auto-sharding capability enables dynamic scaling as the workload grows, providing financial institutions with the flexibility to adapt to changing demands and ensuring a smooth and scalable open banking infrastructure. Availability In the realm of open banking, availability becomes a critical challenge. With increased reliance on banking services by third-party providers (TPPs), ensuring consistent availability becomes more complex. Previously, banks could bring down certain services during off-peak hours for maintenance. However, with TPPs offering 24x7 experiences, any downtime is unacceptable. This places greater pressure on banks to maintain constant availability for Open API services, even during planned maintenance windows or unforeseen events. MongoDB Atlas, the fully managed global cloud database service, addresses these availability challenges effectively. With its multi-node cluster and multi-cloud DBaaS capabilities, MongoDB Atlas ensures high availability and fault tolerance. It offers the flexibility to run on multiple leading cloud providers, allowing banks to minimize concentration risk and achieve higher availability through a distributed cluster across different cloud platforms. The robust replication and failover mechanisms provided by MongoDB Atlas guarantee uninterrupted service and enable financial institutions to provide reliable and always-available open banking APIs to their customers and TPPs. Security and privacy Data security and consent management are paramount concerns for banks participating in open banking. The exposure of authentication and authorization mechanisms to third-party providers raises security concerns and introduces technical complexities regarding data protection. Banks require fine-grained access control and encryption mechanisms to safeguard shared data, including managing data-sharing consent at a granular level. Furthermore, banks must navigate the landscape of data privacy laws like the General Data Protection Regulation (GDPR), which impose strict requirements distinct from traditional banking regulations. MongoDB offers a range of solutions to address these security and privacy challenges effectively. Queryable Encryption provides a mechanism for managing encrypted data within MongoDB, ensuring sensitive information remains secure even when shared with third-party providers. MongoDB's comprehensive encryption features cover data-at-rest and data-in-transit, protecting data throughout its lifecycle. MongoDB's flexible schema allows financial institutions to capture diverse data requirements for managing data sharing consent and unify user consent from different countries into a single data store, simplifying compliance with complex data privacy laws. Additionally, MongoDB's geo-sharding capabilities enable compliance with data residency laws by ensuring relevant data and consent information remain in the closest cloud data center while providing optimal response times for accessing data. To enhance data privacy further, MongoDB offers field-level encryption techniques, enabling symmetric encryption at the field level to protect sensitive data (e.g., personally identifiable information) even when shared with TPPs. The random encryption of fields adds an additional layer of security and enables query operations on the encrypted data. MongoDB's Queryable Encryption technique further strengthens security and defends against cryptanalysis, ensuring that customer data remains protected and confidential within the open banking ecosystem. Activity monitoring With numerous APIs offered by banks in the open banking ecosystem, activity monitoring and troubleshooting become critical aspects of maintaining a robust and secure infrastructure. MongoDB simplifies activity monitoring through its monitoring tools and auditing capabilities. Administrators and users can track system activity at a granular level, monitoring database system and application events. MongoDB Atlas has Administration APIs , which one can use to programmatically manage the Atlas service. For example, one can use the Atlas Administration API to create database deployments, add users to those deployments, monitor those deployments, and more. These APIs can help with the automation of CI/CD pipelines as well as monitoring the activities on the data platform enabling developers and administrators to be freed of this mundane effort and focus on generating more business value. Performance monitoring tools, including the performance advisor, help gauge and optimize system performance, ensuring that APIs deliver exceptional user experiences. Figure 2: Activity Monitoring on MongoDB Atlas MongoDB Atlas Charts , an integrated feature of MongoDB Atlas, offers analytics and visualization capabilities. Financial institutions can create business intelligence dashboards using MongoDB Atlas Charts. This eliminates the need for expensive licensing associated with traditional business intelligence tools, making it cost-effective as more TPPs utilize the APIs. With MongoDB Atlas Charts, financial institutions can offer comprehensive business telemetry data to TPPs, such as the number of insurance quotations, policy transactions, API call volumes, and performance metrics. These insights empower financial institutions to make data-driven decisions, improve operational efficiency, and optimize the customer experience in the open banking ecosystem. Figure 3: Atlas Charts Sample Dashboard Real-Timeliness Open banking introduces new challenges for financial institutions as they strive to serve and scale amidst unpredictable workloads from TPPs. While static content poses fewer difficulties, APIs requiring real-time updates or continuous streaming, such as dynamic account balances or ESG-adjusted credit scores, demand capabilities for near-real-time data delivery. To enable applications to immediately react to real-time changes or changes as they occur, organizations can leverage MongoDB Change Streams that are based on its aggregation framework to react to data changes in a single collection, a database, or even an entire deployment. This capability further enhances MongoDB’s real-time data and event processing and analytics capabilities. MongoDB offers multiple mechanisms to support data streaming, including a Kafka connector for event-driven architecture and a Spark connector for streaming with Spark. These solutions empower financial institutions to meet the real-time data needs of their open banking partners effectively, enabling seamless integration and real-time data delivery for enhanced customer experiences. Conclusion MongoDB's technical capabilities position it as a key enabler for financial institutions embarking on their open banking journey. From managing dynamic environments and accommodating unpredictable workloads to ensuring scalability, availability, security, and privacy, MongoDB provides a comprehensive set of tools and features to address the challenges of open banking effectively. With MongoDB as the underlying infrastructure, financial institutions can navigate the ever-evolving open banking landscape with confidence, delivering innovative solutions, and driving the future of banking. Embracing MongoDB empowers financial institutions to unlock the full potential of open banking and provide exceptional customer experiences in this era of collaboration and digital transformation. If you would like to learn more about how you can leverage MongoDB for your open banking infrastructure, take a look at the below resources: Open banking panel discussion: future-proof your bank in a world of changing data and API standards with MongoDB, Celent, Icon Solutions, and AWS How a data mesh facilitates open banking Financial services hub
Real-Time ESG Data Management
ESG (Environmental, Social, and Governance) data collection and reporting has become a corporate priority, with over 96% of S&P 500 companies publishing sustainability reports in 2021, according to research from the Governance and Accountability Institute. There are several factors driving the adoption and use of ESG data; ranging from consumer preference for companies with positive ESG information, to employees, who increasingly believe environmental, social, and governance metrics are important indicators when choosing an employer. Many government bodies and regulators either have, or are considering, mandatory ESG data collection and ESG data reporting requirements for corporations under their jurisdiction. The European Union is taking the lead here , with several key pieces of legislation either already enacted, or coming soon. In the US, the SEC has also announced proposed rule changes for securities reporting, mandating companies make detailed climate-related disclosures in their filings. In addition to companies that report on their own data, financial firms, including the private equity industry, use ESG data and research to weigh risks and identify opportunities for the companies they invest in. Faced with growing scrutiny around ESG reporting and scoring, companies are struggling to meet ever more detailed and comprehensive reporting requirements. At the heart of the problem is the sheer volume and variety of data companies are expected to ingest and analyze to produce the scores that investors, consumers, and government entities demand. And with real-time data making its way into reports, ESG data management is becoming even harder. ESG data collection and analysis The volume and variety of ESG data makes collection and analysis difficult. The data collection problem can be broken down as follows: Variety Unlike financial datasets, which are mostly numerical, ESG metrics can include both structured and unstructured datasets, like an email or a media report. If a company wants to analyze satellite data to derive their own climate dataset, they may even need to analyze images and videos. Given these variables, companies need to employ a data model that can support many different types of data . Velocity As companies increasingly integrate real-time data sources into their ESG scoring systems, the velocity of data collected and analyzed increases exponentially. One example is loan due diligence in the financial sector. As customers demand faster loan approval turnaround times, financial institutions that currently rely on quarterly ESG data to make those decisions now need the information in real-time to instantly approve loans in an ESG compliant manner. Volume The increased variety of data sources, coupled with the growing velocity of data being collected leads to an increase in the sheer volume of data requiring analysis. Currently, ESG ratings and scores are derived from a blend of human judgment and model driven quantitative rating. But as the volume of data increases, along with the need for instant analysis of that data, real-time analytics and an increased use of AI/ML tools will become an ever greater part of ESG ratings and reporting. On top of this, there are also no universally applicable ESG standards, leaving companies having to deal with multiple different standards, with different data requirements, depending on which jurisdictions they operate in. Real-time ESG data analytics Companies are increasingly incorporating real-time data into their ESG analysis, reporting, and scoring. Harnessing technologies such as cloud computing, AI, and machine learning, those that utilize real-time data can, for instance, instantly parse breaking news stories for ESG-related data on their investments, or incorporate up-to-the-minute satellite data into reports on a firm’s environmental impact. The financial services industry in particular is taking a lead on integrating real-time ESG data into investment decisions. Asset and fund managers use real-time data platforms that allow them to calculate up-to-date ESG scores to aid investment decisions and risk calculations. For example, a bank looking to invest in an electric vehicle company would be alerted to a breaking news story about a hazardous accident at the manufacturer’s battery plant, with follow up data from social media or analyst reports quantifying the size of the public reaction and the level of negative market sentiment around the accident. MongoDB and ESG data management MongoDB Atlas is an ideal data foundation for ESG platforms. MongoDB Atlas uses the document data model, giving users the ability to ingest data from almost any source, consolidate data from a number of siloed data sets, enable the easy search of that data, and with a few clicks, create customized views of the data without the need for additional ETL operations to other databases or tools. MongoDB Atlas also future-proofs your ESG data platform with a flexible data schema that can easily adapt to rapidly changing ESG requirements and standards. See why Hydrus chose MongoDB Atlas as the basis for its ESG reporting platform. FAQ ESG data definition ESG (Environment, Social, and Governance) data comes from a growing list of sources, all of which help “score” a corporation based on how well positioned it is to handle the risks and opportunities presented by the environment, societal stakeholders, and corporate governance. Environment - What are a company's greenhouse gas emissions? How about its stewardship over natural resources? And how well positioned is it to weather physical climate risks, like global warming, flooding, drought, fire etc. Social - How does a company measure up against prevailing fair wage and employee engagement metrics? What impact does a company have on the communities where it operates? Governance - How well is a company managed? How responsive is a company to shareholders? How accountable is leadership? What safeguards are in place to ensure transparency? The growing interest around ESG data science and data analytics has prompted the rise of a new industry of ESG data companies and ESG data management software vendors. What are the different ESG data sources? ESG data may come from two primary sources; 'inside-out' and 'outside-in'. Inside-out data is supplied by companies, used for analysis, and usually lags 6-12 months due to annual ESG-related disclosures. Outside-in data is more regularly updated, sometimes even in real time. Most financial institutions, including banks who often have access to a lot of financial and company data from their customers, do not rely solely on their own data. ESG data analysis requires a broad range of inputs and data that the bank does not possess or can obtain even from their customers. For example, a bank may want to assess the risk of flooding for a chip manufacturing company that has factories in several provinces in China. The bank would need to collect the flood data from the different operating locations in order to score the risk. As banks don’t typically collect flood data themselves, the bank would purchase data from third-party climate data vendors. At this nascent stage of climate risk assessment within the banking industry, it is likely that the bank would not even attempt to collect the raw climate data and create the risk models to score the risk, relying instead on third-party risk scoring vendors. The bank would then make use of these scores and combine in models which they have strong competencies eg. credit risk to come up with flood risk-adjusted credit risk scores for loan approvals. Why is ESG data essential for investors? ESG data is used by asset managers and investors for market analysis, supporting asset allocation and risk management, and in providing insights into the long-term sustainability of investments in various corporations.
Green Lending, Green Data - The Impact on Banks Explained
On 13 Dec 2022, the European Banking Authority (EBA) published its roadmap for sustainable finance. The roadmap – a conglomeration of standards and rules aimed at better integrating ESG risk considerations into the banking sector – is set to come into effect in a rolling fashion over the next 3 years. In our work with leading European banks, clients regularly tell us how they’re starting to build or revamp their ESG data platforms in anticipation of the coming changes around green financing. The conversation typically revolves around how they can flexibly incorporate the many new data sources, types of data, and formats that they will have to ingest and analyze under the EBA’s roadmap of changes. Clients are also increasingly interested to hear what MongoDB has to offer around real-time ESG information delivery. These inquiries come despite the fact that the EBA didn't yet demand real-time public disclosure and regulatory submissions of sustainability information. Check out our blog on Real-Time ESG data management. Green lending, green metrics, green data One interesting area of the EBA roadmap concerns loans with environmental sustainability features, so called green lending. These loans, sometimes called energy efficient or green mortgages, are typically given to retail clients and SMEs to make energy efficient improvements to homes and other buildings, such as adding solar panels or funding other renewable energy work. According to the roadmap, "... the EBA will consider the merits of an EU definition for green loans and mortgages, and will identify potential measures to encourage their uptake or facilitate their access by retail and SME borrowers…. In line with the request, the EBA will deliver its advice to the European Commission by December 2023." With the EBA pushing to increase the uptake of green loans, affected banks will have to re-work their scoring criteria for green loans to fit the EBA’s new classification and incentives guidelines: Banks will need to change their green loans credit scoring model to grant "green" retail loans and mortgages. Changes will also likely be required for risk adjusted performance indicators such as RAROC (Risk-Adjusted-Return-on-Capital) which many banks used to quantify the risk-return ratio and other indicators or metrics for pricing and approval decisions. This may result in a change in the acceptance performance of the loans and mortgages. As requested by the European Commission, the changes would not only affect new loans, but also "...already originated loans". Depending on the final advice of EBA, this could potentially mean reassessing existing loans with new data to determine if they can now be classified as “green”. Additionally, a re-assessment of most, if not all the related indicators for risk management and reporting would also need to happen. All of these potential changes mean banks having to collect more data, from more disparate sources than ever before. The impact on banks All these can mean significant impact to the loan origination process and data systems supporting the process. Here are some questions for banks to think about: Managing evolving or unforeseen changes. How would a bank change their Loan Origination system and related data platform (eg. credit data mart) to quickly adapt to the new green loan taxonomy and data elements? As the standards and classification rules are still evolving, how can one design an application and data schema that will still assure the development team that they easily adapt without throwing away existing work? Capturing different data attributes for the same product/loan. How can banks take existing retail loan products or mortgages and integrate different assessment criteria such as country specific regulations within EU and outside of the EU? How about incorporating specific market/business practices within a country, such as a car loan, which can vary based on the type of car (Battery electric vehicles, vs hybrid, vs gas powered). Incorporating new data types and formats. How can one capture information that goes beyond traditional financial credit data, including new data for both green classifications and green risk assessments? In the ECB’s 2022 climate risk stress testing , the ECB already gave a preview that geospatial data will be required to assess loan risks. How can banks add geo-location data and perform queries and analytics in a co-existent and seamless manner with the other existing data on their existing data platform? How about incorporating a whole raft of new unstructured sources, such as text description (emails, collateral documentation) that contains required ESG or sustainability characteristics to correctly classify the loan, for instance carbon emission descriptions of the house under mortgage. Finding insights from data explosion. With the increasing volume and variety of data sources, how can the borrowers quickly find the information (such as guidelines and related product information or ESG related guidance to obtain the relevant data) needed to correctly submit all the required loan information? Can potential borrowers type in "green car loan" and the lending bank’s web site or mobile app return immediately the relevant information for the potential customer? How can green loan credit officers quickly search for borrowers pending approval that have textual collateral information or certain risk information, matching keywords related to new risk findings that change the risk decision? Meeting the demands of customers, and the competition. Will the bank’s loan origination systems be able to provide a sustainability risk-adjusted credit score in real-time for in-principle approvals? Will that system scale to keep up with the demand caused by a large volume of retail borrowers? How MongoDB can help Loan origination, including post-origination monitoring, requires a large system with multiple modules and corresponding internal user groups such as loan application and data capture, data enrichment, financial risk analytics, decision and approvals, and loan closure. There are many ways a bank can architect or revise its loan origination and monitoring systems. Here is a simplified architecture with MongoDB for a green loan origination system built to service the EBA’s proposed green loan changes. Simplified architecture for Green Loan origination. A few key features of using MongoDB Atlas: Atlas Device Sync can automatically synchronize MongoDB's mobile database Realm, deployed on the mobile devices of users, back to Atlas. Borrowers (or even loan officers) who need to submit or review a large set of documents can access the documents faster with the offline-first Realm mobile database. The use of Realm and Device Sync also speeds up mobile development and alleviates the need to maintain complex data synchronization logic. Atlas Search is an embedded, full-text search in MongoDB Atlas that gives you a seamless, scalable experience for building relevance-based app features. Built on Apache Lucene, Atlas Search eliminates the need to run a separate search system alongside your database. Combined with Altas Search facet, users can quickly narrow down Atlas Search results based on the most frequent attribute values in the specified attribute field. Atlas Data API and GraphQL – MongoDB Atlas provides a low-code/no-code approach for developers to quickly develop APIs for other internal or even external applications (like Third-Party Providers (TPPs) in an Open Banking ecosystem) to access data in a secure manner. MongoDB supports the use of GraphQL, a query language for API development designed to let developers construct requests that pull data from multiple data sources in a single API call. This helps eliminate over-fetching problems and circumvents the need for multiple costly round trips to the server. The ease of building data access and reduction of performance roundtrip, helps banks accelerate business with TPPs in an open banking ecosystem, improving the customer experience either with direct access to the bank's mobile applications, or those of a TPP. Data Aggregation Pipeline is a framework for data aggregation, modeled on the concept of data processing pipelines. Documents enter a multi-stage pipeline that transforms them into aggregated results. This allows bank development teams to quickly implement data analytics in a natural sequence of data processing units, rather than needing to use multiple nesting SQL statements. This framework is the cornerstone of providing the high performance transanalytics capabilities that MongoDB is known for . Banks can develop both real-time on-the-fly ESG-adjusted credit scoring and also batch analytics processing required as part of the loan origination process. Atlas Charts is a data visualization tool built into Atlas. It provides a clear understanding of your data, highlighting correlations between variables and making it easy to discern patterns and trends within your dataset. The Charts API allows banks to build in-app business intelligence with a variety of analytics tools to help both the borrowers and the bank to gain more insights into the loan. ESG vendors have used MongoDB to help their Fortune 500 customers to improve their ESG performance . For retail loans where the ESG complexity or green requirements should be a lot less complicated, the self-service analytics that Charts can provide would help to accelerate green retail loan processing even more. Atlas Triggers allow you to execute server-side logic in response to database events or according to a schedule. Triggers can respond to events or use pre-defined schedules. Triggers can be combined with many other integration features, such as the Data API mentioned above, to perform the necessary actions for the loan workflow. No task would be missed or remain unprocessed! Offer Green Loans with MongoDB One of the questions I am asked by peers in risk management is, “Why would ESG be relevant to retail loans?”. Such a question makes me suspect that there is still a lack of understanding around the relevance of ESG, sustainability and climate risk to those who may be working in ESG but not in retail lending business. The EBA's roadmap clearly indicates that there is a need to not just require sustainability to be incorporated in retail loans and green mortgages, but also to develop standards and guidelines to support that. This EBA sustainable finance roadmap clarifies, consolidates, and expands on earlier plans and should help financial institutions impacted to be more prepared for the wave of changes coming in ESG and sustainability financing. Both business and technology teams should start thinking about how to adapt for these evolving requirements and newly forming standards, whether they are in a market directly impacted by the EBA’s roadmap, or in one that will be influenced by these new standards. Afterall, EU regulation is often referenced and / or adopted by regulators in other countries and regions.