Transforming Industries with MongoDB and AI: Financial Services

This is the fourth in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.

In the dynamic world of financial services, the partnership between artificial intelligence (AI) and banking services is reshaping traditional practices, offering innovative solutions across critical functions.

Relationship management support with chatbots

One key service that relationship managers provide to their private banking customers is aggregating and condensing information. Because banks typically operate on fragmented infrastructure with information spread across different departments, solutions, and applications, this can require a lot of detailed knowledge about this infrastructure and how to source information such as:

• When are the next coupon dates for bonds in the portfolio?

• What has been the cost of transactions for a given portfolio?

• What would be a summary of our latest research?

• Please generate a summary of my conversation with the client.

Until now, these activities would be highly manual and exploratory. For example, a relationship manager (RM) looking for the next coupon dates would likely have to go into each of the clients' individual positions and manually look up the coupon dates. If this is a frequent enough activity, the RM could raise a request for change with the product manager of the portfolio management software to add this as a standardized report. But even if such a standardized report existed, the RM might struggle to find the report quickly. Overall, the process is time-consuming.

Generative AI systems can facilitate such tasks. Even without specifically trained models, RAG can be used to have the AI generate the correct answers, provide the inquirer with a detailed explanation of how to get to the data, and, in the same cases directly execute the query against the system and report back the results. Similar to a human, it is critical that the algorithm has access to not only the primary business data, e.g. the portfolio data of the customer, but also user manuals and static data. Detailed customer data, in machine-readable format and as text documents, is used to personalize the output for the individual customer.

In an interactive process, the RM can instruct the AI to add more information about specific topics, tweak the text, or make any other necessary changes. Ultimately, the RM will be the quality control for the AI’s output to mitigate hallucinations or information gaps.

As outlined above, not only will the AI need highly heterogeneous data from highly structured portfolio information to text documents and system manuals to provide a flexible natural language interface for the RMs, it will also have to have timely processing information about a customer's transactions, positions, and investment objectives. Providing transactional database capabilities as well as vector search makes it easy to build RAG-based applications using MongoDB’s developer data platform.

Risk management and regulatory compliance

Risk and fraud prevention

Banks are tasked with safeguarding customer assets and detecting fraud, verifying customer identities, supporting sanctions regimes (Sanctions), and preventing various illegal activities (AML). The challenge is magnified by the sheer volume and complexity of regulations, making the integration of new rules into bank infrastructure costly, time-consuming, and often inadequate. For instance, when the EU's Fifth Anti-Money Laundering Directive was implemented, it broadened regulations to cover virtual currencies and prepaid cards. Banks had to update their onboarding processes swiftly, and software, train staff, and possibly update their customer interfaces to comply with these new requirements.

AI offers a transformative approach to fraud detection and risk management by automating the interpretation of regulations, supporting data cleansing, and enhancing the efficacy of surveillance systems. Unlike static, rules-based frameworks that may miss or misidentify fraud due to narrow scope or limited data, AI can adaptively learn and analyze vast datasets to identify suspicious activities more accurately. Machine learning, in particular, has shown promise in trade surveillance, offering a more dynamic and comprehensive approach to fraud prevention.

Regulatory compliance and code change assistance

The regulatory landscape for banks has grown increasingly complex, demanding significant resources for the implementation of numerous regulations. Traditionally, adapting to new regulations has required the manual translation of legal text into code, provisioning of data, and thorough quality control—a process that is both costly and time-consuming, often leading to incomplete or insufficient compliance. For instance, to comply with the Basel III international banking regulations, developers must undertake extensive coding changes to accommodate the requirements laid out in thousands of pages of documentation.

AI has the capacity to revolutionize compliance by automating the translation of regulatory texts into actionable data requirements and validating compliance through intelligent analysis. This approach is not without its challenges, as AI-based systems may produce non-deterministic outcomes and unexpected errors. However, the ability to rapidly adapt to new regulations and provide detailed records of compliance processes can significantly enhance regulatory adherence.

Financial document search and summarization

Financial institutions, encompassing both retail banks and capital market firms, handle a broad spectrum of documents critical to their operations. Retail banks focus on contracts, policies, credit memos, underwriting documents, and regulatory filings, which are pivotal for daily banking services. On the other hand, capital market firms delve into company filings, transcripts, reports, and intricate data sets to grasp global market dynamics and risk assessments.

These documents often arrive in unstructured formats, presenting challenges in efficiently locating and synthesizing the necessary information. While retail banks aim to streamline customer and internal operations, capital market firms prioritize the rapid and effective analysis of diverse data to inform their investment strategies. Both retail banks and capital market firms allocate considerable time to searching for and condensing information from documents internally, resulting in reduced direct engagement with their clients.

Generative AI can streamline the process of finding and integrating information from documents by using NLP and machine learning to understand and summarize content. This reduces the need for manual searches, allowing bank staff to access relevant information more quickly.

MongoDB can store vast amounts of both live and historical data, regardless of its format which is typically needed for AI applications. It offers Vector Search capabilities essential for retrieval-augmented generation (RAG). MongoDB supports transactions, ensuring data accuracy and consistency for AI model retraining with live data. It facilitates data access for both deterministic algorithms and AI-driven rules through a single interface. MongoDB boasts a strong partnership ecosystem, including companies like Radiant AI and Mistral AI, to speed solution development.

ESG analysis

Environmental, social, and governance (ESG) considerations can have a profound impact on organizations. For example, regulatory changes—especially in Europe—have compelled financial institutions to integrate ESG into investment and lending decisions. Regulations such as the EU Sustainable Finance Disclosure Regulation (SFDR) and the EU Taxonomy Regulation are examples of such directives that require financial institutions to consider environmental sustainability in their operations and investment products. Investors' demand for sustainable options has surged, leading to increased ESG-focused funds. The regulatory and commercial requirements, in turn, drive banks to also improve their green lending practices. This shift is strategic for financial institutions, attracting clients, managing risks, and creating long-term value.

However, financial institutions face many challenges in managing different aspects of improving their ESG analysis. The key challenges include defining and aligning standards, and processes and managing the flood of rapidly changing and varied data to be included for ESG analysis purposes.

AI can help to address these key challenges in not only an automatic but also adaptive manner via techniques like machine learning. Financial institutions and ESG solution providers have already leveraged AI to extract insights from corporate reports, social media, and environmental data, improving the accuracy and depth of ESG analysis. As the market demands a more sustainable and equitable society, predictive AI combined with generative AI can also help to reduce bias in lending to create fairer and more inclusive financing while improving the predictive powers. The power of AI can help facilitate the development of sophisticated sustainability models and strategies, marking a leap forward in integrating ESG into broader financial and corporate practices.

Credit scoring

The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome by adopting alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI, while introducing the potential challenge of hallucination, represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness.

The use of artificial intelligence, in particular generative artificial intelligence, as an alternative method to credit scoring has emerged as a transformative force to address the challenges of traditional credit scoring methods for several reasons:

• Alternative data analysis: AI models can process a myriad of information, including alternative data such as utility payments and rental history, to create a more comprehensive assessment of an individual's creditworthiness.

• AI offers unparalleled adaptability: As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust.

• Fraud detection: AI algorithms can detect fraudulent behavior by identifying anomalies and suspicious patterns in credit applications and transaction data.

• Predictive analysts: AI algorithms, particularly ML techniques, can be used to build predictive models that identify patterns and correlations in historical credit data.

• Behavioral analysis: AI algorithms can analyze behavioral data sets to understand financial habits and risk propensity.

By harnessing the power of artificial intelligence, lenders can make more informed lending decisions, expand access to credit, and better serve consumers (especially those with limited credit history). However, to mitigate potential biases and ensure consumer trust, it's crucial to ensure transparency, fairness, and regulatory compliance when deploying artificial intelligence in credit scoring.

AI in payments

A lack of developer capacity is one of the biggest challenges for banks when delivering payment product innovation. Banks believe the product enhancements they could not deliver in the past two years due to resource constraints would have supported a 5.3% growth in payments revenues. With this in mind and the revolutionary transformation with the integration of AI, it is imperative to consider how to free up developer resources to make the most of these opportunities. There are several areas in which banks can apply AI to unlock new revenue streams and efficiency gains. The image below provides a high-level view of eight of the principal themes and areas. This is not an exhaustive view but does demonstrate the depth and breadth of current opportunities. In each example, there are already banks that have begun to bring services or enhancements to the market using AI technologies or are otherwise experimenting with the technology.