Jack Yallop

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Reducing Bias in Credit Scoring with Generative AI

Credit scoring plays a pivotal role in determining who gets access to credit and on what terms. Despite its importance, however, traditional credit scoring systems have long been plagued by a series of critical issues from biases and discrimination, to limited data consideration and scalability challenges. For example, a study of US loans showed that minority borrowers were charged higher interest rates (+8%) and rejected loans more often (+14%) than borrowers from more privileged groups. The rigid nature of credit systems means that they can be slow to adapt to changing economic landscapes and evolving consumer behaviors, leaving some individuals underserved and overlooked. To overcome this, banks and other lenders are looking to adopt artificial intelligence to develop increasingly sophisticated models for scoring credit risk. In this article, we'll explore the fundamentals of credit scoring, the challenges current systems present, and delve into how artificial intelligence (AI), in particular, generative AI (genAI) can be leveraged to mitigate bias and improve accuracy. From the incorporation of alternative data sources to the development of machine learning (ML) models, we'll uncover the transformative potential of AI in reshaping the future of credit scoring. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. What is credit scoring? Credit scoring is an integral aspect of the financial landscape, serving as a numerical gauge of an individual's creditworthiness. This vital metric is employed by lenders to evaluate the potential risk associated with extending credit or lending money to individuals or businesses. Traditionally, banks rely on predefined rules and statistical models often built using linear regression or logistic regression. The models are based on historical credit data, focusing on factors such as payment history, credit utilization, and length of credit history. However, assessing new credit applicants poses a challenge, leading to the need for more accurate profiling. To cater to the underserved or unserved segments traditionally discriminated against, fintechs and digital banks are increasingly incorporating information beyond traditional credit history with alternative data to create a more comprehensive view of an individual's financial behavior. Challenges with traditional credit scoring Credit scores are integral to modern life because they serve as a crucial determinant in various financial transactions, including securing loans, renting an apartment, obtaining insurance, and even sometimes in employment screenings. Because the pursuit of credit can be a labyrinthine journey, here are some of the challenges or limitations with traditional credit scoring models that often cloud the path to credit application approval. Limited credit history: Many individuals, especially those new to the credit game, encounter a significant hurdle – limited or non-existent credit history. Traditional credit scoring models heavily rely on past credit behavior, making it difficult for individuals without a robust credit history to prove their creditworthiness. Roughly 45 million Americans lack credit scores simply because those data points do not exist for them. Inconsistent income: Irregular income, typical in part-time work or freelancing, poses a challenge for traditional credit scoring models, potentially labeling individuals as higher risk and leading to application denials or restrictive credit limits. In 2023 in the United States , data sources differ on how many people are self-employed. One source shows more than 27 million Americans filed Schedule C tax documents, which cover net income or loss from a business – highlighting the need for different methods of credit scoring for those self-employed. High utilization of existing credit: Heavy reliance on existing credit is often perceived as a signal of potential financial strain, influencing credit decisions. Credit applications may face rejection or approval with less favorable terms, reflecting concerns about the applicant's ability to judiciously manage additional credit. Lack of clarity in rejection reasons: Understanding the reasons behind rejections hinders applicants from addressing the root causes – in the UK, a study between April 2022 and April 2023 showed the main reasons for rejection included “poor credit history” (38%), “couldn’t afford the repayments” (28%), “having too much other credit" (19%) and 10% said they weren’t told why. The reasons even when given are often too vague which leaves applicants in the dark, making it difficult for them to address the root cause and enhance their creditworthiness for future applications. The lack of transparency is not only a trouble for customers, it can also lead to a penalty for banks. For example, a Berlin bank was fined €300k in 2023 for lacking transparency in declining a credit card application. Lack of flexibility: Shifts in consumer behavior, especially among younger generations preferring digital transactions, challenge traditional models. Factors like the rise of the gig economy, non-traditional employment, student loan debt, and high living costs complicate assessing income stability and financial health. Traditional credit risk predictions are limited during unprecedented disruptions like COVID-19, not taking this into account in scoring models. Recognizing these challenges highlights the need for alternative credit scoring models that can adapt to evolving financial behaviors, handle non-traditional data sources, and provide a more inclusive and accurate assessment of creditworthiness in today's dynamic financial landscape. Credit scoring with alternative data Alternative credit scoring refers to the use of non-traditional data sources (aka. alternative data) and methods to assess an individual's creditworthiness. While traditional credit scoring relies heavily on credit history from major credit bureaus, alternative credit scoring incorporates a broader range of factors to create a more comprehensive picture of a person's financial behavior. Below are some of the popular alternative data sources: Utility payments: Beyond credit history, consistent payments for utilities like electricity and water offer a powerful indicator of financial responsibility and reveal a commitment to meeting financial obligations, providing crucial insights beyond traditional metrics. Rental history: For those without a mortgage, rental payment history emerges as a key alternative data source. Demonstrating consistent and timely rent payments paints a comprehensive picture of financial discipline and reliability. Mobile phone usage patterns: The ubiquity of mobile phones unlocks a wealth of alternative data. Analyzing call and text patterns provides insights into an individual's network, stability, and social connections, contributing valuable information for credit assessments. Online shopping behavior: Examining the frequency, type, and amount spent on online purchases offers valuable insights into spending behaviors, contributing to a more nuanced understanding of financial habits. Educational and employment background: Alternative credit scoring considers an individual's educational and employment history. Positive indicators, such as educational achievements and stable employment, play a crucial role in assessing financial stability. These alternative data sources represent a shift towards a more inclusive, nuanced, and holistic approach to credit assessments. As financial technology continues to advance, leveraging these alternative data sets ensures a more comprehensive evaluation of creditworthiness, marking a transformative step in the evolution of credit scoring models. Alternative credit scoring with artificial intelligence Besides the use of alternative data, the use of AI as an alternative method has emerged as a transformative force to address the challenges of traditional credit scoring, for a number of reasons: Ability to mitigate bias: Like traditional statistical models, AI models, including LLMs, trained on historical data that are biased will inherit biases present in that data, leading to discriminatory outcomes. LLMs might focus on certain features more than others or may lack the ability to understand the broader context of an individual's financial situation leading to biased decision-making. However, there are various techniques to mitigate the bias of AI models: Mitigation strategies: Initiatives begin with the use of diverse and representative training data to avoid reinforcing existing biases. Inadequate or ineffective mitigation strategies can result in biased outcomes persisting in AI credit scoring models. Careful attention to the data collected and model development is crucial in mitigating this bias. Incorporating alternative data for credit scoring plays a critical role in reducing biases. Rigorous bias detection tools, fairness constraints, and regularization techniques during training enhance model accountability: Balancing feature representation and employing post-processing techniques and specialized algorithms contribute to bias mitigation. Inclusive model evaluation, continuous monitoring, and iterative improvement, coupled with adherence to ethical guidelines and governance practices, complete a multifaceted approach to reducing bias in AI models. This is particularly significant in addressing concerns related to demographic or socioeconomic biases that may be present in historical credit data. Regular bias audits: Conduct regular audits to identify and mitigate biases in LLMs. This may involve analyzing model outputs for disparities across demographic groups and adjusting the algorithms accordingly. Transparency and explainability: Increase transparency and explainability in LLMs to understand how decisions are made. This can help identify and address biased decision-making processes. Trade Ledger , a lending software as a service (SaaS) tool, uses a data-driven approach to make informed decisions with greater transparency and traceability by bringing data from multiple sources with different schemas into a single data source. Ability to analyze vast and diverse datasets: Unlike traditional models that rely on predefined rules and historical credit data, AI models can process a myriad of information, including non-traditional data sources, to create a more comprehensive assessment of an individual's creditworthiness, ensuring that a broader range of financial behaviors is considered. AI brings unparalleled adaptability to the table: As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust and learn from new data. The continuous learning aspect ensures that credit scoring remains relevant and effective in the face of ever-changing financial landscapes. The most common objections from banks to not using AI in credit scoring are transparency and explainability in credit decisions. The inherent complexity of some AI models, especially deep learning algorithms, may lead to challenges in providing clear explanations for credit decisions. Fortunately, the transparency and interpretability of AI models have seen significant advancements. Techniques like SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) plots and several other advancements in the domain of Explainable AI (XAI) now allow us to understand how the model arrives at specific credit decisions. This not only enhances trust in the credit scoring process but also addresses the common critique that AI models are "black boxes." Understanding the criticality of leveraging alternative data that often comes in a semi or unstructured format, financial institutions work with MongoDB to enhance their credit application processes with a faster, simpler, and more flexible way to make payments and offer credit: Amar Bank, Indonesia's leading digital bank , is combatting bias by providing microloans to people who wouldn’t be able to get financial services from traditional banks (unbanked and underserved). Traditional underwriting processes were inadequate for customers lacking credit history or collateral so they have streamlined lending decisions by harnessing unstructured data. Leveraging MongoDB Atlas, they developed a predictive analytics model integrating structured and unstructured data to assess borrower creditworthiness. MongoDB's scalability and capability to manage diverse data types were instrumental in expanding and optimizing their lending operations. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and a lack of credit data. Through the use of modern underwriting systems, slice, a leading innovator in India’s fintech ecosystem , is helping broaden the accessibility to credit in India by streamlining their KYC process for a smoother credit experience. By utilizing MongoDB Atlas across different use cases, including as a real-time ML feature store, slice transformed their onboarding process, slashing processing times to under a minute. slice uses the real-time feature store with MongoDB and ML models to compute over 100 variables instantly, enabling credit eligibility determination in less than 30 seconds. Transforming credit scoring with generative AI Besides the use of alternative data and AI in credit scoring, GenAI has the potential to revolutionize credit scoring and assessment with its ability to create synthetic data and understand intricate patterns, offering a more nuanced, adaptive, and predictive approach. GenAI’s capability to synthesize diverse data sets addresses one of the key limitations of traditional credit scoring – the reliance on historical credit data. By creating synthetic data that mirrors real-world financial behaviors, GenAI models enable a more inclusive assessment of creditworthiness. This transformative shift promotes financial inclusivity, opening doors for a broader demographic to access credit opportunities. Adaptability plays a crucial role in navigating the dynamic nature of economic conditions and changing consumer behaviors. Unlike traditional models which struggle to adjust to unforeseen disruptions, GenAI’s ability to continuously learn and adapt ensures that credit scoring remains effective in real-time, offering a more resilient and responsive tool for assessing credit risk. In addition to its predictive prowess, GenAI can contribute to transparency and interpretability in credit scoring. Models can generate explanations for their decisions, providing clearer insights into credit assessments, and enhancing trust among consumers, regulators, and financial institutions. One key concern however in making use of GenAI is the problem of hallucination, where the model may present information that is either nonsensical or outright false. There are several techniques to mitigate this risk and one approach is using the Retrieval Augment Generation (RAG) approach. RAG minimizes hallucinations by grounding the model’s responses in factual information from up-to-date sources, ensuring the model’s responses reflect the most current and accurate information available. Patronus AI for example leverages RAG with MongoDB Atlas to enable engineers to score and benchmark large language models (LLMs) performance on real-world scenarios, generate adversarial test cases at scale, and monitor hallucinations and other unexpected and unsafe behavior. This can help to detect LLM mistakes at scale and deploy AI products safely and confidently. Another technology partner of MongoDB is Robust Intelligence . The firm’s AI Firewall protects LLMs in production by validating inputs and outputs in real-time. It assesses and mitigates operational risks such as hallucinations, ethical risks including model bias and toxic outputs, and security risks such as prompt injections and personally identifiable information (PII) extractions. As generative AI continues to mature, its integration into credit scoring and the broader credit application systems promises not just a technological advancement, but a fundamental transformation in how we evaluate and extend credit. A pivotal moment in the history of credit 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 through the adoption of 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. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Digitizing the lending and leasing experience with MongoDB Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Discover how slice enables credit approval in less than a minute for millions

February 20, 2024

Maximizing Growth: The Power of AI Unleashed in Payments

Artificial Intelligence (AI) technologies are an integral part of the banking industry. In areas such as risk, fraud , and compliance, for example, the use of AI has been commonplace for years and continues to deepen. The success of these initiatives (and others), and the potential to unlock further benefits, is driving further investment in this area in 2024, with Generative AI attracting particular interest. Financial tech analyst Celent created a report commissioned by MongoDB and Icon Solutions that dives into how AI is currently being used in the banking industry today, as well as some of the key use cases for AI adoption in payments to improve operational agility, automate workflows, and increase developer productivity. Download Celent’s report: Harnessing the Benefits of AI in Payments to discover how you can make the most of your AI investments and unlock the limitless possibilities that AI holds for the future of payments. Unlocking a range of workflow and product enhancements AI technologies are used today to address a wide range of different workflows and customer-facing services from process automation and optimization in the middle and back office, to areas such as real-time risk and liquidity management, cashflow forecasting, and service personalization in the front office. Virtual assistants and bots have also become an important part of the customer support process. In this blog, we'll cover some of the key findings from Celent’s Harnessing the Benefits of AI in Payments report and what this means for the banking and payments industry. Advanced analytics, intelligent automation, and AI technologies lead the investment agenda in 2024 Over time, banks have steadily increased their investments in projects to make better and more efficient use of data. In part, this has been driven by the need to respond to rising customer expectations over the speed and quality of digital services, but it also reflects a growing understanding of the true value of account and transaction data. Most important of all, though, has been enabling the technologies required to deliver use cases supported by AI and advanced analytics. It is no surprise to see that projects supported by data analytics and AI technologies are high on the agenda globally. Advanced analytics and machine learning investments are a leading technology priority for 33% of corporate banks, ranking higher than projects relating to robotics and automation (a focus for 31% of the market). Artificial intelligence and natural language processing (NLP) are not far behind and were highlighted as a priority by 28% of banks. Many are also exploring Generative AI While the excitement around genAI is understandable given the obvious potential, the conversation became more nuanced through the latter part of 2023. This is understandable given the complexities of applying large language models (LLMs) to potentially sensitive customer data, as well as broader regulatory concerns over the explainability (and potential auditability) of LLM outputs. That said, there are many areas in which genAI is already being used to support advisors and relationship managers and further innovation in areas such as this is expected. According to the report, 58% of banks are evaluating or testing Generative AI in some capacity while a further 23% have projects using this technology in their roadmap. Emerging use cases for AI in payments and the potential revenue growth A lack of developer capacity is one of the biggest challenges for banks when it comes to 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, financial institutions must consider how to free up developer resources to make the most of these opportunities. As the payments industry continues to evolve, the integration of AI is poised to reshape the landscape, offering innovative solutions that prioritize security, efficiency, personalized user experience. The emerging use cases for AI in payments are a testament to its transformative potential in shaping the future of financial transactions. Leveraging modern technologies to make the most of AI adoption In the rapidly evolving landscape of AI, constant technological advancements and evolving customer needs necessitate strategic investments. To stay competitive, banks and payment providers should not only focus on current product enhancements but also future-proof their capabilities through payment infrastructure modernization . When adopting advanced technologies like AI and ML which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. For example, adding a new payment rail and a new customer access point could be very difficult. Establishing a robust data architecture with a modern data platform that enables banks to enrich the payments experience by consolidating and analyzing data in any format in real-time, driving value-added services and features to consumers. The following recommendations will help ensure financial services organizations can unlock the transformative potential of generative AI at scale while ensuring privacy and security concerns are adequately addressed: Train AI/ML models on the most accurate and up-to-date data , thereby addressing the critical need for adaptability and agility in the face of evolving technologies. By unifying data from backend payment processing to customer interactions, banks can surface insights in real-time to create a seamless, connected, and personalized customer journey. Future-proof with a flexible data schema capable of accommodating any data structure, format, or source. This flexibility facilitates seamless integration with different AI/ML platforms, allowing financial institutions to adapt to changes in the AI landscape without extensive modifications to the infrastructure. Address security concerns with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. Launch and scale always-on and secure applications by integrating third-party services with APIs. MongoDB's flexible data model and ability to handle various types of data, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich payment experiences by consolidating, ingesting, and acting on any payment data type instantly. MongoDB Atlas is designed to help financial services organizations overcome data challenges. It features a flexible document data model and seamless third-party integration capabilities that are necessary to create composable payment systems that scale effortlessly, are always-on, secure, and ACID compliant. Stay ahead of the curve — download Celent’s report now and unlock the limitless possibilities that AI holds for the future of payments. If you prefer a visual exploration, a discussion featuring Celent, Icon Solutions, and MongoDB, register for our upcoming webinar, Using AI to Unlock New Opportunities in Payments with Celent, Icon Solutions, and MongoDB . If you would like to discover more about building AI-enriched payment applications with MongoDB, take a look at the following resources: Discover how the financial sector can make use of Generative AI Deliver AI-enriched payment apps with the right security controls in place, and at the scale and performance users expect Sign up for our Atlas for Industries programme to get access to our solution accelerators to drive innovation

February 12, 2024

Navigating the Landscape of Artificial Intelligence: How Can The Financial Sector Make Use of Generative AI

In the ever-evolving landscape of financial technology, the conversation around artificial intelligence (AI), and particularly generative AI is gaining momentum. AI has been part of the financial landscape for decades but with the advances in generative AI come greater benefits but also risks that financial institutions need to consider in such a regulated industry. While the potential benefits of generative AI are significant and the adoption by many is still being considered, a measured approach is needed when moving from proof of concept into production. In an edition of the Fintech Finance News Virtual Arena , several notable industry thought leaders from HSBC, Capgemini, and MongoDB came together to explore how the financial sector can make use of generative AI and what financial institutions must consider in their AI strategy. Watch the panel discussion How can the financial sector make use of generative AI today with HSBC, MongoDB and Capgemini . Hear from: EJ Achtner, Office of Applied Artificial Intelligence at HSBC Dan Pears, Vice President, UK Practice Lead at Capgemini Wei You Pan, Director, Financial Services Industry Solutions at MongoDB Doug Mackenzie, Chief Content Officer at FF News Addressing the challenges of generative AI While financial technologists have always had to deal with persistent issues like risk management and governance, the adoption of generative AI in fintech introduces new challenges that AI specialists have always dealt with, like inherent biases and ethical concerns. One challenge that stands out for generative AI is hallucination — the generation of content that is not accurate, factual, or reflective of the real world. AI models may produce information that sounds plausible but is entirely fictional. Generative AI models, especially in natural language processing, might generate text that is coherent and contextually appropriate but lacks factual accuracy. This poses challenges in different domains, including misinformation and content reliability. Examples of such challenges or risks may include: Misleading financial planning advice: In financial advisory services, hallucinated information may result in misleading advice leading to unexpected risks or missed opportunities. Incorrect risk assessments for lending: Inaccurate risk profiles may lead to poor risk assessments for loan applicants that can cause a financial institution to approve a loan at a higher risk of default than the firm would normally accept. Sensitive information in generated text: When generating text, models may inadvertently include sensitive information from the training data. Adversaries can craft input prompts to coax the model into generating outputs that expose confidential details present in the training corpus. It is thus paramount that financial institutions understand the technological impact, scale, and complexity associated with AI, especially the generative AI strategy. A strategic and comprehensive approach that encompasses various aspects of technology, data, ethics, and organizational readiness is critical. Here are some key considerations financial institutions must consider when adopting such a strategy: Hallucination mitigation: Mitigating hallucination in generative AI is a challenging task, but several strategies and techniques can be employed to reduce the risk of generating inaccurate or misleading information. One promising strategy is to make use of the Retrieval Augmented Generation (RAG) approach to mitigate hallucination in generative AI models. This approach involves incorporating information retrieval mechanisms to enhance the generation process, ensuring that generated content is grounded in real-world knowledge. Vector Search is a popular mechanism to support the implementation of the RAG architecture which uses vector search to retrieve relevant documents based on the input query. It then provides these retrieved documents as context to the large language models (LLM) to help generate a more informed and accurate response. Data quality and availability: Take a step back before adopting AI to ensure the quality, relevance, and accuracy of data being used for AI training and decision-making can be accessed in real time. Education: Investing in training programs is key to addressing the skills gap in AI, ensuring the workforce is equipped to manage, interpret, and collaborate with AI technologies. For the adoption of AI to be successful, a culture of learning and development is vital, providing employees with the tools needed to be the absolute best that they can be for their personal and professional development. Furthermore, promoting awareness about potential vulnerabilities and continuously refining models to enhance their resilience against hallucination, biases, adversarial manipulation, and other weaknesses are essential to ensure success in generative AI applications. Develop new governance, frameworks, and controls: Before going live, create safe and secure environments for testing and learning that allow you to fail fast in a safe manner. Moving headfirst into production with direct contact with customers can result in the wrong governance methods being implemented. Monitoring and continuous improvement: Implement robust monitoring systems to measure and understand financial impacts, change impacts, scale, and complexity associated with the adoption of AI. Scalability and integration: Design AI systems with scalability in mind to accommodate growing datasets and evolving requirements. Security and privacy: Implement robust cybersecurity measures to safeguard AI models and the data they rely on. Techniques such as adversarial training, input sanitization, and incorporating privacy-preserving mechanisms can help mitigate the risk of generative AI inadvertently revealing private data. Incident response plans should be part of the cybersecurity measures, as well as regular education of the relevant stakeholders on security and privacy. How MongoDB can help you overcome your data challenges In the realm of adopting advanced technologies like AI and ML which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems, particularly when it comes to use cases such as fraud prevention where the platform is integrated with external sources for accurate data analysis on complete data. The inflexibility of existing systems poses a significant pain point, hindering the seamless incorporation of cutting-edge technologies. MongoDB serving as the operational data store (ODS) with a flexible document model enables financial institutions to efficiently handle large volumes of data in real time. By integrating MongoDB with AI/ML platforms, businesses can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. Legacy systems, marked by their inflexibility and resistance to modification present another challenge in the pursuit of leveraging AI to enhance customer experiences and improve operational efficiency. Integration struggles also persist, especially in the financial sector, where the uncertainty of evolving AI models over time requires a scalable infrastructure. MongoDB's developer data platform future-proofs businesses with its flexible data schema capable of accommodating any data structure, format, or source. This flexibility facilitates seamless integration with different AI/ML platforms, allowing financial institutions to adapt to changes in the AI landscape without extensive modifications to the infrastructure. Concerns regarding the security of customer data, especially when shared with third parties through APIs, further complicate the adoption of innovative AI technologies. Legacy systems can stand in the way of innovation as they are often more vulnerable to security threats due to outdated security measures. MongoDB’s modern developer data platform addresses these challenges with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas , a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Mitigate hallucination of Generative AI by using RAG with Atlas Vector Search, LangChain, and OpenAI Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Sign up for our Atlas for Industries programme to get access to our solution accelerators to drive innovation

January 18, 2024