The Ultimate Guide: AI Document Extraction for Financial Services

There are multiple ways that artificial intelligence (AI) in financial services is reshaping complex processes, data extraction efficiency, and the response to rapidly changing regulations.

According to The Economist, 85% of IT executives in banking have a clear strategy for adopting AI. As a result, we’re seeing a revolution in how financial institutions operate, analyze data, and make strategic decisions. To dive into our exploration of how artificial intelligence is impacting the future of financial services, we will uncover:

• An introduction to NLP, IDP, and Data Extraction with AI
• Solving Key Challenges in Financial Services
• Efficient Loan Processing with AI
• Upskilling for AI: Preparing Your Team for Change
• Navigating the AI Landscape
• AI-driven Investment Strategies
• Case Studies: Successful AI Implementations
• Generative AI (LLMs) and Financial Services

We are seeing a combination of emerging intelligent technologies being applied for the most complex financial services contract analysis tasks. For instance, artificial intelligence can extract data from structured and unstructured sources automatically, while technologies like Natural Language Processing and Intelligent Document Processing can be combined to create an even greater solution.

An Intro to NLP

Natural Language Processing (NLP) is when we teach a computer to understand human language just like we do. When we apply NLP to data extraction for financial services, we open the possibilities for automation and efficiency. By analyzing the sentiment, intent, and key themes of textual data with NLP, technology can extract valuable insights and trends. Now organizations can operate from actionable intelligence for decision-making at a fraction of the time it takes for labor-intensive manual alternatives.

The 6 NLP applications in finance:

• Risk assessments
• Accounting and auditing
• Portfolio selection and optimization
• Extracting insights from unstructured data
• Financial document analysis
• Automating regulatory compliance

An Intro to Intelligent Document Processing

When we refer to intelligent document processing (IDP), we’re referencing a workflow of automation that can scan, read, extract, categorize, and organize documents at scale. As a result of IDP, financial services organizations can extract meaningful information and output accessible formatting from large sets of data.

Example Applications of IDP in Financial Services:

• Regulatory compliance and reporting
• Valuation and benchmarking
• Collateral and loan management
• RWA optimization
• ESG reporting
• CLO, CMBS, RMBS analysis
• Bond analysis
• Asset/fund selection and onboarding
• Portfolio monitoring
• Fund administration and reporting
• Mortgage application review and analysis
• Customer onboarding

Please note, this list of applications is not exhaustive and new use cases are being discovered frequently. The Eigen platform can automate document processing and data gathering activities for a multitude of manual business processes.

Data Extraction and Document Processing

AI-driven automation with data extraction and document processing has driven financial institutions away from unsustainable manual data entry and towards advanced IDP solutions.

AI-powered systems can leverage Natural Language Processing to recognize patterns and understand context within documents while extracting data accurately and efficiently. The manual labor of processing incoming documents is enhanced with AI too; streamlining document classification and sorting. Financial institutions are beginning to understand how their extracted data can be validated against predefined rules for expected formats.

The financial services industry faces challenges that demand innovation, adaptability, and a commitment to meeting consumers' evolving needs. By navigating regulatory complexities and exceeding customer expectations, financial institutions can pave the way for a more secure, compliant, and customer-centric future. Embracing opportunities such as artificial intelligence integration, digital transformation, and strategic partnerships will be crucial.

ISDAs

Eigen’s ISDA Digitization Pack

Navigating the complexities of financial markets regarding the intricacies of International Swaps and Derivatives Association (ISDA) documents is no easy feat. From regulatory reporting to risk management, the accurate processing of ISDA contracts is crucial for financial institutions.

With Eigen's ISDA Digitization Pack – financial institutions can leverage +100 pre-built fields and automate information processing with +98% data accuracy. Eigen's ISDA Digitization Pack also offers a comprehensive suite of tools designed to streamline the processing of ISDA documents, including:

• Automation: Accurate processing of information and insights from ISDA documents
• Remediation: Rigorous data remediation with custom human exception handling workflows
• Extraction: Validation of critical details from ISDA documents with regulatory standards
• Integration: Seamless integration with existing IT systems and databases

Enhancing compliance and risk management for ISDAs

Financial institutions face implementing robust processes to ensure adherence to regulatory requirements and mitigate risks associated with derivatives transactions. AI can enhance compliance and risk management for ISDAs through comprehensive document management, which establishes a centralized repository for storing and managing ISDA agreements. These enhanced processes provide version control, access controls, and audit trails to ensure the integrity and security of ISDA documents.

By coupling AI and NLP technology, the financial services industry can automate analysis steps, monitor regulatory requirements relevant to ISDA transactions, and conduct risk assessments. How?

• Expect +90% OOTB accuracy by using custom confidence targets
• Use pre-trained models or build requirements based on your business needs
• Identify enforceable terms to identify relationships across ISDA Master, Schedule, CSA and amendments

Regulatory Reporting for ISDAs with AI

Regulators have significantly heightened their scrutiny and enforcement of various regulatory mandates, such as Dodd-Frank and BRRD. Consequently, many financial institutions have initiated the digitization of their ISDA documents either in response to regulatory pressure or as a proactive measure to avoid potential penalties and sanctions from regulatory bodies.

Digitizing ISDAs leads to improved regulatory compliance. Through discussions with regulators, there's a growing consensus that 24-hour real-time compliance is becoming increasingly crucial, particularly in times of market volatility. Consequently, leveraging AI technologies such as the Eigen platform may be the most effective method to ensure proper compliance.

When considering a solution for collecting and aggregating vast amounts of data, look for ISDA pre-trained models to get instant access to data from ISDA master agreements, ISDA schedules and CSAs. See how Goldman Sachs leverages Eigen to efficiently analyze their QFC portfolio (including the ISDA family of documents) of 50+ agreement types on an ongoing basis.

From LIBOR to Basel iii, Eigen Technologies plays a crucial role by using its AI platform to analyses and automate the extraction of key data points for regulatory compliance. No matter what laws/regulations exist, our solution can help you access the data you need to meet regulatory pressures.

Loan Processing

Automated Loan Processing with AI

The efficient handling of loan operations process is crucial for sustained success. Facing the challenges of increased deal volumes and complex loan agreements, many leading investment management firms are looking for AI-driven data extraction solutions.

Efficiency through AI and Large Language Models (LLMs):

Organizations are harnessing the power of AI and LLMs to automate the processing of information and insights from credit agreements with over 98% data accuracy. This includes performing data remediation with human exceptions and handling workflows based on custom business rules.

Driving Automation with Eigen's Custom Models

Eigen's no-code solution allows organizations to build custom models on the go. One significant financial investment firm utilized pre-trained models for LMA and LSTA-style documents, incorporating over 100 fields. This adaptability ensures that Eigen's AI solutions align with the organization's specific needs, providing a tailored approach to streamlining operations.

Enhancing Compliance and Risk Management

AI solutions have made it possible to Reduce Risk-Weighted Assets (RWA), improve CCAR/stress testing compliance, and collect complete and accurate data. The technology enables organizations to easily extract, validate easily, and download information needed from agreements, facilitating seamless integration with downstream IT systems and databases.

Using AI for loan processing, you can automate the intake of key data points, such as income, assets, liabilities, and credit history, from unstructured documents. AI-powered models like Eigen’s Credit Agreement Digitization Pack can deploy pre-trained models with +100 data fields covering everything from covenant ratios to transfer provisions. In addition to pre-trained models, you can build custom models to extract data from non-LMA or LSTA bilateral loan documents.

With a pre-trained model solution and +98% accuracy, you can also perform data remediation, extract relevant information, share the data downstream, and improve CCAR/stress testing compliance. AI for loan processing extends the impact to process derivatives contracts, reducing both the cost and time required to identify contracts affected by changes in standards. Financial institutions worldwide are turning to similar AI data extraction solutions to unlock the value trapped in financial contracts.

Enhancing compliance and risk management for loans

Compliance and risk management for loans are among the many financial services industry's challenges. Beyond efficiency, AI-powered decision-making from data analysis helps assess credit risk accurately, while optimizing loan portfolios. By harnessing the power of AI for end-to-end loan management, lenders can strengthen governance, mitigate risks, and drive sustainable growth.

Regulatory Reporting for Loans

The European Banking Association (EBA) introduced guidelines in May 2020 focusing on loan origination, internal governance arrangements, and credit facility monitoring. These guidelines necessitated near real-time data gathering with unprecedented granularity and accuracy. Eigen's AI capabilities proved vital in implementing monitoring frameworks that compile granular credit risk data, ensuring compliance with EBA guidelines and deadlines.

Wealth Management

Efficiency in Wealth Management with AI

Without AI, wealth managers encounter significant inefficiencies across front and back-office operations. However, by leveraging AI technologies, financial professionals can streamline their business.

AI is crucial in automating tasks such as new client onboarding and portfolio allocation, which helps tailor investment strategies to align closely with individual client preferences and financial goals.

Wealth managers rely on AI for their back-office services, including data analysis, to provide actionable insights that can be provided to the client. Repeatable compliance and regulatory reporting tasks are automated, alleviating the need to adhere to government requirements manually.

Regulatory Reporting for Wealth Management

Wealth managers are embracing emerging technologies to free up their time to pay attention to higher-yielding services like purchaser engagements. The operational complexities of evolving regulations are shifting to technology-driven solutions to achieve zero compliance breaches. Now, wealth managers can delegate their tasks of reading IMAs, prospectuses, and statements of additional information (SAIs) to Natural Language Processing tools. On the back end of their responsibilities, financial firms are learning how to use the data they have to feed into large language models to help anticipate potential issues involving exceptions and then identify and correct those issues.

Investment Strategies

AI-driven Investment Strategies

Most trading floors involve high-energy trading execution that takes place by phone or computer; which can be error-prone. It’s incredibly time-consuming for investment firms to review trade agreements for every trade. To make matters worse, the trade details are contained in many types of documents that often require humans to extract the pertinent data manually.

Multiple agencies pour over documents to identify errors and agree on the terms. To optimize the process, banks and financial institutions are finding that they can automate the confirmation process with intelligent document processing.

Now, we’re seeing financial institutions gain cost efficiencies and time savings by reducing the human-driven effort across unstructured documents and the risk involved with missing errors in the trade confirmation process.

Applying IDP to the Confirmation Process

Trading requires an intelligent document processing solution capable of handling unstructured content by extracting relevant data on par with human comprehension. Financial institutions choose platforms like Eigen’s IDP to facilitate the creation of models to automate trade confirmation processes. Eigen’s IDP solution also layers natural language processing (NLP) capabilities to extract identified key terms from documents to be loaded into a structured format.

Using IDP to automate the confirmation process means efficiency and cost savings of upwards of 80% compared to manual processing. Financial services institutions adopting this method for their trade confirmation process benefit from cost and time savings.

Organizations are increasingly navigating the inevitable integration of artificial intelligence solutions throughout their organizations. 40% of respondents in a 2023 McKinsey survey are responding by planning increased investments in AI in their fiscal budgets. The impending adoption of AI requires a comprehensive approach, not just from leadership buy-in but also through carefully executed frameworks by organizational stakeholders for the success of AI transformation and adoption. The year 2024 is expected to witness significant reskilling efforts and the hiring of new resources as organizations adapt to evolving talent needs.

So, how can you successfully implement AI in your organization?

Challenges during implementation depend on organizational maturity levels. Success is achieved through education, defining business objectives, transparent change management, and continuous testing and iteration. Nurturing AI for long-term benefits involves investing time and resources and recognizing its human aspect. It also ensures stakeholders understand its power and collaborate effectively for successful implementation.

How can you successfully and seamlessly implement AI? Follow this eight-step framework or get more details from our blog on AI implementation.

Seven-step framework for successful AI implementation:

1. Embed AI successfully within 1-2 processes

2. Choose a process where you can expect significant results

3. Gather key learnings from the initial implementation

4. Develop the blueprint for future implementations

5. Share your success story with stakeholders

6. Prioritize what to change and when

7. Conduct root cause analysis

AI's impact on talent will result in new work opportunities and transform how tasks are executed across the business landscape. The positive aspect will be AI's potential to assist nearly every individual in their roles, with documented benefits resulting in increased revenue and cost savings. We are facing rapid change, but there’s also a sense of urgency in adopting AI. According to McKinsey's 2023 survey, disruptive changes in industry competition, particularly in technology and financial services, remain a central concern going into 2024.

There will be significant areas to navigate when an organization chooses to implement AI successfully, and these include:

• Assessing the risks associated with AI adoption
• Accuracy of the AI
• What strategic frameworks to use when analyzing the impact of AI on the business

Mitigating Risks with AI in Finance

In navigating the AI landscape, organizations must meticulously consider associated risks while focusing on accuracy. Adopting AI poses challenges in mastering best practices, including machine learning operations (MLOps). Despite these challenges, quick adoption has been instrumental, exemplified by Eigen Technologies. Eigen's AI, chosen by many partners through rigorous vetting processes, stands out for its automation, precision, and subject matter expertise, addressing concerns about risk and compliance.

Preparing Leadership in Finance for AI

Successful AI adoption requires leadership mapping high-value opportunities, focusing on necessary capabilities, and evaluating the efficiencies gained across the organization. Reskilling the workforce is crucial, and the McKinsey survey reported that AI early adopters and high performers were more likely to engage in such efforts. Further, the role changes to support AI ambitions highlight the need for data engineers, machine learning engineers, AI data scientists, and prompt engineering roles.

Implementation Framework for AI

Embedding AI successfully involves a strategic approach. Organizations should start with one or two processes and gather key learnings before expanding. The focus should be on measurable impacts in weeks or months; not years, with subsequent implementations informed by specific challenges and findings. Providing tangible ROI is essential for securing buy-in from senior executives, and each successful implementation contributes to broader organizational education on AI possibilities.

Overcoming Challenges with AI

Internal success stories, both quantitative and qualitative, play a crucial role in overcoming resistance. Based on past experiences, identifying potential challenges during implementation allows for proactive problem-solving. Collaboration with various business units is vital to understanding inefficiencies, opportunities, and customer issues.

Preparing an organization for AI involves technological integration and a holistic approach to talent, risk management, and strategic implementation. As AI becomes integral to business processes, upskilling efforts and thoughtful frameworks will be key drivers of success. The journey to AI readiness requires a blend of technological acumen, strategic planning, and a commitment to continuous improvement. Organizations, especially banks and financial services institutions that embrace this paradigm shift, stand to reap the benefits of enhanced efficiency, improved decision-making, and a competitive edge in the rapidly evolving business landscape.

By integrating AI into their organizations, organizations must understand whether benefits from increased revenue, lower cost, customer satisfaction, or employee experience are anticipated. Through the adoption of AI, the finance industry will yield measurable results in weeks or months rather than years, reaffirming the actual value of artificial intelligence solutions.

Eigen can help you map out a clear return on investment (ROI), so your organization can secure buy-in from senior executives and stakeholders. As you explore the full potential of AI adoption, you’ll want to understand how Eigen's technology can address your specific use case. To begin to develop a blueprint for tailored artificial intelligence solutions. The Eigen team is here to help, to discuss your specific use case, talk to an expert at Eigen today.

As we’ve discussed, technology is paramount in driving efficiency and overcoming complex challenges in financial services. The financial sector demonstrates how a global investment management firm, insolvency lawyers, leading banks, and financial institutions are harnessing the power of Eigen's AI platform.

Investment Management Firm Automates Loan Operations Processes Using Eigen

A global investment management firm managing over $50 billion in assets successfully addressed challenges related to manual loan onboarding and operations. Due to nuanced loan agreements and internal review procedures, the firm faced labor-intensive and time-consuming processes. By leveraging Eigen, they streamlined loan onboarding, achieving efficiency gains and reducing average loan transaction processing time from three hours to one hour while maintaining accuracy. The firm also automated document analyses, improved data re-keying efficiency, and integrated data into other systems, enriching overall business functions. Eigen's capabilities extended to processing derivatives contracts and reducing the time and cost associated with identifying contracts impacted by changes in standards. Now, his customer looks forward to overcoming future process and data challenges.

Gathering Critical Data for a High-Profile Bankruptcy Case

In response to the urgent need for critical data in a high-profile bankruptcy case involving Greensill Capital's collapse, Eigen's technology played a crucial role. Under severe financial distress, the insolvency lawyers enlisted Eigen to compile information from over 10,000 credit and investment documents swiftly. Eigen successfully extracted 22,000+ data points with an impressive accuracy of 98.6%, delivering the required data to the law firm and the end client in under 24 hours. As a bonus, accuracy eliminated the need for additional human review by the law firm.

Automating document processing for Goldman Sachs

In 2015, Eigen Technologies partnered with Goldman Sachs, demonstrating the effective use of AI to analyze and continuously monitor an extensive portfolio of qualified financial contracts (QFCs). Eigen’s solution not only met the requirements of the Dodd-Frank Act but also ensured compliance with various regulations and resulted in 1,500 documents processed straight-through per day, nearly 50 data points routinely collected, and over 50 document types auto-analyzed.

Eigen Efficiently Processes Commercial Bank’s Documents

A leading UK bank utilized Eigen to analyze diverse valuation reports for risk-weighted asset capital relief. The bank faced challenges in analyzing the real estate collateral due to the need for more structured formatting in the valuation reports. Eigen efficiently processed the highly variable document type, achieving the task 10 times faster than manual processing alone. This resulted in a significant boost to the team's productivity, increasing it by 75%.

Leveraging an AI Platform to Meet Regulatory Compliance

Eigen Technologies played another vital role in leveraging its AI platform to meet regulatory requirements for a financial institution. We were tasked with analyzing and automating the extraction of more than 20 data points related to the LIBOR transition. Eigen's AI proved capable of efficiently navigating the changing regulatory landscape.

Eigen's Technologies enable rapid analysis and accurate data, streamlined processes for banks, and partnerships with financial institutions on timely regulations. Leading firms are finding growing confidence in Eigen’s capabilities through the proven results of time and money saved.

Generative AI, as embodied by Eigen's groundbreaking Generative Insights, is reshaping the landscape of financial services. The combination of intelligent document processing and Large Language Models (LLMs) presents a unique opportunity for financial institutions to:

• drive efficiency
• reduce costs
• and unlock valuable insights from their data.

Eigen's commitment to innovation has transformed Generative AI and large language models (LLMs) into its already robust platform, providing financial services with the tools they need to future-proof their business models.

An Overview of Generative AI and LLMs in Financial Services Leveraging Large Language Models (LLMs) in Finance

Eigen’s feature, Generative Insights, empowers businesses to quickly go from raw data to actionable insights by leveraging Large Language Models. It is a powerful tool for structured and unstructured content, including complex financial reports, documents, and emails, into valuable and usable structured data. This transformative feature enables financial institutions to automate manual processes within their workflows, significantly reducing the time spent digging through intricate documents to locate crucial information.

The Synergy of Eigen and Generative AI

Eigen's coupling of generative AI, Generative Insights, is more than just a standalone feature; it represents a seamless integration with the existing Eigen model. By feeding a balance sheet into the Large Language Model, users can layer on Generative Insights to derive specific insights. For instance, asking questions such as "What is the debt-to-asset ratio?" triggers the model to leverage preexisting data extraction capabilities, providing accurate and contextually relevant answers. This rapid and dynamic process showcases the immense potential of combining Eigen's platform with Large Language Models.

Powerful Insights for Financial Services

Generative Insights is a game-changer for financial services, offering a leap in productivity while addressing critical business challenges. The feature enables banks to leverage Eigen in tandem, ensuring faster, better, and more substantial outcomes. The ability to quickly extract and analyze data saves money and delivers superior data quality, enabling financial institutions to make more informed decisions.

How Eigen is Leveraging LLMs

Document input and output complexity present challenges, mainly when dealing with more intricate information extraction tasks. While large language models (LLMs) are a solution, they’ve shown limitations regarding reliability, repeatability, cost, and trust issues. Eigen addresses these challenges by systematically feeding LLMs with reliable prompts, enabling enterprise-grade AI usage through a regulated 'enterprise document RAG.'

By focusing the document and context down to hundreds of tokens instead of hundreds of thousands, Eigen achieves precise answers at a fraction of the cost. Where other solutions deploy two main methods, vector database and chunking, Eigen operates through traditional classification and extraction methods, ensuring efficient, precise, and repeatable outputs. Moreover, Eigen's process ensures AI governance through accountability at every step.

For more info about Eigen’s Generative Insights, check out this on-demand webinar.

What does GenAI mean for the Financial Services Industry?

Generative AI (LLMs) use in financial institutions symbolizes a more significant movement within the financial services industry toward embracing AI from the top down. The phenomenal success of applications like ChatGPT showcases the appetite for AI innovation at the individual level. However, to fully unlock the transformative power of AI, financial institutions must navigate the challenges of top-down adoption, investing in process automation, coordinating employees, and cultivating a culture that welcomes and leverages AI to enable new processes. As the industry embarks on this journey, the integration of Generative Insights stands as a beacon, guiding financial institutions toward a future where AI-driven processes become the norm rather than the exception.

Now that you're briefed on the key challenges that financial services face and the solutions available to meet those challenges, you’re thinking about advancing your journey with artificial intelligence.

Follow these next steps to begin your journey to digital transformation with artificial intelligence:

1. Fill out the form below to access all the resources in Eigen's Ultimate Guide: AI Document Extraction for Financial Services.

2. Get an on-demand demo

3. Find your use case