AI in Record-Keeping

• Blockchain Council
• November 07, 2024

Record-keeping is essential for any organization. In most countries, sectors & industries, it is also legally mandatory to do proper record-keeping. Keeping track of every activity is crucial, but isn’t it tedious? 

Manually categorizing and maintaining records can consume extensive administrative resources. Data entry and review tasks alone occupy up to 50% of administrative staff time in some organizations.

Over it, manual record-keeping doesn’t guarantee that your records will stay safe or none will get their hands on them. And talking about the accuracy of traditional record-keeping, data loss due to human error can reach 95%. 

What if we tell you there is a system that can reduce your time for record-keeping by 90%, increase accuracy to 99.99%, and even lower overall operational expenses by approximately 30-50%?

The solution lies here: Artificial Intelligence. 

So, What is AI?

AI is a technology area focused on creating systems that can perform tasks usually requiring human intelligence. This includes activities like decision-making, pattern recognition, and predictions. In record-keeping, AI shines by managing large amounts of data, organizing and securing them through automation. This process ensures records are quickly classified, searchable, and safe.

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Why Go with AI for Record-Keeping?

Traditional methods of keeping records come with several challenges, like time-consuming steps, limited access, and a tendency for errors. These issues can slow operations and reduce accuracy, making AI an appealing alternative.

Saving Time

Manual records demand a lot of work for tasks like filing and finding information, often resulting in delays and extra labor costs. AI can handle these tasks automatically, organizing and categorizing files with algorithms that work quickly and consistently. This frees employees to focus on important tasks while the system sorts and files, saving time and money.

Boosted Accuracy

In typical setups, errors happen—entries get misplaced, or information is entered incorrectly, leading to inaccurate data and compliance problems. AI reduces these risks using specific algorithms that regularly verify and keep data accurate. Features like natural language processing (NLP) allow AI to read and sort information from text that isn’t neatly structured, helping to keep records error-free.

Avoiding Data Silos

In many organizations, different departments have separate record-keeping methods, which creates isolated data sets that are hard to access across teams. AI addresses this by linking various departments and systems, allowing everyone to access centralized information. With AI, organizations benefit from having one searchable, shared data space, making it easy to pull up information when needed.

Easier Access

Paper files or basic digital storage can limit access to data, especially in businesses with multiple locations or remote teams. AI-powered record systems, often with cloud integration, enable access to information from any location. This makes retrieving records easier and allows teams to collaborate more effectively by providing instant updates on the latest data.

Meeting Compliance Standards

Traditional record-keeping systems may lack strong compliance controls, especially for sensitive information. AI improves compliance by automatically tagging and keeping track of sensitive data, raising alerts for unusual activity, and managing data per current rules. This minimizes the chance of oversights, keeping records safe and properly managed.

Reducing Storage Costs

Storing physical records requires space, and digital files need ongoing maintenance. AI can reduce storage needs by organizing data efficiently and removing duplicates. It also securely deletes old records, freeing up space and reducing storage expenses.

RecordsKeeper AI offers a viable solution to make the most of AI in recordkeeping. The platform integrates AI to simplify and enhance the record-keeping process, reducing manual effort and increasing accuracy. Through AI-powered categorization and retrieval, users experience faster, more intuitive management of records across various formats and sectors.

Critical AI Applications in Record-Keeping

1. Data Extraction and Organization

AI is invaluable in extracting data. Automated data extraction removes the risk of human errors by scanning and pulling details directly from various sources. Advanced AI uses optical character recognition (OCR) and NLP to digitize paper documents and organize them for easy access.

For example, in banking, AI helps generate and verify documents to meet compliance standards. This reduces the workload on staff, allowing them to focus on more complex tasks while keeping document accuracy high.

2. Managing Compliance

In fields where compliance is highly regulated, AI plays a significant role. Financial firms, for instance, are required by the U.S. SEC to maintain specific records. Tools like Microsoft’s Copilot and Zoom’s AI Companion, introduced in 2024, help manage these needs by automatically creating and saving records that meet regulatory rules. This automation reduces the need for manual record-keeping while keeping up with compliance requirements.

Additionally, a survey by InterPARES Trust AI found that almost 46% of organizations using AI in record-keeping see improvements in compliance. Many organizations plan to expand their AI use for this purpose, highlighting AI’s value in navigating complex regulations where records must be well-structured and retained according to strict guidelines.

3. Reducing Errors and Enhancing Accuracy

AI’s ability to reduce errors helps maintain accurate records. In sectors like finance and healthcare, incorrect records can lead to legal issues and unhappy clients. AI reduces these risks by automating repetitive jobs like categorization. As AI systems learn from past data, they improve in spotting and fixing errors, making them highly reliable in fields where accuracy is critical.

For example, Docsumo’s AI-based tools for financial record-keeping offer automated data extraction, sorting, and error-checking, greatly increasing record reliability. Machine learning helps these systems recognize patterns and detect anomalies, meaning human supervision is only needed in rare cases, lowering costs overall.

4. Predictive Insights and Strategic Planning

AI in record-keeping isn’t just about storing information; it also provides insights for making informed decisions. Predictive analysis helps organizations spot trends and plan for the future, using past data to make educated predictions. Financial firms rely on this to plan cash flow, predict market trends, and fine-tune investment portfolios.

This feature is precious when strategic decisions depend on accurate predictions. AI-driven insights in record-keeping help companies anticipate needs and avoid unexpected risks. According to a McKinsey survey from 2024, companies using predictive AI across various functions report notable revenue growth, thanks to the foresight AI offers in areas like inventory and supply chain planning.

Examples of AI Solutions in Recordkeeping

AI has transformed recordkeeping with tools designed to streamline records collection, storage, and retrieval, especially for organizations managing high volumes of sensitive data. Below are some essential AI-driven tools that enhance recordkeeping across industries:

Microsoft Copilot in Financial Recordkeeping

Launched in March 2024, Microsoft introduced Copilot within its Microsoft 365 tools, acting as a digital assistant for documentation tasks. Copilot helps users organize, summarize, and manage records across apps like Word, Excel, and Teams. For example, Copilot drafts and summarizes text in Word, which is especially helpful for consistent documentation. In Excel, it organizes data by performing analyses, enabling users to interpret information efficiently. Meanwhile, Copilot captures meeting notes, action points, and reminders in Teams, simplifying how teams document and track collaboration. By automating these actions, Copilot reduces manual errors and speeds up documentation.

Zoom AI Companion for Team-Based Recordkeeping

In May 2024, Zoom’s AI Companion received updates to support data integration with other platforms. This feature lets users create summaries, assign tasks, and save meeting notes. This tool is a valuable asset for businesses that need to archive sensitive data from meetings, especially those with regulatory demands. Following data retention guidelines, Zoom’s AI ensures any shared information meets archival standards.

IBM Watson Discovery for Data Retrieval

IBM Watson Discovery is crafted to manage extensive data collection, making it suitable for sectors needing both structured and unstructured data. With natural language processing, Watson Discovery scans extensive records, extracting relevant information quickly. This helps teams with compliance and audit tasks by providing quick, accurate access to necessary records. Centralizing and categorizing data with Watson Discovery means less time spent searching through massive databases, enhancing efficiency and accuracy.

Docsumo for Document Management

Docsumo utilizes AI to streamline document handling, automating data extraction from various documents. With OCR and machine learning tools, Docsumo digitizes records, making it easy to search, retrieve, and organize files. This is especially beneficial in compliance-heavy sectors, where data accuracy and accessibility are essential. Docsumo also automatically tags and organizes documents, making it easier to meet industry standards.

Dynatrace’s Davis® AI Engine for IT Recordkeeping

The Davis® AI Engine from Dynatrace aids in tracking and managing data in IT environments. Its features include automated detection of anomalies and analyzing root causes, creating logs that serve as historical records for system performance. For IT teams, this continuous log is vital for troubleshooting and ensures compliance with data management standards. By automating log captures and data organization, Davis® reduces the time IT professionals spend manually updating records and allows them to locate needed information quickly.

Copilot4DevOps for Project Documentation

Designed for use within Azure DevOps, Copilot4DevOps simplifies project recordkeeping by tracking project tasks, timelines, and regulatory documentation. It automatically produces compliance reports, helping teams monitor project progression and maintain accurate records. This is particularly useful in software development and engineering industries, where maintaining precise project documentation is essential to meet industry standards.

iGTB Copilot in Banking

iGTB Copilot supports banking professionals by automating the handling of financial records. This AI organizes past payment data, creates summaries, and provides insights that help with compliance and reporting. In banking, where accurate transaction records are required, iGTB Copilot makes it easy for teams to manage records and retrieve transaction histories as needed for audits or client relations.

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Building an AI-based recordkeeping System

Setting up an AI-powered recordkeeping system involves multiple steps to ensure efficiency, accuracy, and compliance. Here’s a guide to get started:

Define Goals and Requirements

Start by identifying your organization’s recordkeeping needs. Consider necessary features like search and data security, plus any legal standards. These initial steps will guide the choice of AI tools suited to long-term objectives.

Collect and Organize Data

Gather all relevant records, such as digital files and scanned documents, and prepare them by organizing and cleaning up data for better accuracy. This organization allows AI tools to handle records more effectively, improving retrieval and compliance.

Choose Suitable AI Tools

Select AI tools based on your objectives. Platforms like TensorFlow and Keras support record processing, while cloud services like AWS and Google Cloud provide the infrastructure to handle large data volumes.

Develop and Train the Model

Use machine learning models suited to your data, such as NLP for text or image recognition for scanned documents. Training with labeled data helps refine the model’s ability to categorize and organize records accurately.

Test and Validate the Model

Conduct tests on the AI model using metrics that check for reliability and accuracy. Cross-validation confirms the model can manage various data scenarios, catching issues before full implementation.

Integrate with Existing Systems

Ensure the AI solution can work with your current recordkeeping software. Seamless integration helps centralize data, providing easy access to records through one interface.

Continuous Monitoring and Compliance Checks

Set up monitoring to track the AI system’s accuracy, security, and compliance with regulations. Regular checks reveal necessary adjustments as rules evolve, such as those from GDPR or CCPA.

Train Staff for Effective Use

Train team members on the AI system’s functions and compliance features. When staff understands how to use the system effectively, it boosts adoption and enhances productivity.

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Compliance in AI-Based Financial Recordkeeping

For finance, recordkeeping is governed by strict regulations. For example, U.S. broker-dealers must comply with SEC’s Rule 17a-4, which mandates keeping records of all business communications. Tools like Microsoft’s Copilot and Zoom’s AI Companion can create and manage documentation across platforms, but they must be set up to meet SEC’s record retention standards.

In 2024, the SEC tightened its focus on AI-generated records, requiring compliance for any content generated, shared, or communicated through email or chat. Companies using AI tools, such as Microsoft Copilot, must ensure records meet regulatory standards, especially if client or business-critical data is involved.

The EU’s upcoming AI Act is also set to impose strict rules for documenting AI use, which will impact global businesses, particularly in data-sensitive sectors like finance. This act stresses transparency and high standards in recordkeeping, pushing companies to align their AI policies with these regulations.

Challenges in AI-Driven Recordkeeping

Implementing AI in recordkeeping isn’t without challenges. Data quality remains critical, as AI relies on clean, consistent data. According to a report from 2024, data bottlenecks are expected as companies face difficulties in sourcing, cleaning, and annotating data. These challenges highlight the need for quality data handling and partnerships to meet the demands of complex AI systems.

Privacy is another concern with AI-based recordkeeping. Systems that handle sensitive data must adhere to strict security standards, especially as privacy laws become more rigorous worldwide. Additionally, human oversight is essential to ensure AI meets accuracy and compliance standards. This “human-in-the-loop” approach is vital in fields dealing with sensitive data, as AI alone may not meet every regulatory requirement.

Latest Trends and Future of AI in Recordkeeping

The world of recordkeeping is experiencing a transformation with AI, making data management faster and more accurate than ever before. AI offers new ways to manage records efficiently, from automating document sorting to advanced predictive insights. Here’s a look at the latest AI-driven trends in recordkeeping and what the future holds.

1. Automating Tasks with AI

AI has become a powerful tool for record management, especially in handling tasks that usually take a lot of time, like sorting, tagging, and organizing documents for storage. New AI tools now use methods like “fingerprinting” and language analysis to spot patterns within files, which makes sorting automatic and helps cut down on manual effort. This technology is beneficial for industries with strict rules, like healthcare, law, and finance, where accurate and timely recordkeeping is critical.

2. Organizing Data with Natural Language Processing (NLP)

NLP, which deals with processing text, is proving valuable as businesses handle massive amounts of text-based data, such as emails and scanned files. By pulling out essential details and automatically organizing documents, NLP makes it easier to find and manage records. Optical Character Recognition (OCR), a specific type of NLP, helps convert paper records into digital files, making them easier to search across different systems while keeping data organized and accessible.

3. Improved Cybersecurity

As more records go digital, securing them has become more critical due to rising cyber threats. AI-driven security measures, such as automatic monitoring and data encryption, help keep sensitive information safe from unauthorized access. AI can even detect early warning signs of data breaches, which supports privacy standards under rules like GDPR and CCPA that require companies to handle data securely. This proactive security keeps private information better protected.

4. Adapting to Remote and Hybrid Work

As work environments become more flexible, with many people working remotely or in hybrid settings, AI-powered recordkeeping systems now offer secure access to documents from anywhere. This setup lets teams work together smoothly, without risking data security, even when they’re not in the same place. Such adaptability helps ensure that organizations meet compliance standards while supporting modern, flexible work setups.

5. Predicting Data Needs for Better Planning

Another emerging use of AI in recordkeeping is predictive analytics, which helps organizations plan for future data needs. By analyzing past document use and retention patterns, AI can suggest storage timelines and identify which records can be securely deleted. This proactive approach not only aids in meeting compliance standards but also cuts down on storage costs by identifying no longer necessary files.

6. Managing Big Data and Integrating Systems

As organizations gather more data, AI helps manage this vast information more effectively, extracting valuable insights to guide decision-making. Integration with systems like CRM (Customer Relationship Management) and ERP (Enterprise Resource Planning) improves the data flow between recordkeeping and other core functions. This approach helps streamline operations and maintain consistent information across the organization, supporting a unified system.

7. Using Blockchain for Added Security

Blockchain technology is gaining interest as a way to improve transparency and accountability in recordkeeping. It allows for permanent, unchangeable records of document activities, which boosts trust in data accuracy, especially in fields that require clear proof of record history. With its secure, tamper-proof design, blockchain offers an added layer of safety that works well with AI’s data management abilities.

8. Enhanced Compliance Tools for Financial Fields

As more organizations adopt AI, regulatory bodies like the SEC have updated rules to ensure that AI-generated records meet legal standards, especially in finance, where accurate records are crucial. The new guidelines encourage companies to monitor AI-generated data closely, preventing the misuse of private details. For instance, tools like Zoom’s “Ask AI Companion” now include options for pulling information from third-party sources, following strict compliance to ensure records are stored correctly.

Final Thoughts

AI improves recordkeeping by making data handling faster, more accurate, and more secure across various industries. Traditional records management methods demand a lot of time, involve heavy manual work, and are often prone to errors. In contrast, AI-powered solutions offer automatic processes that help cut down on time and reduce costs significantly.

A significant part of keeping recordkeeping ethical is by making AI decisions transparent. Models of explainable AI (XAI) help clarify these decisions, which is valuable for audits and regulatory checks in organizations where accountability is required. Combined with human supervision, these systems allow for responsible records management, particularly around privacy, security, and data storage rules.

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