Data Action Layer March 27, 2024

Revolutionize your data management: how ML transforms data operations

by Bethany Walsh

Website Patterns vs Templates

As financial services firms grapple with the challenge of extracting meaningful information from unstructured data, the shift toward using automated document processing platforms provides more efficiency, adaptability, and scalability.

However, not all workflow automation platforms are the same. Many rely on creating templates for each document type a firm regularly processes. Software that uses document templates limits the full potential of an automated workflow.

Leveraging a platform that uses the latest AI and machine learning advancements to understand a document, even if it has never seen it before, can revolutionize your data management processes.

The limitation of templates

Templates require uniformity, a condition seldom met in the real world, where documents come from countless sources, each with its own format and structure.

Operations and technology teams often find themselves in a bind, extracting what information they can from disparate documents and attempting to impose some semblance of order. Relying on clients, counterparties, and prospects to adhere to specific formats is both impractical and inefficient, leading to processes that are difficult to scale and prone to error. If one fund adds a new data field or changes its document structure and the template stops working correctly, you’re delayed in capturing critical data.

As your firm grows, your workflows become increasingly complex. Template-based processes can't adapt and ultimately break down.

Transform workflows with machine learning

Advanced AI and machine learning allow standardization at the data level rather than the document level. AI enables software applications to understand context more deeply, making them more adept at categorizing and locating information regardless of the document's format or whether it has seen a particular document before.

This process avoids the fragility of templates for the flexibility of machine learning models. Alkymi uses machine learning models to power our automated workflows for specific document types, called Patterns. Each model is trained on hundreds or thousands of diverse examples of a given document, teaching the model to recognize the required data fields across differing formats. Pre-set rules specify how data is extracted, transformed, and validated, including which data fields are required from a document, how those fields are validated, and in what format the extracted data should be presented. Patterns can be tailored to meet specific data extraction needs without building a template that can become obsolete with any workflow change. The Pattern will find the exact data points you need inside forms, text, rows, table cells, and columns.

Find the right Patterns

Clients can identify what types of Patterns work best for them. Some Alkymi clients choose from our existing Patterns, ranging from alternatives and asset management to data operations. Because these Patterns use pre-trained machine learning models, they are built to capture key data fields from core documents and transform unstructured documents into validated, actionable data.

Many clients require Patterns to be configured to meet their workflow needs. This means that existing Patterns are augmented with additional fields, formats, ways to validate data, and methods to transform and categorize that data into insights. Alkymi re-trains its existing machine learning models with those new requirements.

Finally, some clients want bespoke Patterns built from scratch. For them, Alkymi builds custom machine learning models with tailored fields and formats and trains them solely on proprietary client data.

Whatever the use case, our API and integrations offer data ingestion and export capabilities, allowing for connection to any email inbox or downstream database. Alkymi supports over 25 file formats, provides flexible deployment options, and is equipped with enterprise-grade security.

Advance your workflow automation

The transition from templates to true machine learning is not merely a change in technique; it represents a fundamental shift in how financial services firms approach data management. Machine learning-powered workflows offer a scalable, adaptable, and efficient solution that aligns with the dynamic nature of today's data-driven business environment.

By implementing Patterns, firms can enhance their operational efficiency, reduce the risk of errors, and ultimately gain a competitive edge. The future of data management lies in embracing flexibility and innovation.

As the data landscape continues to expand and diversify, the limitations of traditional templates become more apparent. Patterns offer a sophisticated, technology-driven solution that addresses these challenges and paves the way for future-proof data management processes.

The shift from templates to machine learning-powered workflows marks a revolutionary stride in workflow automation. ML-based processes are especially useful for financial services firms that grapple with extracting meaningful information from unstructured data. Empowered by the latest in AI and machine learning, Patterns offer a scalable, adaptable, and efficient approach to data management, transcending templates' rigid and error-prone nature. This approach dramatically streamlines workflows and provides firms with a sustainable competitive edge.

More from the blog

February 22, 2024

Quarterly Statements Rule: 3 ways to ensure compliance

by Elizabeth Matson

The transition window for the SEC's new Private Fund Regulations is well underway. AI-powered workflows will help you stay compliant and improve efficiency.

January 18, 2024

Speed up CIM review with LLMs

by Elizabeth Matson

Reviewing a stack of CIMs can be painful, but it doesn’t have to be. Here's how we redesigned a CIM review workflow using large language models.

December 20, 2023

2023: A year in review

by Harald Collet

2023 was a monumental year for Alkymi. Take a look at our top data points from the past year.