Automation and Machine Learning in Finance

The financial services industry is on a transformation journey. Forward-thinking financial services organizations realize the potential of digital transformation to reduce costs, make better use of existing resources, and drive better business outcomes.

On the journey to transformation, however, there are some bumps in the road. For financial services, a large volume of highly variable documents and the resulting unstructured data they represent is a significant hurdle to jump.

Routinely, financial services organizations deal with thousands upon thousands of documents of all kinds, coming from disparate sources such as emails, faxes, postal mail, client-facing portals, and more.

For many organizations, the only way to process these documents is via manual review. In reality, manual review is becoming increasingly cost-prohibitive. And even in the best of scenarios, the sheer volume of documents makes manual review unsustainable as organizations scale.

Then there’s the cost in terms of customer experience to consider. Even if organizations manage to review all their document manually, the time it takes to do so puts them at a distinct disadvantage in the area of customer experience. Today’s consumers expect immediate responses, and those financial organizations furthest on their transformation journey are more likely to be able to deliver them.

Financial services organizations cannot simply ignore these challenges if they hope to remain profitable and provide good customer experiences at the same time. In an effort to revolve these issues, the financial services industry has turned to document automation.

The Evolution of Automation in Finance

To be clear, for many years, the financial services sector has been somewhat reluctant to adopt document automation. In addition to being somewhat conservative by nature, the industry is also all too well aware that document-understanding technologies, until recently, have been lacking in both flexibility and accuracy.

Template and Rules-based Technologies

Some of the first document automation technologies included solutions that were template-based or rules-based. These technologies work well for structured documents, which adhere to strict formatting (ie. static form fields). However, template-based and rules-based solutions simply cannot accurately process unstructured documents. That makes them largely unsuitable for financial services, where accuracy is essential.

OCR Technology

Optical character recognition (OCR) technology has also been used for document automation, but it, too, is limited in what it can accurately interpret from a document. For financial services, more is needed.

Machine Learning and Deep Learning Technologies

Today, machine learning technology and a subset of machine learning known as deep learning enhance the document automation technologies of the past, improving accuracy and processing both structured and unstructured documents at a fraction of the time and expense it takes to manually review documents.

Deep learning leverages natural language processing (NLP) technology along with machine learning models trained on thousands of document types to quickly ingest, split up and classify, and extract key data from documents and then automatically send that data to the appropriate downstream systems for use.

Why Deep Learning Is the Next Step in Automation and Transformation

Deep learning is far superior to previous technologies used for document automation. It is an advanced subset of machine learning inspired by the human brain and is like a brain simulation built from a large, deep neural network. Deep learning models are trained on a huge amount of data, and they autonomously learn and solve highly complex problems the way a human brain might, but at much higher speed.

That fundamentally changes the way financial services organizations approach document automation. Document processing solutions that use technologies such as deep learning and natural language processing can interpret highly variable documents such as contracts, financial statements, credit reports, correspondence, and even handwritten documents with a high degree of accuracy that previous types of document automation solutions simply could not process.

For more information about how deep learning works for document automation, download your free copy of “The Ultimate Deep Learning Guide for Unstructured Data.”

How Instabase Uses Automation and Machine Learning for Finance

With a depth of understanding and experience with the financial services industry, Instabase is uniquely qualified to help apply the power of machine learning, deep learning, and automation to financial services workflows and data.

The Instabase Automation Platform for Unstructured Data leverages deep learning technology and natural language processing, combined with low-code building blocks to help financial services organization ingest, classify, split, enrich, validate, and push out relevant data to downstream systems in an accessible structured format.

With Instabase, financial services organizations can:

  • Eliminate or greatly reduce most of the costly, tedious manual review of unstructured documents.
  • Extract relevant data from unstructured documents with a high degree of accuracy.
  • Speed the delivery of excellent customer experiences by eliminating process bottlenecks.
  • Scale quickly, adding capacity for more business without adding associated employee costs.

Learn how financial services organizations use Instabase for document automation in The Big Book of FSI Use Cases and our financial services solution page.