Finance processes often generate a wealth of documents, including legal contracts, purchase orders, and licensing agreements, among many others. Manually reviewing these documents for errors or potential issues in their various formats can be time-consuming and prone to mistakes caused by fatigue and repetition. Using automated document parsing coupled with machine learning techniques can significantly reduce the burden for manual reviewers across a variety of use cases.

Benefits of automated document parsing and machine learning pipelines

Building an automated document processing and machine learning pipeline can seem daunting, but there are many benefits to investing the time and resources required to develop a solution.

These benefits include:

• Reduction in the number of documents needed to be reviewed by a human being, often by an order of magnitude
• Automated prioritization to flag and review the riskiest or most error-prone documents first
• Reveal patterns or trends through unsupervised learning techniques not previously known to subject matter experts
• Provide decision support to key personnel and stakeholders

All these benefits have the potential to save money by reducing human error, increasing efficiency, and generating valuable business insights.

Steps to implement a solution

At Neal, we typically recommend following several basic steps to build an end-to-end document analysis and machine learning pipeline regardless of the document type or specific use case. These steps can be adapted to your situation and needs and may require more or less effort depending on the problem scope.

Step 1: Understand context and business impact

Whether the process is new or used to augment an existing process, understanding the end goal and what factors influence the result is vital to building the pipeline. It is essential to work with subject matter experts to understand how the end-product could be integrated into existing processes and used to enhance workflows. Understanding the context and impact will shape how the machine learning models leverage the documents and incorporate the results into the workflow.

Step 2: Identify key features and extract them into structured datasets

After identifying the key outcomes, focus on what aspects of the documents influence these outcomes and how they represent them. These features are what you will want to extract from the document and structure so that a machine learning model can use.

Examples of key features include:

• Financial figures or tables embedded in a body of text
• Specific keywords or phrases present (or missing) within the document
• Statistical characteristics of a document (word/page count, table count, etc.)
• Adherence to any existing business rules or logic sets

Once a given organization identifies these key features, it can evaluate how best to store the information in a structured dataset. One row could represent a single page of a document or an entire document. The organization can also aggregate data at different levels by using multiple tables. However you decide to organize the features, make sure the structure is consistent and applies to all documents used in the process.

Finally, extract the document contents into the structured datasets using available tools, such as the optical character recognition (OCR) text extraction capabilities from Microsoft’s Cognitive Services or open-source options available in Python. This step will likely require several adjustments depending on the complexity of the documents and the scope of the feature set.

Step 3: Train machine learning model on structured data

Once the documents have been extracted and organized into a structured dataset, you can build a machine learning model to address your specific use case. If your solution incorporates supervised ML models and requires a set of labeled training data, ensure this is captured in the previous step and incorporated into the structured datasets when parsing the documents. Unsupervised techniques such as document clustering or topic analysis can be helpful when working with more text-based features.

Regardless of the modeling technique, it’s important to frame your model and the output it generates within the context developed while understanding the use case. Framing models in this fashion helps to ensure the machine learning model’s output meets the needs of your stakeholders and provides value to the process.

Additionally, working with subject matter experts throughout the modeling process can help build confidence in the model’s capabilities and accelerate adoption and integration into existing processes.

Example use cases

The following are a few selected examples of how automated document processing with machine learning can provide value across many different departments and functions within an organization:

Identify input errors on purchase orders or expense reports

• Check for duplicates or near-duplicates already in the system
• Identify suspicious trends or habitual misuse

Evaluate risk on legal documents

• Review financial figures for discrepancies or anomalies
• Scan for problematic phrases or missing legal clauses

Bank statement and transaction reconciliation

• Match posted bank statement transactions with sales or expense receipts
• Reconcile paper or hand-written records with digital statements

Any process requiring form review, whether by human reviewers or through a set of business rules, can potentially benefit from adding machine learning into the document review process.

Summary

Incorporating machine learning-based automated document processing into new or existing workflows that rely on human review can significantly reduce the number of documents employees need to review manually. It can also help increase throughput and enable faster turn-around times for reviewing documents while saving money by reducing human error. By working through a few key steps, your organization can begin to realize these benefits within your current workflows.

What use cases can you identify that might benefit from automated document processing?

Learn more

Here are a few more resources to help you learn more about applying machine learning solutions in finance:

• Our modern finance solutions are built to help organizations improve planning and forecasting, ensure compliance, and enable greater efficiency
• You can also check out my previous blog on machine learning forecasting and forecasting automation