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How to automate annuity statement processing using OCR and Impira AutoML

Downloading PDF annuity statements from various carriers and retyping the data into Excel is a tedious and time-consuming process. This guide describes how financial advisors can set up Impira AutoML to convert all of their fund documents into a spreadsheet instantaneously.

Download sample files

Using automated machine learning (AutoML) for annuity statement processing

Financial advisors, RIA firms, CFP practitioners, or anyone who provides investment guidance is familiar with the multitude of documents that must be managed on behalf of their clients. Carriers typically provide a web portal where statements can be downloaded as PDF files. From there, the financial information (e.g. beneficiary name, gain/loss, beginning and ending values, etc.) must be manually re-keyed into a spreadsheet so that the information can be used for forecasting or sent on to a CRM system.

In the past, brokers have attempted to use optical character recognition (OCR) technology to expedite the process, but found that the results are less than satisfactory. Today, however, advancements in machine learning technology make it possible to cleanly extract the data.

Impira AutoML builds on the vision of OCR technology by applying an additional layer of intelligence to the data capture process. The system is designed to reduce the number of errors that traditional OCR generates by allowing users to teach the AutoML to learn from its mistakes, quickly and continuously improving. Our easy-to-use interface mimics the look and feel of a spreadsheet, which means that anyone can use the tool and immediately realize productivity gains.

With Impira AutoML, you can automate your financial statement processing in less than five minutes, and scale the number of clients that your firm can manage.

Business ROI 

More accurate than OCR, Impira AutoML makes it easy to convert annuity statements into a spreadsheet format. This means that financial advisors can dedicate more time to advising, and spend less time manually copying and pasting information from one document into another. The ability to work with a wide range of carrier documents and the ability to adapt to changes in those documents over time will ensure that brokers are working at their full potential. Delight your clients with fast turnaround times, and increase the number of clients your firm can manage. Get started with Impira AutoML today and experience how easy it is to automate your document-heavy processes.

How to set up Impira AutoML with annuity statements

Step 1: Sign up for a free Impira account

Navigate your browser to to create your free account. Impira provides up to 200 file units (pages) for free. Impira won’t ask for your credit card number, and there is no time limit for your evaluation.

screenshot of Here you can sign up for Impira AutoML, machine learning for your documents

Step 2: Create a new collection

Once you’ve created your account, go ahead and create your first collection. To do that, click on the ‘+’ symbol on the left-hand sidebar.

Screenshot of + button for creating a new collection.

In the prompt, give your collection a name, like “Stark Statements.”

Screenshot of the "Create collection" model where you name your collection before uploading it to Impira's smarter OCR platform

Step 3: Upload your statements

Let’s upload some sample statements into your account:

1. First, make sure that your collection is selected.

Screenshot of where to select your collection.

2. Drag and drop your files onto the page.

Animated gif on how to drag and drop your files into Impira.

Step 4: Train an AutoML model on your statements

Notice that your uploaded files have arranged themselves into a table, and each row is a different file. So far, our table only has one column (“File”). When you train AutoML, the fields that you extract will be displayed as additional columns on this table. Let’s open up the first file to begin training the AutoML.

Extracting initial data from your forms

1. Double-click on the thumbnail icon of your first file to open the mark-up interface.

Animated gif of how to open a file within Impira.

2. Let’s start by extracting the Investor Number. Click and drag to highlight the number. On the right-hand sidebar give the field a name (e.g. “Investor Number”) and click the “Add Field” button.

Animated gif of how to extract data from your file using OCR and machine learning.

3. Close the mark-up interface by clicking the ‘X’ in the very top-right corner.

Screenshot of where to close the mark-up modal.

4. This will return you to the collection view, where you’ll see a new column in the table for “Investor Number.”  In the column header, the spinning indicator informs you that the AutoML is actively learning as you label items. It’ll then immediately make predictions for the Investor Number across every document in the collection.

Animated gif of Impira AutoML training the unique machine learning models and the Impira table filling in predicted extractions.

Notice that some of the cells may initially be blank, or that a red flag is displayed on the left-hand side of the cells. The flag color is a visual confidence indicator of Impira AutoML’s prediction: green means most confident and red means least confident. Because Impira AutoML is a continuously learning system, you can boost these confidence levels by providing additional feedback, like clicking and confirming fields.

Screenshot of the orange flag (an indicator that the ML model predictions need review)

Revising your AutoML models

Double-click on a cell displaying a low confidence indicator to reopen the mark-up interface. You’ll see a red dotted box around our prediction. If our prediction is correct, click on the checkmark to confirm it. If it’s incorrect, click the pencil icon and highlight the correct answer.

Screenshot of what it looks like once you open a document to review the extracted data.

Every time you validate or correct a prediction, the AutoML will update again and provide a fresh set of predictions. With just a few of these interactions, the flags will start to turn green, indicating high confidence.

Screenshot of green predictions meaning the ML model has come back with a high confidence predicton.

Repeat with more fields

Repeat the above process to create additional columns for all of the fields you’d like to extract (e.g. End Date, Beginning Balance, etc.).

A filled in Impira AutoML table.

Now that your collection is configured and performing well, you can upload additional statements to the collection and see them added on as new rows. The AutoML fields that you’ve already defined will automatically apply to the new files.

Step 5: Export your data

When you are satisfied with the results, you can easily export the data from Impira. To download the data as a CSV file, click the “Download” button in the upper right-hand corner. In the drop-down, select “All files records (CSV).”

Screenshot of the two methods for downloading.

Alternatively, many customers choose to configure programmatic workflows with Impira. Impira offers pre-built connectors with many common systems such as Dropbox and Amazon S3, as well as a robust set of REST APIs.

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