Invoice Capture: In-depth guide to most firms’ first AI purchase

Invoice capture is a growing area of AI where most companies are making their first purchase of an AI product because it is an easy to integrate solution with significant benefits.

While digitization helped automate numerous processes, mostly rule based software was used in digitization. However, invoice capture involves both reading the invoice text with Optical Character Recognition (OCR) and understanding its context with machine learning.

What is invoice capture?

Invoice capture (also called invoice capture, invoice data extraction or invoice OCR) is extracting structured from invoices so invoices can be automatically processed.  Invoice capture has been the first back office process to be automated with AI for most companies. Read more

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Invoice Automation: First Process to Automate in 2019

Invoice automation (also called automated invoice processing) is a maturing area of automation with limited implementation risks and significant benefits. Invoice automation would free up back office finance/procurement teams to focus on higher value added tasks.

What is invoice automation?

Invoice automation allows straight through processing (no human interaction) most of the time for the entire invoice process. Invoice automation involves

  • monitoring for invoices: Invoices arrive in companies as PDFs, image files and increasingly rarely as hard copy documents.
    • For digital invoices, an RPA bot or a simple email automation tool can flag emails with invoices and forward them for data extraction. Some companies use a dedicated email address for invoices to further simplify invoice monitoring.
    • For hard-copy invoices, companies are switching to using a single address to centralize invoice scanning
    invoice capture: Extracting relevant details (e.g. bank account, ordered item) from the invoice. If software does not have confidence in the results, it is sent to employees for a manual check. evaluating invoice against order records and other criteria to ensure that the payment is indeed a valid one. Evaluations include
    • cross-checking invoice against purchase orders
    • cross-checking invoice for duplicity
    • using working capital optimization policies to decide payment time
    • using limits to to decide whether to manually process invoice. Invoices that are abnormally large compared to a suppliers’ usual invoices may need to be manually verified to ensure that wrong payments are not done
    recording invoice-related information in systems making the necessary payment to settle the invoice

    All steps except invoice capture are rule-based processes. However, invoice capture relies on machine learning to extract the data in the invoice. For more, please read our article on invoice capture. Read more

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Fraud Detection: In-Depth Guide [2019 update]

Fraud detection is a major challenge for merchants that accept electronic payments, acquirers that manage electronic payment networks and for banks that are exposed to various types of financial fraud including money laundering.

Which companies are impacted by financial fraud?

Banks and other companies that receive significant number of financial transactions are at risk of suffering from financial fraud. e-Commerce companies, credit card companies, electronic payment platforms, B2C fintech companies all need to employ software to limit financial fraud. Read more

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15 Fraud Detection Software: A Complete List [2019 update]

We wrote extensively about fraud detection before. If you believe that your company needs a leading edge fraud detection solution, we list the major fraud detection vendors below.

Fraud Detection Companies Ecosystem

Tech giants

Tech giants like Experian are providing end-to-end fraud detection solutions. They have the advantage of having a large sales force and long term relationship with their customers and they have significant budgets to create competent solutions

AI Startups

Since 2000s startups are tackling various aspects of fraud. The first area of focus was to serve merchants which are easy to sell to as they are smaller e-commerce businesses. Later, with increased commercialization of AI, AI vendors started selling to merchants, acquirers and banks. Read more

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Manufacturing analytics: $6T opportunity [2019 update]

There has been sustained excitement about IOT and the data capabilities it will bring to industry. In 2013, McKinsey&Company had predicted that IOT would bring $3-6T economic impact in 2025.

With margins getting smaller and competition getting more intense, manufacturing companies are getting smarter about how they can become more productive in order to become more profitable. Manufacturing analytics is one of the most effective ways to do so.

Sometimes called the 4th industrial revolution, manufacturing analytics analyze the historical performance data of machines in order to forecast their future; and their failure. And with technology and the environment to support it evolving rapidly, there has never been a better time to get started. Read more

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