AI in Accounts Receivable

Delivering greater speed & strategy to the entire invoice-to-cash cycle

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Esker Synergy AI

A discrete, digital assistant
supporting your AR team

Esker Synergy AI is a set of AI capabilities (deep learning, machine learning, NLP, etc.) and other intelligent tools (RPA, OCR, etc.) that powers Esker’s Accounts Receivable solution suite.

In practice, Esker Synergy acts as a digital assistant for your AR team by facilitating daily activities and providing AR managers with predictable insights to help them anticipate issues and make informed business decisions. It works by providing three specific capabilities:

  • Payment predictions

    Using predictive analytics, Esker Synergy predicts when payment will likely arrive based on previous invoices, payments history and other customer data — improving collections efficiency and visibility over incoming cash and AR performance.

  • Recommendations

    Ever wish you knew what the optimal action was to take in any scenario? Now you can with Esker Synergy! Thanks to prescriptive analytics, you and your team can facilitate better decision-making and accelerate the entire invoice-to-cash cycle.

  • Data extraction

    Esker Synergy automatically captures data from all AR documents and uses it to carry out critical actions (e.g., routing the document, matching a payment to an invoice, creating a claim, etc.). This removes the burden off the AR team and optimises your invoice-to-cash automation efficiency.

What our customers say...

“I absolutely love risk level prioritisation on the system, it helped us drive our decisions every day and empowered the team members to work flexibly and autonomously."

Ruth Birkin, Credit Manager with over 15 years of experience

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“Esker has significantly reduced processing time for large remittances with 800+ line items from 2+ hours to mere minutes."

Mozima Mohammed National Credit Manager, Fletcher Steel

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How Esker Synergy empowers every AR stakeholder

One of the biggest benefits of Esker Synergy is its expansive reach — although all AR stakeholders have their own unique pains and processes, they all benefit from the AI-powered capabilities.

  • Credit analysts

    • Collect, combine & weigh all the internal & external data required to assess customers’ credit risk
    • Suggest a risk category to assign to a customer according to their situation & credit rules/policy
    • Suggest credit limit to assign to a customer according to their situation & credit rules/policy
  • Cash application & deductions specialists

    • Route inbound remittances & claims to the solution
    • Split batches of remittances or lockbox files
    • Extract data from remittances & deductions supporting documents
    • Find the right matching to auto-allocate a payment among multiple open items combinations
    • Recommends allocation suggestions when auto-allocation is not possible
  • Collections specialists

    • Provide payment predictions (per invoice & reports) & customer-estimated risk level
    • Prioritise collection calls by risk level (critical/high/normal)
    • Suggest deferring a call when payment is expected to arrive within the next 3 days
    • Suggest quick actions/replies to customer messages (e.g., log a promise-to-pay, resend an invoice or account statement, update a contact, etc.)
  • AR manager

    • Provides collections forecast at 30/60 days or more
    • Predict collection effort based on collection strategy, customer & invoice data, & payment predictions
    • Provides predictable insights on the customer’s portfolio (expected payments & risk level)

Payment predictions

Improve your collections efficiency
with laser-like focus

Using machine learning that utilises previous invoices, payments and other customer data, Esker Synergy can make payment predictions on each open invoice according to its status and customer payment behavior. Over time, these predictions help determine a risk level per customer by comparing the customer’s current payment situation with that of their past payment behavior and are used to give predictable insights to anticipate and prioritise collection efforts.

How is this featured in the solution?

Each new open invoice is assigned a range of probable dates, also called the “most likely pay date.” Users can view this metric at the invoice level or through reports.

Collection calls needed are reprioritised based on customer risk level (critical/high/normal) and suggestions to defer calls for which payment is expected within 3 days are made so the AR team can easily identify the highest risk of payment default.

The “projected collection activities” report combines payment predictions with the existing collection strategy and other data such as promises-to-pay, logged calls, and customer and invoice status to predict the collection effort needed over the next 7 days (e.g., number of reminders and account statements to be delivered and the number of collection calls). This prediction gives the AR manager an idea of the workload required of the collections team and allows them to adjust their strategy or allocate more resources to potentially achieve their collection goals.

Collections forecast report provides an aggregated view of expected payments within the next 30-90 days. These are based on the payment predictions and combined with promises-to-pay or payments scheduled on the customer portal. This report provides greater visibility on incoming cash so AR managers can make better forecasts and adjust strategies accordingly.

Prescriptive analytics

Guide your decision-making
with smart suggestions

Esker Synergy AI facilitates decision-making at each step of the invoice-to-cash process by providing suggestions to the user. Not only does this improve overall AR performance, it makes life easier for the AR team by suggesting:

  • New credit limit or risk category

    Esker Synergy makes recommendations to the credit analyst during a credit review to assign the right credit limit or risk category to the customer based on different criteria (e.g., scoring data, payment behavior, sales history, etc.).

  • Smart allocations (when multiple combos are possible)

    When auto-matching can't be done, Esker Synergy provides suggestions for allocation to help the user find the right match between all possibilities and display the most relevant/likely allocation first.

  • Quick actions from customer messages

    In the context of a customer message, NLP is used to recognise keywords and recommend actions to the user to help them process the messages more efficiently. Typically, quick user actions are suggested to meet the most common customer requests such as sending an invoice, updating a contact or logging a promise-to-pay.

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Data extraction

Take paperwork & data entry out of your AR equation

As an expert in data extraction with several long-term developments and patents, Esker has the technological foundation to accurately capture documents like remittance advices or claims from emails and route them automatically to the right process and extract data from those documents to use it to automate the payment allocation or create a new claim to approve. And, in the case of user correction, Esker Synergy auto-learns to increase the accuracy the next time.

Esker customers benefit from high recognition rate from the beginning, delivering immediate value:

  • +95%

    Remittance processing time accelerated by 95% with Esker Synergy


Positive-sum growth

Everyone wins with Esker Synergy!

  • Your process

    The entire invoice-to-cash process benefits from the capabilities of Esker Synergy, resulting in greater efficiency in cash collection and DSO reduction.

  • Your team

    Esker Synergy enhances the overall user experience thanks to highly intelligent and easily utilised automation, analytics and suggestions.

  • Your customers

    The CX is improved thanks to better knowledge of the customer, better business decisions (e.g. credit limit), and faster replies thanks to NLP.

You’re in good company. Explore some of the other companies and AP leaders that have benefited from automating their processes with Esker.
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