Curious How AI Can Improve Collection Effectiveness?

Searching For a Solution
 
More businesses than ever are turning to Artificial Intelligence (AI) to improve their collections effectiveness. Organizations are turning to AI in order to better predict how likely a particular receivables collection effort is to be successful. 

But here’s the bad news: so far, AI has been a disappointment for many businesses. According to a recent survey by MIT-Sloan and the Boston Consulting Group, 65% of companies report seeing no value from their AI investments. Even among organizations that have made “significant investments” in AI, 40% report no business gains from AI.

There are many reasons companies have failed to see business gains from traditional AI, including:

Users have access to a single AI predictive model that is tied to a specific set of business conditions. When conditions change, the model cannot dynamically respond.

Users are not able to define their business assumptions or cost-benefit tradeoffs to determine how to best allocate their limited resources. 

End users cannot easily incorporate their feedback resulting in a less optimized model.

With business conditions constantly changing, you need to quickly figure out:

  • Which specific collections should you pursue given your current resources.

  • How does changing how and when you pursue a collection affect the likely outcome, and how many resources should you allocate to each collection strategy.

  • How you can quickly adjust your collection strategy to compensate.

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Aible's Unique Approach to a Typical Collections Use Case

Why Aible For Collections Effectiveness

What is needed is a fundamentally new approach to AI; an AI solution that provides an OPTIMAL AI model tuned to your unique business needs, one that is always optimized, even as business conditions change. 
Traditional AI falls short, but there is a solution...

See Aible in Action

Here is a 5-minute video that shows how Aible optimizes collection effectiveness in a time of rapid change

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Popular Collection Use Cases: 

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Likelihood to Pay
Determine which customers are most likely to pay

Collections Optimization 
Know how many collection resources to allocate to increase collection effectiveness

Strategic Planning
Understand what factors drive your collection effectiveness so you can improve quickly

Scenario Planning 
Prepare for best case, worst case, and everything in between automatically with AI

Hypothesis Testing
Increase your collection effectiveness by using the most effective collection strategy 

Aible is the only AI solution that goes beyond predictions to enable you to optimize and anticipate. Aible lets you consider hundreds of shifting market factors and gives you the optimal recommendations to maximize business impact and ROI.

Predict
Aible can predict the probability that a marketing promotion will convert, but since not all prospects are equally valuable and marketing assumptions and prospect behavior can change - you need a strategy that can adjust quickly.

Optimize
Aible lets you balance resource constraints to determine the optimal resource allocation across multiple promotions and buyer segments so that you always target the promotions that add the most business value. 

Anticipate 
As market conditions and customer preferences change across products and regions, Aible tells you how you can quickly adjust your marketing strategy to compensate. Aible provides an efficient frontier of many models versus a single fragile model, ensuring you use the right model given the circumstances.

Don't see you specific use case? 
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