Plat.AI makes it easy to integrate machine learning into your platform. You can use our predictive models to receive risk evaluation in real-time through our specialized indexes.

We created several risk indexes by building machine learning models and training them on millions of real-life data cases. Our Default Stability Index evaluates the applicant’s company or organization to determine the probability of default. Our Employer Stability Index targets specific industries while factoring in the employee’s income type. These are two of our most popular indexes.

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Understanding Plat.AI’s Platform and Server-Based Solutions

Whether you want to reduce default rates or weed out fraudulent applications, we have a setup that will integrate seamlessly with your current lending or marketing resources.

Plat.AI provides real-time predictive modeling for companies in the financial industry. We offer platform and server-based solutions and can help you to choose the best product for your needs.

With our platform-based solution, we offer two possible cooperation options:

First Option:

User-Created Models

  • You receive access to our platform with all necessary training and documentation about how to utilize its functionality.
  • You will have access to our ticketing desk for support with technical difficulties and for professional consultancy.
  • You can build models in-house and apply them using our platform.
  • The platform enables monitoring of your model performance and provides quick responses to changing socioeconomic conditions.

Second Option:

Plat.AI-Created Models

  • Our team works closely with you to identify problems solvable through modeling. We help explore and define what useable data you have available.
  • Our team trains different models and tests them using validation data.
  • Upon your approval of the model’s accuracy, we deploy it using our platform and provide the probability of the desired outcome or respective scores on a real-time basis.
  • You will receive detailed documentation about model parameters and their impact.
  • Our team will monitor the performance of the models and, if needed, will make any necessary adjustments.

While using a platform-based option is ideal for users with limited coding experience, there are some limitations.

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  • Relatively cheaper
  • Relatively easier and faster to build and integrate
  • Fully interpretable results
  • Faster real-time prediction speed
  • Availability of monitoring tools


  • Limited usage of Machine Learning potential
  • It might not yield the same accuracy as black-box solutions
  • Models will not auto-update

Server-Based Solution

For users who are not able to build and deploy complex ML models in-house or need support, our server-based solution can fit your needs:

Our team works closely with you to identify problems solvable through modeling. We help explore and define what useable data you have available. If necessary, we can help you increase the predictive power of your data models with recommendations on how to drive the digital transformation of your company.

We fit your specific problem into our Machine Learning and Deep Learning algorithms and test them using validation data. Based on the testing results, we deploy the appropriate model on your servers or ours.

The production-level models provide real-time predictions enabled by API integration with your system.

You will also receive documentation explaining applied black-box algorithms.

Our team will be available for support and consultation for the entire contracted period.

We will monitor the deployed models with an advanced toolkit developed through years of experience in this field.

We will provide your team with a training program specially prepared for your company, enabling you to maintain and update the models.

Our server-based solution may be a better fit for your company if the following advantages outweigh the limitations.

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  • Ability to apply any machine learning algorithm
  • Ability to apply any data preparation/transformation procedure
  • An unlimited toolkit that makes it possible to achieve more accurate prediction
  • Automatic model update


  • More expensive than a platform-based solution
  • Longer setup time
  • Harder to interpret results (but still possible)