Bigger and more dynamic data sets

Today, automatic underwriting is largely performed on the basis of static data sets acquired through customer surveys. The rules we apply to those data sets are at least even static as the latter. So the moment something changes in the equation – say, parameter value or weight – our underwriting model becomes obsolete. Deep learning mechanics enable us to explore bigger and more dynamic data sets and sources in order to deliver more accurate risk assessments.

Business advantages

  • Increased operational efficacy
  • More accurate risk assessments
  • Less cognitive load for your employees

Our approach

As this is a data-driven solution, our data scientists analyze your data and evaluate its business potential, then create a solution that uses the potential best. We take a closer look at demographic and financial situation of the client, their past transactions, their loan history and related products, plus publicly available data from the internet and social media.

We use all of that to create the most accurate model that can provide a financial health score and predict how likely the client is to miss an installment payment or adhere to their financial obligations in general.

We can provide you with either a lightweight solution dedicated to solve your particular pain point, or a full one, encompassing other useful features.

See our products that leverage machine learning for credit scoring:

Comarch Insurance Underwriting

Comarch Insurance Underwriting is a solution supporting end-to-end management of underwriting cases. It comes with an intuitive workspace and a collaborative underwriting platform improving the automated risk evaluation process and speeding up manual decisions.

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