Our ability to create a consumer-level wallet view is a core element of our bank marketing models. Our data scientists have many years of experience in the development, execution, and management of all components of the modeling processes and have built hundreds of unique models for credit card issuers.

Models that drive smarter customer-level actions

Our model suite is designed to fully satisfy a bank’s needs: Risk-adjusted revenue models, balance transfer models, category-level spend and usage, lifetime value, product preference, underwriting & default risk, price elasticity, and fraud & anti-money laundering risk. Delivery options include bank-hosted solutions using Argus algorithms, Argus-hosted solutions with scores provided to the bank each month, and delivery through monthly bureau feeds and application data.

How it works

  • Multiple data feeds

    Multiple data feeds

    Our models use data from a number of sources, such as banks, bureaus, partners, and industry sources. Based on our time-series data, we create model algorithms to accurately simulate the outcomes we see in the industry data.

  • Machine learning environment

    Machine learning environment

    We create models using only Argus off-us data as a dependent variable. We then retro test the model’s score, create market cells, and campaign files. Next, we run the campaign and evaluate the results using Argus off-us and on-us performance. After validating and maintaining the models, we tweak and rebuild them as needed.

  • Diverse execution platforms

    Diverse execution platforms

    We are then able to execute to multiple platforms: FCRA-compliant prospect database, digital execution platform, mass media, and non-FCRA execution platform.

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