Platform simplifying venture capital for fund managers and founders

Company

Odin

Industry

Financial Services

Country of the Company

USA

Type of Service

Development

Tasks

  • Develop a classification model to help customers understand their spending habits.
  • Create an adjustable ML model to profile credit and debit transactions.
  • Build a clustering ensemble to define regular and irregular transactions.

Challenges

The main challenge was to create a competitive advantage by utilizing AI to distinguish the financial app from others and to grow expertise in advanced analytics and machine learning.

Solutions

  • An adjustable ML model that profiles customer transactions by merchant, purchase type, and income.
  • A clustering ensemble that categorizes each user’s transactions as regular or irregular.

Outcomes

  • The ML saving advisor became one of the most used features of the app.
  • The first version of the model allowed accurate labeling of regular and irregular transactions in 80% of cases.

Technologies Used

Python (scikit-learn, gradient boosting)

Data warehouse (AWS Redshift, Google BigQuery)

Power BI

Company

Odin

Industry

Financial Services

Country of the Company

USA

Type of Service

Development

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