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