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Customers’ account history holds valuable information. Augmenting raw data with spending categories allows better understatement of customer habits. It allows customers to manage their personal finances in a straightforward manner, analyze it and take action. Financial institutions can use this additional information in internal processes, customer segmentation and use it as a main value driver. 


Websensa PFM uses advanced AI-based and Rule-based classifiers to label each user transaction with appropriate category,. Classification Engine uses custom, multi level category tree that will be best suited for the Bank and it’s customers needs.


Advanced Architecture:

  • Multi-Level Architecture leveraging 

  • Artificial Intelligence and Rule-Based Classification,

  • Automatic adaptation to user behavior,

  • Customizable Category Tree


High Accuracy:

transaction labeled correctly

High Efficiency:

150 transaction per second per core

High Scalability:

Real-time transaction categorizastion


Key features for customers

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Limits, Alerts & notifications

Provides a mechanism that identifies and detects events, which trigger notifications to customers  when certain events occur, e.g. a limit is reached or a new transaction is received.


Set targets connected with specific categories, subcategories or merchants. This allows customers understand how they spend their money compared to a plan and helps to make decisions to change their financial behavior to fulfill or change targets

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Cash flow prediction

Identify and manage cyclical payments and incomes. Give customers a comparison between current and historical flow. Predict future transactions and payments.

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Financial analysis

Create custom reports and views of transactions based on categories, subcategories or merchants. See aggregations of spendings per week, month or year.

Spending/income categorization

Automatically assign each transaction to a category and subcategory. That gives customers an insightful overview of their spendings an incomes.

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Key features for customers

Key features for financial institutions

Client segmentation & risk assessment

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Builds customer representation based on multiple data, like: static features (demographic data, geo location, devices), owned products, cash flow, spendings categories distribution. Use it in recommendations, prediction services, credit scoring or fraud detection. This works in real-time, based on current user activity.

Customers data augmentation

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Enhanced your data that can be used for multiple products and helps with customers behaviour analysis.

Key features for financial institutions

How it works

How it works


REST API - solution can be easily integrated into your existing infrastructure using API. Used when near real time is required. Transactions are classified as soon as they become available for the engine.

Batch mode - used for more efficient classification of large amounts of historical data.

Custom integration - PFM is open for other ways of integration. Queue systems are supported as well as direct database access. 




Click the button to download the whitepaper describing PFM details

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