Machine Learning in Finance

Using classification model and self-service BI we helped a financial service company to optimize pricing policy. The model takes into consideration customers’ behaviour and adjusts pricing depending on individual financial profiles.


Better offers personalization for each individual customer

The company want to use tool for personalize recommendations of new financial products which will automatically take into account preferences of each individual customer and find the best offer for them.

Improve existing scoring systems

Company want to decrease number of customers who were unable to pay off the loan. There was a need is to create additional tool which could support existing scoring systems using alternative data sources and automatically detect hidden behavioral patterns.

Flexible analytics tool to create ad-hoc analysis by business users

A light modification of the report required a lot of work for company’s analysts. Company want to improve existing analytical systems so that users can conduct ad-hoc analysis by themselves.


We designed and implemented Machine Learning solution together with Business Intelligence self-service software with a data warehouse, where the business users could be self-sufficient in development of dashboards and reports – in order to analyze a relation between sales and customers. Developed Business Intelligence system supports marketing, operations, business, sales and HR departments.

​Custom machine learning model for scoring
We created a machine learning algorithm that calculates risk ratio and adjusts prices for each client individually based on their financial profile.

Custom recommendation engines

We created a data-driven recommendation engine that analyzes a huge amount of transactional and customer behaviour data to increase sales, overall business performance and client engagement and satisfaction. This solution was used to support up-sell and cross-sell campaigns.

Implementation of self-service BI for ad-hoc reporting

We implemented data warehouse, OLAPs and self-service BI, which gave possibility to increase efficiency. Reports were produced faster and insight were shared across organization.


​Human interaction and report development time was reduced by implementing analytical cubes that provide the end user with multiple analytical options. Visualization tools help to navigate through data universe and analyze links between occurrences – all in a much more intuitive and user-friendly way.


Lower costs of unpaid loans


C-level executives hours saved each month


R programmers
tableau implementation consulting
aws developers big data
code integration
R programmers
tableau implementation consulting
aws developers big data
code integration

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