Artificial intelligence is poised to transform the financial industry. This intelligence will be built on modern software platforms that combine data from different sources, process it, and transform it into relevant prediction. The shift from data gathering systems to predictive ones that help financial companies to understand the data, has already started. 

Our Services

Machine Learning

Discover the hidden knowledge within your data. Get data-driven recommendation.

Big Data

Develop big data environment that enables you to store massive amount of data.

Complete Software Solution

We are with you all along the way to build a Machine Learning backed software and applications.

AI FinTech Use Cases

  • Churn prediction and customer segmentation

    Build predictive models to know your customers better. With help of machine learning methods we analyze your customers behaviour to determine their loyalty and to prevent them from churning.

  • Recommendation system

    With help of machine learning make sure your customers are getting what they like throughout their experience using your services.

  • Fraud detection and Anti money laundering

    Apply predictive models and anomaly detection models to your data to detect, identify and prevent fraudulent actions.

  • Information extraction and sentiment analysis

    Gather and analyze real-time data from sources like market data, financial articles and news, relative contents in different languages and social media to create relative reports and assess their impacts using natural language processing methods, e.g. sentiment and intelligent specifications on a stock.

  • Risk management and portfolio optimization

    Estimate portfolio risk and return using machine learning methods, predict portfolio risk appetite with lower estimate error and high accuracy.

  • Insurance underwriting

    Evaluation of risk and exposure of potential customers, e.g. how reliable a person or an application is!

Project Process

1.       Business Understanding

Focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition and a preliminary plan.

2.       Data Understanding

Starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.

3.       Data Preparation

The data preparation phase covers all activities to construct the final dataset from the initial raw data.

4.       Modeling

Modeling techniques are selected and applied.  Since some techniques like neural nets have specific requirements regarding the form of the data, there can be a loop back here to data prep.

5.       Evaluation

Once one or more models have been built that appear to have high quality based on whichever loss functions have been selected, these need to be tested to ensure they generalize against unseen data and that all key business issues have been sufficiently considered.  The end result is the selection of the champion model(s).

6.       Deployment

Generally this will mean deploying a code representation of the model into an operating system to score or categorize new unseen data as it arises and to create a mechanism for the use of that new information in the solution of the original business problem.  Importantly, the code representation must also include all the data prep steps leading up to modeling so that the model will treat new raw data in the same manner as during model development.

Are you planning an Artificial Intelligence project?

Send us your e-mail and we will contact you with further details.