GENERATIVE AI IN THE FINANCIAL SECTOR

As your organization handles more and more data, it becomes more and more difficult to operationalize and organize that information for data-driven decision making. At the same time, there are important areas where you don’t have enough data to make sound predictions.

Our no code platform makes it easy to create AI solutions to tackle your biggest problems. Boltzbit proudly works with:

Using Big Data: Probabilistic Database Linking

With many data points at your fingertips, probabilistic linking helps you combine information from different sources to form a new linked dataset. This is incredibly valuable to better understand your customers across a wider range of variables, but typically this complex project can be very time consuming.

Our customer imported their separate, unlinked databases to Boltzbit where our neural network quickly began deep learning, testing and connecting pathways between the data. Without writing any code, our customer was able to identify common transaction patterns by users across multiple databases quickly and efficiently.

This allowed them to consolidate databases so they could leverage more from their data in a much shorter period of time.

Enriching Small Data: Portfolio Optimization

Having a clear view of the future is vital when needing to make forecasts or optimization decisions. However, this is made incredibly harder when looking at events which are rare or where limited data is available.

Our customer used generative AI to create synthetic data for rare market events. They were able to ingest some data records, then built a cube to predict and increase the number of records so that they had a more robust foundation for data-driven prediction.

This allowed them to improve the accuracy of their price forecasting and make more confident decisions around portfolio optimization.

Automation: Enhanced Prospect Profiling

Knowing when and what product to pitch to your client requires an in-depth understanding of their intentions, goals, past behaviors, and preferences. Today, this is typically done by investing heavily into historical analysis of your client’s portfolio and spending time with them trying to predict their next needs.

Our customer used generative AI to better understand their clients automatically by pulling their data together and building rich client profiles. This AI was then able to make accurate recommendations to the account manager on which products would be best suited for the client.

This saved a huge amount of time for the account management team and gave them clear indicators of where to focus and enhance client engagement in future.