Technologies have driven business progress by providing innovative and effective ways to solve business problems. The financial sector is one of the most accepting of innovation, and the growing pressure from fintechs has encouraged other businesses to act. This has driven rapid incorporation of artificial intelligence (AI) processes and machine learning (ML) models into standard processes in financial institutions, including to optimize decision making.

New methods are replacing traditional machine learning methods that were fairly easy to understand, explain and interpret, but less effective. It is obvious that the use of AI is essential for banks, insurers, telecommunications operators, the public sector, health care and industry. Financial institutions that do not use AI risk lagging behind competitors who make strategic decisions using AI-supported analytical systems.

Profiting from the wave of AI use

The use of artificial intelligence is changing the financial industry. Several factors have significantly increased the potential of its applications:

• Big data. Financial institutions have collected data for many years, but they could not monetize its potential until recently.
• Machine learning mechanisms. Data mining is not a new concept, and the financial industry has widely used it for years. However, modern analytical methods have expanded the range of approaches and methodologies available to analysts and developers, enriching analytical modelling processes.
• Processing power and technological infrastructure. Modern in-memory technology (RAM processing) significantly shortens the modelling process and allows the use of demanding AI techniques. The time required to develop and implement analytical models has been radically reduced.

AI techniques for targeted solutions

Many of us associate AI with robots, chatbots and other machines that learn from historical data and support human processes. This is one application, but AI techniques also provide a toolkit for building targeted solutions.