As soon as AI started emerging from the field of research, Banking became a particularly favourable development axis for it. The banking business has also accelerated the implementation of several applications linked to AI: Expert Systems, bank risk, Data Management, Regulatory…

Wider process automation thanks to « Machine Learning » :
Thanks to ‘Machine Learning’ (Lien vers la définition Wikipedia), machines are able to learn and manage tasks for which they were not programmed at first. This allows the machine to make decisions and choices with no human intervention. This enables a broad coverage of automated processes.

FEKRAis well-positioned to work on this development axis. We have already implemented AI with Machine Learning. in the funding system of several clients in the banking sector. The objective is to help the human respond to funding requests in a timely manner.

Service and user experience improvement thanks to « Chatbots » : Chatbots (Link to Wikipedia) help improve different banking functions.

  • Assistance and advice to clients
  • Assistance to administrators

Better Risk Management :
Risk exists in different forms in the banking sector. Thanks to AI implementation, risk management can be improved in different areas:

  • Risk of fraud
  • Cybersecurity
  • Customer rating and Granting

Data Management and KYC improvement Over the last years, the volume of databases (structured and non-structured) has grown steadily, with very heterogeneous sources: data, voice, images, videos…).

Thanks to AI and Datascience, banks can use and value Big Data primarily to improve CRM and for Marketing strategies.

The Trading algorithms, especially ‘High-Frequency Trading’ – HFT (Lien vers la définition Wikipedia) have caused several small stock market crashes.

High-Frequency Trading’ is a big part of stock trading and, in normal days, it helps bring volumes to markets. However, in times of crisis, algorithms amplify trends and can cause considerable damage. –

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