Attending AI for Finance was the opportunity to hear strategic actors of the financial ecosystem discuss and debate what the finance of the future would look like with AI.  The event took place at Le Palais Brongniart in Paris on September 3rd. 

AI is not new. It is the cloud and the computing power that are game changers and make space for innovation” says Adina Grigoriu, CEO Active Asset Allocation. The Finance Industry  has made impressive progress adopting AI and comes naturally first as an industry when talking about data and AI. 

What are the main impacts of AI on finance ?

Customer experience comes first

From chatbots to augmented messaging, sorting and routing support enquiries, personalized UX and interfaces and natural language processing, AI impact is huge on customer.

AI definitely changes the conversation we have with our customers” says Claire Calmejane at Societe Generale. “It is a great help to better interact with the customers” says Aldrick Zapellini at Credit Agricole. Allianz group uses it to hyper personalize their answers. And Jean-Philippe Desbiolles VP IBM Watson Europe talks about “Customer Experience with embedded AI” or “AI with a Human touch”.  

Employee’s knowledge and effectiveness comes second

Saving time for employees and enhancing their operational effectiveness means reinvesting this time into more effective human relationships.” says Aldrick Zappellini, Directeur Data & Analytics at Credit Agricole SA. That is also true at Credit Mutuel Arkea. Virtual assistant, email analysis… : 35 000 employees are equipped with that kind of tools.

Followed by security, risk and compliance as well as processes. 

Fighting fraud, having the right signals emerged from data analysis is crucial for the ecosystem, interesting use cases are detailed later in this article.

RPA (Robotic Process Automation) is dead, “vive le RPA” with intelligence.” says Jean-Philippe Desbiolles, VP IBM Watson Europe. At the beginning, it was only automation with no intelligence. Now with AI, RPA enters a new era and may transform into RIA “Robotic Intelligent Automation” according to the software company Blue Prism.

Use cases

Round tables on the main stage and master classes have raised many interesting uses cases. I’d like to highlight the “Risk management All ladies Round Table” moderated by Emma Sezen, Head of AI for Finance, Start up Inside. It featured Adina Grigoriu, CEO Active Asset Allocation, Sophie Elkrieff, Chief Investment Officer Maif and Anne Lamotte, “My Future” Ecosystem leader at Allianz. Special kudos to them 🙂

In a complicated interest rate context, Maif uses “Active Asset Allocation” to diversify its assets allocations. Thanks to AI, the investment team explores new and creative investment opportunities. Before, it took 8 weeks to create an Asset allocation model for 10 asset classes, now it is nearly immediate. “It means we provide personalized asset management while cutting costs, that is a win win for us and our customers” says Sophie Elkrieff at Maif. 

Another common use case in Asset management is the “Stress Algorithm” : the objective is to bring investment losses to a very minimum. Predictive models are built with many parameters, each of them individually weighted.

Stress Algorithm are also used in Cybersecurity as mentioned in the AI & Cybersecurity Round Table. “First applications of AI in Cybersecurity is to identify the threats and wether the alerts are relevant or not. To really enhance human work, AI should be able to answer WHY this is a threat.” – David Sadek, VP Research, Technology and Innovation at Thales

#How ? What’s the perfect organization to scale AI projects ?

Datalabs, Cross functional teams, POC, Group Project, Customer clusters, what are the initiatives I saw.

Datalabs have a role to play in the acceleration of data initiatives and some organizations are coming back from localized POC. It is most of the time very difficult to transform a regional project into a group project as the requirements are too differents. “To be successful, a data or AI project should bring along all counterparts : CA Tech & services as well as a the User pole.” says Aldrick Zappellini, Directeur Data & Analytics chez Credit Agricole SA. 

A small cross functional AI team interacts with the 350 Data scientists of Societe Generale, spread in different locations. “A lot of initiatives are on a global level, we are working on scaling the projects” says Claire Calmejane from Societe Generale.

At Allianz,  the “My future” ecosystem led by Anne Lamotte focuses on forecasting and optimizing future revenues generated by the customers : savings, pensions. It also focuses on self service, making sure customers can access the performance of his or her contract anytime. 

More generally, while AI allows on most cases to modernize IT while leveraging existing systems, it does impact data lakes and infrastructures as most of the AI Apps are cloud based. 

A final word ?

“Involving AI in process is not about replacing people but investing in people to find the right place for everybody. The new keyword is Learning not Data.” says JP desbiolles, VP IBM Watson Europe.

AI for Finance is organized by “StartupInside” with an ambition to create and fuel sectorial AI clusters on Health, Industry, Environment and retail.

By Marina Detienne