ADS & ECAAI | Applied AI for FinTech
In this webinar we will be exploring how AI is and can be used in the finance sector.
12:00 Introduction & Welcome
12:05 Talk #1: ECAAI Finance Lab
12:30 Talk #2: Machine learning in Financial Economic Crime
Talk #1 by Kees van Montfort
Kees van Montfort is a senior researcher and lecturer at the Amsterdam university of Applied Sciences and expert of the ECAAI Finance Lab.
In his talk Kees will be introducing the AI Finance Lab. The AI Finance Lab focuses mainly on the financial and administrative functions within companies. These functions are changing radically due to new technologies. AI is increasingly taking over tasks previously performed by humans. The predictions vary from source to source, but roughly half of all jobs will disappear due to algorithms in the next 10 to 20 years. At the same time, many new jobs will be created. They are currently building up knowledge in the following areas: 1) The optimal use of AI in the professional field of Finance & Accounting; 2) The adoption of AI in the professional field of Finance & Accounting; 3) AI and Ethics; 4) Financial Fraud Detection; 5) Financial Innovations.
Talk #2 by Tamara Trofimenko
Tamara works as a Data Scientist for the Analytics for Financial Crime team at ING. She has a master’s degree in Computational Science, and she joined ING straight after her studies. Her team specialises in using analytics and machine learning to detect financial crimes, for example anti-money laundering or terrorism financing.
Tamara will be discussing Machine learning in Financial Economic Crime. The monitoring of financial economic crime is an important activity of a financial institution. Criminals require access to the financial system in order to realize the benefits of their crimes. Global financial institutions, ar e at risk of being used to facilitate the laundering of the proceeds of crime and the financing of terrorism, and/or becoming involved in transactions related to sanctioned entities or controlled exports. There are a number of different systems which monitors or detects such criminal activities. A large number of these can be manual processes which require substantial man hours or rule based systems which, in general, generate a high number of false positives increasing the workload for analysts down the line. The goal of building advanced analytics models in the KYC and financial crime domain is to enhance the screening, monitoring and detection capabilities that exist. In addition, customer due diligence can leverage analytics techniques for increased efficiency.
Zoom details: https://us02web.zoom.us/j/85258721133