•Definitions and concepts (and... misconceptions) including: Digitalization, Artificial Intelligence, Big Data, and disruption as well as the drivers of AI
•An overview of the current Asset Management situation globally and a focus on Roboadvisory
•Practical use of Artificial Intelligence in Roboadvisory: a.o. at the investment strategy level
1. The classic Roboadvisor analyzed
2. The advanced Roboadvisor
3. Key evaluation criteria
4. Concrete example showing also development techniques: exclusive proprietary Research
5. Other key dimensions in roboadvising: ESG/SRI/SI (Sustainable Investing)
•AI as a ... theme fund including a benchmark (developers of AI and also their main beneficiaries)
•Simplest (mixed) risk measures and their issues
1. Value-at-Risk (VaR) and its different variants and back-testing
2. Discussing further developments including conditional VaR
3. Market risk’s Key Risk Indicators
4. Bringing in AI: to better take liquidity into account, for example.
*Contains exclusive proprietary Research
•About credit risk specifics
•Measuring credit risk
•Treating credit risk and the Internal
•Control System
•Credit risk’s Key Risk Indicators
•Bringing in AI
*Contains exclusive proprietary Research with algorithmic principles
•Story of big cases
•What about Basel on operational risk?
•Strongly improved model
•Operational risk’s Key Risk Indicators
•Bringing in AI: to better take education into account, for example.
*Contains exclusive proprietary Research with algorithmic principles
Professionals associated with the fields of:
Risk Management
Asset Management
Financial Services
Financial Planning
Project Management
Strategy Development
Audit
Corporate Risk Management
Compliance