Graph-based AI in WealthTech and RegTech

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Graph-based AI generating new business models in Wealth&Asset Management

In today’s ‘AI Economy‘, machine intelligence is becoming a fundamental building block for the digitalisation of the core processes in investment management and wealth/robo advisory. For example, AI can be utilised to constantly analyse and monitor risk concentrations in portfolios (‘automated service for portfolio health checks‘) or to construct fully customised client portfolios with adaptive diversification. Or to cure an existing portfolio in a few steps. The resulting risk-adjusted performance is much more robust than the classical Markowitz and Risk Parity approaches. These results hold across most assets, constraints, rebalancing frequencies, investment goals and other customisations. In the end, the AI approach makes things faster, cheaper, more effective – and it enables new business. Why and how does it work? Traditional portfolio construction approaches do not account for the changing influences and relationships across assets. They do not see contagion and risk spreading effects among them. However, these structures are fundamental drivers of portfolio performance and it is important to know them and to act upon them. All this can be automated with AI. The system starts with asset return time series as input. Investment parameters are also defined, e.g. collected by chatbots. The system then starts to learn the structures across the assets and constructs a feasable portfolio. The structural intelligence about the underlying portfolio dynamics lead to robust outperformance with low draw downs. The portfolio is constantly monitored and suggestions are made based on the structural intelligence. The methodology has been scientifically tested in peer-reviewed journals and also in practice. It is reinventing diversification and has another advantage: the learned structures and the derived prescriptions can be visualised and animated with modern web-based browser technologies. This means that portfolio managers, advisors and clients get intuition and transparency of the machine working behind.

Graph-based AI for Market Risk Management and Regulatory Reporting

Regulation on consumer protection and risk management is sharpening worldwide. For example, in terms of consumer protection, clients now have to be informed by their asset managers or advisors about asset shifts and regime changes affecting their portfolios. The general health of a portfolio has to be constantly monitored. In terms of risk management, institutional investment clients need to be educated by their asset manager about risk concentrations and the quality of diversification in their portfolios. Traditional risk management approaches do not account for the changing influences and relationships across assets, leading towards those risk concentrations and asset shifts. They exhibit a blind spot in this relevant area as they do not see contagion and risk spreading effects across assets although these structures are fundamental drivers of risk. These tasks can be automated and digitised by graph-based AI in terms of Regulatory Technology. The system starts with asset return time series as input related to the specific client portfolio considered. The system then learns the structures across the assets with graph-based Data Science and AI methods. The extracted structures are automatically interpreted in the context of portfolio risk, asset shifts, systemic risk and diversification. Results come as a new system of metrics (like ‘VaR‘) highlighting the patterns of clustering and interconnectedness gathered from the input market data. Another major advantage of this approach is its intuition, transparency and capability for visual analysis. Interpretable models are enhancing the understanding and communication of asset/portfolio networks especially in a coupling with visual, interactive interfaces. Additionally, almost all disciplines in market risk management can be extended by the graph-based AI methodology. Examples are stress testing, scenario analysis, model calibration, early warning, market cycle detection, and tail risk protection. This is also the basis for offering digital client portals for self-empowered, self-directed clients. The methodology has been scientifically tested in peer-reviewed journals and also in practice.