Syntherion KIG is a startup initiated at ETH Zurich, officially located at the chair of Entrepreneurial Risk of the ETH Risk Center, and based on a project sponsored by St. Gallen Kantonalbank (SGKB), the Swiss National Fond (Bridge) and Innosuisse (Innosuisse coaching program) that bridges state-of-the-art research in the fields of Finance and Machine Learning (ML) with challenges in risk management functions in banks and beyond. The mission is to develop a software based on original research and apply it to medium-sized banks to address growing pain points regarding stress tests . If the proof of concept with the cantonal bank is achieved, the goal is to roll out the product and approach other players in the financial market in Switzerland and beyond.

Small and medium-sized banks that lack the resources to develop internal risk management solutions are subject to complex risks and face 1) insufficient data quantity or quality and input from various sources; 2) inappropriate and ...
Syntherion KIG is a startup initiated at ETH Zurich, officially located at the chair of Entrepreneurial Risk of the ETH Risk Center, and based on a project sponsored by St. Gallen Kantonalbank (SGKB), the Swiss National Fond (Bridge) and Innosuisse (Innosuisse coaching program) that bridges state-of-the-art research in the fields of Finance and Machine Learning (ML) with challenges in risk management functions in banks and beyond. The mission is to develop a software based on original research and apply it to medium-sized banks to address growing pain points regarding stress tests . If the proof of concept with the cantonal bank is achieved, the goal is to roll out the product and approach other players in the financial market in Switzerland and beyond.

Small and medium-sized banks that lack the resources to develop internal risk management solutions are subject to complex risks and face 1) insufficient data quantity or quality and input from various sources; 2) inappropriate and inflexible, application-specific risk analysis and measurement tools; and suffer from 3) poor estimates of profits and losses for stress testing activities.

In response to that, we offer a truly flexible risk management software allowing for real-time risk management with a dedicated focus on stress testing by 1) building on the most powerful AI techniques available such as Bayesian learning, deep generative models, and causal models that, in combination, are able to; 2) use every bit of data available; 3) cope with the lack of data and unstructured data and unfounded assumptions which can be detected even in complex risk models; 4) be used safely while remaining in compliance.

Customers will 1) gain deeper knowledge on risks through simulations & scenario analytics; 2) evaluate risk/performance measures reliably; 3) tackle computational complexity; 4) capture underlying mechanisms from the inside by compositional modeling; & 5) flexibly adapt the software tools to bank-specific needs. This will 1) optimize banks’ risk return ratio; 2) reduce banks’ regulatory & software costs; 3) improve banks’ & thus overall financial stability.

- We occupy the sweet spot between providers of generic modeling toolkits with limited specifications for banks (e.g. MathWorks) & providers of application-specific tools not using AI (e.g. RSN).
- High market barriers for others: we have built technological leadership AND gained deep market insights -> SGKB’s CRO
- USPs derive from IP
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Employees

Dr. Christian Hugo Hoffmann
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Dr. Christian Hugo Hoffmann CEO Dynamic AI and FinTech entrepreneur by heart with both highly advanced academic knowledge and practical exposure in an international context