Founded by business experts supported by technologists
Izengard’s core founders each have more than 30 years experience in Financial Services or in law enforcement. One of our key founders has been the Chief Technology Officer of a Financial Crime software firm and has knowledge of how solutions have been historically built.
Our founders include ex-Banking compliance officers, Financial Crime consultants and Risk Management specialists with global experience spanning regulators from the US, Canada, UK, EU, UAE, India, Singapore, Malaysia, Vietnam, Thailand, Hong Kong, Japan and Australia. Our founders understand the unique challenges in cyber security, in all areas of financial crime and have a solid grounding in IT risk related issues as well as in crime and anomaly detection. We have helped major organizations transform operations and bring best practices in terms of IP which will benefit clients as they see Izengard. Our founding team is multi-national and we co-operate across UK, Singapore and USA to achieve our goals.
With our strong business knowledge and foundations in IT, we employed expert developers and a CTO to help us architect, modularize and integrate the 3 domains into a unified data structure, seamless process integration and woven business logic, which makes Izengard unique compared to the silo solutions. The expertise in Data Science with our business domain also mean our models are holistic and can accept triggers from multiple areas and determine with precision. The reality from early machine learning deployment is you get a nice big improvement over traditional rules and analytics based approaches, but the improvements do not carry on and in fact model fatigue sets in and they deteriorate over time. So there are also false positives in machine learning models, just different than the ones that existed in traditional systems. Our models are holistic and as they uncover new signals are able to generate with human guidance never versions of themselves, so that they stay relevant and reduce the overall occurrence of a false positive match, which is also helped with our strong belief in Causal AI.
Therefore our core technology principles include a unified data model although it is distributed due to data privacy concerns, seamlessly integrated processes, holistic models covering signals from the 3 domains, causal AI and experential learning coupled with various deployment options.
Izengard applies the zero trust approach to all areas of crime. Izengard beings with the principle to not trust but use data points to verify suspicion. Therefore just like in major forensic crime situations, all evidence is collected, tagged, made sure no one can tamper with it or contaminate it.
Similar to a forensic lab, once the data is tagged and classified, stored in a tamper proof container it is then subjected to a series of tests.
A forensic methodological approach is used which may involve parallel streams of work such as cyber and financial crime does their own tests, but also shares their findings and results and if there are common actors, common events, common data points, common transfers, these are then modelled by a specialized machine learning set of algorithms known as Causal AI methods which provide cause and effect as well as explainability. The scoring approach Izengard takes is unique (as it is based on the cause and effect severity) and gives a much better approach to detecting potentially suspicious activity than current vendor solutions allow.
Izengard’s founders have been unsatisfied in their long careers with banks back offices, with a number of things: