Decision boundaries are the hypersurfaces that partition the underlying vector space into two sets, one for each class. The classifier will classify all the points on one side of the decision boundary as belonging to one class and all those on the other side as belonging to the other class. Decision boundaries are the regions of a problem space in which the output label of a classifier is ambiguous (e.g. solvent – not solvent, creditworthy-not creditworthy, causal – non-causal, etc.). Financial data is notoriously noisy and decision boundaries are not always clear cut.
DecisionBoundaries brings clarity to complex financial situations for the benefit of its clients by relying on our 30 years of transactional experience in investment banking (both on the trading and corporate finance sides of the business, and as a CEO), together with a rich academic background in Industrial Engineering, Business, Financial Engineering, Data Science, and Machine Learning.
Indeed, DecisionBoundaries straddles the divide between the academic and the practical which allows us to address financial challenges with both intellectual rigor and creativity. When we value a complex security, we prove our valuation. When we advise a creditors’ committee, we leverage our contacts and longstanding credibility in the investor community to work creatively toward a resolution. When we value a business, we use a good part of the 95% of available data that is ignored by traditional appraisers to make a better outcome possible for our client. When we advise a financial institution on capital adequacy, we combine our intimate knowledge of the rules and regulations with our financial engineering wherewithal to design solutions which are both compliant and creative.
DecisionBoundaries is not all things to all people and it focuses exclusively in its core areas of expertise. Indeed, we believe that any client we work with will have an edge over the financial challenge they are facing.
At DecisionBoundaries we are “Fluent in Complexity”. This doesn’t mean that we embrace complexity for its own sake but that we are not intimidated by it. Indeed, we are fluent in both human languages (which enables us to economically navigate cross-border engagements), and also in machine languages (which allow us to be cost-effective in otherwise computationally expensive engagements).
The quality of our work product, combined with our ability to minimize costs, make DecisionBoundaries a compelling value proposition.