WHAT WE DO
QBT develops algorithms and software in the fintech area, backed by a proof experience in the direct provision of services.
The mentioned experience has increased our know-how, consolidating our ability to develop tailored and customized management softwares in order to provide the best technological solution according to the specific customers’ needs.
Moreover, through a deep research and development activity we explored new areas of business turning into a point of reference in the field of Artificial Intelligence, and, in particular, in the Natural Language Processing and machine learning.
We develop all of our research activities thanks to a close cooperation with universities and research institutes, which represent the natural competence network at QBT.
Over the years, we specialized in the development of algorithms and softwares for the financial sector, for the control of stock exchange trading and for the management of non-performing loans, both mortgage-backed and unsecured, risk analysis and valuation of real estate asset.
The development of our products was possible thanks to the combined use of traditional techniques, such as statistical analysis, and experimental techniques related to new research areas, such as the use of behavioral models and agent-based simulation ones.
QBT realizes, upon specific request of its customers, white-label or in house products and services, investing in projects trusted to be worth which are always rooted and raised from the research and development area.
One of our greatest result was the development of a method of valuing and forecasting cash flows deriving from the activity of collecting non-performing loans (NPL). The big difference with the other existing methods lies in the fact that the valuation process takes into account the type of credit, i.e. guaranteed and non-guaranteed, thus generating a provision of collection and its timing.
The algorithm is based on a complex mix of technologies:
- purely algorithmic calculation
- statistical analysis
- agent-based simulation
- complexity analysis
We are able to provide different solutions according to the various and multiple needs and to the quality/quantity of the available data.
The algorithm input can result from data entry activities on management software specifically developed and tailored on customers’ needs or from massive traces extracted from the Customer’s systems.
The results can be produced in a raw format, for subsequent processing in excel sheets, or may be aggregated and reinterpreted through business intelligence tools.
Fintech: Risk Assessment
Rating and Alert System
Rating Alert System (RAS) is a project jointly developed by QBT sagl and Ontonix Srl.
It represents an efficient system of analysis and monitoring of the company performance from a systemic point of view.
Through the analysis of the company data (balance sheet, risks), RAS elaborates a default precursor of the company.
- The system is available online at http://ras.qbt.ch/.
Business Complexity x Economic Uncertainity = Fragility
A company is all the more fragile as its parameters vary significantly over time. The feature of uncertainty makes it difficult to manage a company and run a business. In order to reduce the complexity (or fragility) in a company it is necessary to take action and handle properly the variability factors.
The Resilience Rating™ shows where to start:
- research and selection of the company analyzed;
- lists of default risk companies;
- reporting by macro categories (portfolio, sectors) or by company;
- map of complexity;
- indexes and analysis of the correlations between the variables (how are the items of a company balance sheet related? What is the level of complexity? What is the rating?);
- complexity profile (which variable, of the balance sheets or risks, offers the greatest contribution to the complexity of the system?).