An algorithm based on machine learning and Artificial Intelligence redefines the management of financial portfolios in the name of transparency, neutrality and efficiency.

Qi4M is an innovative startup founded in 2017 by a team of experts in the economic-financial sector,with a great experience in asset allocation, Machine Learning and Artificial Intelligence. The only FinTech among the top 5 startups selected in the first acceleration program of B4i – Bocconi for innovation, the platform for pre-acceleration, acceleration and development startups within the companies of Bocconi University in Milan, thanks to the experience of four months of training, coaching and networking made available by B4i, Qi4m has been able to mature and better define its product and its mission: “Today Qi4M is a more mature reality with a clear awareness of what will be its role as a company with a strong vocation for research and innovation to transform and make more democratic a critical sector such as finance, also through the opportunities that Machine Learning & AI are offering and will increasingly offer to FinTech companies like ours “, explains co-founder Vittorio Carlei, professor of Advanced Analytics for Business at LUISS Guido Carli

University and Advisor of the R&D division. It all started from a chat between long-time friends about the possible advantages of a revolutionary idea for the financial market: minimizing distortions in the choices made for investments through the use of machine learning techniques. A question far from trivial, worth the Nobel Prize to Daniel Kahneman.

In fact, Carlei tells of a market often pulled by irrational drivers, which can be revolutionized by an independent algorithm and free from interests,driving investment choices.

The founders started the activity as a secondary job, until the meeting with Massimo Molinari took place, who intended to create an asset management platform capable of facing the crisis of asset management due to high costs and the difficulty of meeting people’s

needs. Customers at that time paid huge costs, in exchange for modest results.

Qi4M has set itself the goal of developing mathematical algorithms for intelligent, neutral, automatic and transparent management.

The revolutionary idea began to find confirmation also in the scientific literature with a series of important publications. Among these, particularly significant, is that of Professor Bryan Kelly of Yale University who says that machine learning is the future for the management of equity portfolios.

Qi4M was already very successful at the time, as it had important clients such as Credit Suisse and BNP Paribas Cardif.

The company has further expanded: today it employs almost 20 people and plans to close 2020 with almost one million in revenues. Despite COVID-19.


The first advantage that Qi4M offers is the low cost of management, through the elimination of compliance costs and the exploitation of economies of scale of a technological nature. This makes Qi4M competitive compared to the same portfolio management activity done by a traditional company.

Secondly, Qi4M’s platform is agile, therefore more scalable.

Third and not least, the performances are definitely interesting. Just during the lockdown, when the market lost almost 40%, Qi4M left only 15% on the field and in May 2020 it has already returned to the level of the maximum shares of 2019.

Carlei points out with satisfaction that few have achieved these results, and that generally a similar goal is achieved with sophisticated and niche managers and decidedly more expensive.

The typical client of Qi4M is the institutional one, which for market reasons cannot be a small investor. “Withour management – explains Carlei – we guarantee that large subjects can configure solutions accessible even to people with capital to invest smaller ones”. This is by virtue of Qi4M’s way of working, with the product building business being decoupled from distribution.

The algorithms develop sophisticated but transparent strategies, typical of a high-performing manager with high costs, but applied to products accessible even to those who have to invest little. This peculiarity allows to profile the investor and to guarantee him the performance and a level of sophistication that otherwise he would not have.

Case histories

The first success story of Qi4M is that of the dubbed collaboration with BNP Paribas Cardif. It has entrusted the Milanese company with the mission of placing insurance savings products on the market, profiling customers and suggesting ad hoc products. Experts have developed a complex platform that uses natural processing algorithms to calculate whether costs and performance are in line with customer expectations. These are complex formulas, which allow to support the banker in grasping the customer’s needs more adequately, offering him products that he sees best suited to his level of understanding. The second important case history is that relating to the activity of reballancing ingent.

Qi4M produced an index, which became a certificate issued by Credit Suisse and placed by Credit Suisse to its clients, which raised 30 million on an annual basis. In this particular case, if the strategy proves successful Qi4M also gains on the performance generated by the index strategy. Through the proprietary algorithm, Qi4M manages the Newfoundland Index published by Credit Suisse: an example of tailor-made financial innovation thanks to fully customizable, scalable and economical solutions.

Finally, the scientific partnership with Nowcasting Ltd., a company born from a group of professors from the London School of Economics, is interesting. The partnership consists of the development of an asset allocation platform that uses machine learning techniques to segment and profile strategies for individual customers.

Finally, for the first half of 2021 the start of the internationalization activity is planned, possible thanks to the partners who already have a European vocation. The goal is to go beyond the Italian borders and establish itself in more countries of the Old Continent.