Dr. Manuel Fernández-Utrilla Miguel
Dr Manuel Fernandez is an Engineering PhD, International Executive MBA by Instituto de Empresa Business School, expert in venture capital and investments by London School of Economics (LSE), expert in Artificial Intelligence – Machine Learning by Massachusetts Institute of Technology (MIT) and Superior Telecommunication Engineering by University of Málaga.
Wide international exposure with excellent negotiation and management skills. Collective experience of over 15 years in EMEA region, which enabled to build a valuable network of business relations following project development for multinationals and different European public administration organizations. In addition, insightful knowledge about the business environment in EMEA region in various positions and industry sectors.
Several positions focused on strategy, planning and management. Frequently engaged in long/short term strategy planning in various fields of business; in addition business development management, market scans projects & assignments.
Considerable knowledge and experience have been gained from attending a PhD, different management master programmes, an International Executive MBA, many conferences, seminars & meetings with several policy makers and top managers from different sectors, in addition to networking with top echelon clients (high net worth individuals) who have the ability/purchasing power to start initiatives worldwide.
In conclusion, I can provide social and team-work management skills in order to develop business in EMEA region in any sectors which I have worked in. My international exposure alllows me to set a solid framework to solve any business problem with solvency.
MicroLoans Platform Evolution: from a monolithic platform to distributed microservices
Our success case is based on the evolution from a monolithic to a distributed microservices platform hosted on cloud. DINEO platform started as a website with a very limited set of functionalities. It was born as a probe of concept (POC) for a potential very profitable business. This POC exceeded our best expectations and hundreds of thousands of customers found a fantastic solution in our offer. It is obvious that the service platform, this POC created only to demonstrate that business was successful, was developed to offer services to a lower number of customers. Servers started to reach all their limits. Customers started to demand a more customized attention and more specific services.
All requirements were collected. A new design started from the scratch. We might make the business sustainable and the growth and operations couldn’t be frozen.
First decision made was to host all applications on cloud using Kubernetes. This technology offers us a huge agility and optimization of resources. We have saved, not only time, but also money. Nowadays our platform can provide services to more customers with lower investments. Therefore, we have raised profits.
On the other hand, we changed our code language. We are developing the new platform using Python. Main reasons for our decision were a quicker development and more accuracy in mathematic operations. This new platform has been designed and developed creating a set of microservices. The goal is to be as much agile and customized as possible. The reason is the continuous changes in governmental regulations, APIs from providers or our own new products.
Linked with the previous point, all financial processes and services are critical in Quality of Service. We have implemented Jenkins, which enables our development team to reliably build, test, and deploy our code, and SonarQube to catch bugs and vulnerabilities in our application, with hundreds of automated Static Code Analysis rules.
Finally, we would like to share our data area. This new development has been design considering from the scratch Data Analytics requirements. It means, data bases, processes and flows have been design taking into account the whole analytic process which will run afterwards. In terms of Artificial Intelligence, we have used machine learning to define the optimum number of clusters to classify our customers. In addition, we analyze our default customers and can predict if we will be able to recover the money or not. In the negative case, we will be able to sell this set of default customers to specialize companies and try to reduce our losses. Other success case around IA, is the customized loan. We are able to offer a customized loan based on our historic reducing losses and default cases.
In summary, our new platform takes all advantages from environments on cloud and kubernetes to optimize resources and timings. A full flexible infrastructure which offers us maximum scalability and a top quality of service (Jenkin and SonarQube). A new code in Python with minimum development timing. A new design considering Data Analytics processes from the beginning. Running AI algorithms to optimized business processes and provide a customized service. Finally, all these evolutions are running while the current platform is providing services to +800K users.
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Our success case is based on the evolution from a monolithic to a distributed microservices platform hosted on cloud. DINEO platform started as a website with a very limited set of functionalities. It was born as a probe of concept (POC) for a potential very profitable business. This POC exceeded our best expectations and hundreds of thousands of customers found a fantastic solution in our offer.