and their information, and FinTechs that contribute with technology and innovation to offer better products and services. For example, among the companies taken into consideration for this study, it is outstanding the case of DILE who feeds its AI technology with data from banks and insurance companies to generate predictive inferences resulting in better commercial strategies, reduction of churn rate and prevention of fraud. “Many people think that machine learning is AI, that a chatbot is AI. For us, the concept AI is simple and we communicate it in such a manner that intelligence is the ability to use certain learning to solve certain problem.” E. Marín, co-founder of DILE Institutional conversation, February 12, 2018 What this FinTech does is to train first the AI with historical data and then integrate the solution with the bank’s structure. A software model is implemented as service (SaaS) to keep functioning 24/7 the AI and the cloud infrastructure where data live. Likewise, Alkanza feeds its machine learning algorithms with financial market in- formation and data of the clients’ financial behavior to help their machine learning algorithms to build investment portfolios through automated algorithms through a very friendly online app. This allows banks, brokers, fund assessors and insurers may offer to their clients a simple experience to manage their investments at a lower cost, thanks to AI that performs everything in an automated manner. Instead of going to an office and talk to an advisor, the user downloads an app that studies his profile and makes recom- 1 mendations that may be managed from his mobile phone. 1 Other tasks that AI algorithms are assuming is compliance by financial institutions with regulations and are triggering another trend to apply technology known as RegTech (regulatory technology) that allow the regulator and its relationship with the institutions be more efficient. A TWO-WAY PATH 72