Advising on development and adaptation of products and services, such as crowd-funding, P2P Lending, e-payment and money institutions, regulatory and compliance, tax structuring and tax legal matters, smart contracts and traditional commercial contracts, asset tokenization, intellectual and industrial property protection, data management, data security, data protection and cybersecurity, capital raising, robot advisers and artificial intelligence, consumer protection, M&A, competition law and employment matters;
F inTech, RegTech and InsurTech is the result of the emergence of advanced technologies in the economy. FinTech being the combination of Financial and Technology, presents itself as computer pro-grams and other technology used to support or enable banking and financial services. Since its inception, FinTech has expanded in scope, now covering the full spectrum of finance and financial services: finance and investment, internal operations and risk management, pay-ments and infrastructure, data security and monetization, and consumer interfaces.
Some of the key technologies in FinTech include: Artificial intelligence (AI) as the science of making computer programs perform tasks such as problem-solving, speech recognition, visual perception, decision-making and language translation. AI has numerous applications and is increasingly used in the financial sector (e.g., robo-advice, transaction authentication). Increases in data processing and storage power, as well as advances in some of its sub-sets, most notably machine learning, have boosted AI in the recent years. Machine learning – machine learning can be considered a sub-field of AI that focuses on giving computers the ability to learn without being specifically programmed for such through handinputted codes. It is focused on parsing out and learning from large amounts of data, in order to make a determination or prediction.
Machine learning uses a variety of techniques, including neural networks and deep learning. In the past, AI tried to mimic human behavior through rules based methods, i.e., logic-based algorithms. Today, machine learning is data-based, that is, computers analyze a large volume and variety of data to recognize patterns, which do not need to be intuitive or rational, or translated into programming codes. This type of machine learning is already having impact on financial services and financial supervision.Big Data analytics – Big Data is a loose term to refer to large volumes of unstructured (e.g., emails, Internet traffic) and structured (e.g., databases) data whose analysis is not possible using traditional analytical tools. It includes data collected through networks such as the Internet or corporate intranets, and other data that organizations generate and store in the normal course of their business-es. Big Data analytics focuses on, for instance, discovering patterns, correlations, and trends in the data, or customer preferences. It can be based on machine learning or other technologies. FinTech as such results in the heightened cybersecurity (for example, it is impossible for humans or traditional technology to process big data at the speed and accuracy as machine learning does), smart trading (mobile payments), smart banking, such as digital-only banks (relying solely on a digital interface for customer onboarding and service delivery, saving retail and office costs), white label banking (companies without banking license or a regulatory infrastructure to offer financial products to their customers), lowered operating costs and heightened accuracy for KYC and AML procedures, automated web-based financial planning tools (automated tools to generate reports, graphs and insights with raw data), preventing fraud (algorithms which analyze transactional history of an account against data points and identify the likelihood of fraud), chat boxes for more positive customer service and robo-advisors.
RegTech being the combination of regulation and technology, presents itself as computer programs and other technology used to facilitate the delivery of regulatory requirements. It intends to streamline AML compliance and facilitate the use of social media and biometrics to transform customer due diligence experience, implement fraud prevention measures and assist banks to filter through the suspicious activity. In connection herewith, the newly emerged term SupTech has to be defined as Supervisory technology – innovative technology used by Financial supervisory agencies to support supervision, digitize reporting and regulatory processes. RegTech as such results in the automation of due diligence widely applied in the field of employee surveillance, compliance data management, fraud prevention, and audit trail capabilities.
InsurTech is being the combination of insurance and technology, presents itself as computer programs and other technology used to squeeze out savings and efficiency from the current insurance industry model in order to improve customer experience, simplify policy manage-ment and increase competition.
Our Law Office advises clients operating across FinTech, RegTech and InsurTech sector and covering the following legal aspects:
Advising on the liability regime, including the strict v. alternative liability regime applicable to the autonomous investment advisors considering personalization of advice, disclosure of any conflict of interest, best execution, responsibility for the robo-advisors, provision of services not covered by the fiduciary standard;
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