Non-Technological and Technological (SupTech) Innovations in Strengthening the Financial Supervision

Non-Technological and Technological (SupTech) Innovations in Strengthening the Financial Supervision

Marcin Flotyński, Kamilla Marchewka-Bartkowiak
DOI: 10.4018/978-1-7998-4390-0.ch006
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The application of non-technological and technological innovations provides support to supervisory institutions in the digitization of reporting and regulatory processes. This chapter deals with international applications of innovations in financial supervision (SupTech). The aim of the chapter is to present the most popular technological and non-technological solutions along with an evaluation of their usefulness in exercising supervision over the financial market. The authors discuss the types of innovations and the reasons for implementing them by supervisory institutions. Furthermore, they describe the most important non-technological supervisory solutions and technologies that supervisors can apply in creating SupTech tools. The study utilizes, among other things, data from reports and elaborations by central banks, supervisory institutions, and consulting companies. The authors' main focus is on the analysis of the solutions utilized by European Union member states.
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Supervisory Innovations

The development of financial technologies brings about new challenges in terms of the supervision over the financial services market in the context of reducing both micro- and macro-prudential risks. For more than a decade public supervisors (including central banks and specialized agencies) have been adjusting their operations to the continuous growth of competition among subordinate institutions and sectors. The new approach to supervision relying on supervisory innovations has not replaced the traditional approach yet, but it is becoming complementary to it. This does not mean, however, that some of the tools dedicated to technological services today will not, in the future, cover all of the supervised institutions in terms of monitoring their communication with clients, data reporting, or timely responding to identified irregularities.

Hence, “supervisory innovations” can be construed as regulatory, institutional, procedural and technological solutions aimed at enhancing the effectiveness of the supervision over financial technologies market and minimizing the risks involved.

Key Terms in this Chapter

Self-Organizing Maps (SOM): A type of neural network which uses an unsupervised training algorithm and a process known as self-organization.

Data Lake: A repository for storing unstructured and structured data that is downloaded in its raw form and stored by a highly scalable, distributed files system known as open source.

Machine Learning (ML): A subset of artificial intelligence aimed at enabling computers to analyze and learn from huge amounts of data with a view to making forecasts.

Data Pull Approach: A technology controlled by the financial supervisors used to gather business data directly from the institutions’ operational systems. It is useful for reporting process and it ensures good quality information for decision making.

Tokenization: A process of digital token issuance, based on the blockchain technology.

Application Programming Interface (API): Definitions, protocols and tools that determine the way various applications interact with each other.

SupTech: Technological tools used in financial supervision.

Geographic Information Systems (GIS): A computer system covering analyzing, managing, intercepting, hardware, software and displaying information in the geographic context.

Text Mining: A multidisciplinary field involving information mining, text analysis, categorizing, grouping, visualization, data exploration and machine learning.

Predictive Modeling: A mathematical and computing technique for predicting events or results.

Distributed Ledger Technology (DLT): A data base shared by a great many parties (nodes) with a view to conducting jointly agreed transactions based on the so called consensus mechanism.

Natural Language Processing: An area covering artificial intelligence and linguistics which involves processing the information saved in a computer’s database so as to make it comprehensible for human beings.

Innovation Lab/Accelerator: Special one-off or long-term programs established by public institutions in cooperation with external partners. Basically, innovation accelerators allow sponsors (also from the financial sector) to finance and test prototypes of new technological products in a safe supervisory environment.

Network Analysis: A process of examining structures by means of networks and graphs theory. It is a set of integrated techniques for presenting the relationships between specific entities and analyzing the structure.

Big Data: Huge amounts of unstructured data (e-mail messages, internet traffic, etc.) and structural data (e.g., data bases) the analysis of which is not possible by means of traditional analytical tools.

Dashboards: Configurable, dynamic tools for interactive reporting, which download and visualize data automatically.

Fintech: Innovations in financial services based on technologies that can lead to the creation of new business models, applications, processes and products, thus having a significant impact on the financial market and institutions and the way financial services are rendered.

Neural Network: A set of interconnected units or nodes which are similar in functioning to the animal neuron with the ability to learn from training patterns. It is modelled on the structure of the human brain and can be used to solve problems in such fields as economics, statistics, and technology.

Artificial Intelligence (AI): A set of technologies enabling computers to perceive, learn, reason, and offer help in making decisions so as to solve problems in a manner that imitates the human way of thinking.

RegTech: A tool or a platform that facilitates regulatory compliance, making it more efficient.

Random Forest Algorithm: Is a popular algorithm for machine learning. It can be used as regression and classification algorithm. It creates multiple decision trees and merges them together to make predictions.

Cloud Computing: Facility using remote and co-shared host servers on the Internet for storing, managing and processing data.

Robotic Process Automation (RPA): A method of automating repetitive processes with software which works like a human being and performs tasks on computers.

Web Scraper: A technique of mining data from the Internet and saving it in the system or data base for future download or analysis.

Chatbots: A program utilizing artificial intelligence, which interacts with another chatbot or a human being, giving the impression of interacting with a human being.

Smart Contracts: An intelligent (digital) contract, which can perform itself automatically on fulfilling specified conditions.

Blockchain: A kind of distributed ledger technology. Blockchain is a type of database used for transaction recording. It is copied to all of the computers in a participating network. Data is stored in ‘blocks’ which are fixed structures.

Innovation Office: Dedicated centers of contact for firms to raise enquiries with competent authorities on fintech-related issues and to seek non-binding guidance on the conformity of innovative financial products, financial services or business models with licensing or registration requirements and regulatory and supervisory expectations.

Regulatory Sandbox: Specially organized environments of legal supervision created by financial supervisor (or regulator) for testing possible effects of new technological solution implementation in an isolated environment (e.g., a group of clients).

Data Cubes: A tool used to evaluate data aggregated from different viewpoints.

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