Journalist: Amy Tsai | Source: PANews

Blockchain and artificial intelligence are going to be crucial components of Regulatory Technology, or Regtech, according to a recent report by JD Finance.

Regtech can be divided into two parts — Supervisory Technology (Suptech) and Compliance Technology (Comptech).

Data Is the Main Drive


Regtech is a combination of administrative regulation and technology. This publication refers to “Innovative technology in financial supervision (suptech) — the experience of early users,” a report released by the Financial Stability Institute (FSI) under the Bank for International Settlements (BIS) earlier in July. Regtech, as defined by JD Finance, is a more efficient solution in data collection and management based on technologies like cloud computing, artificial intelligence and blockchain within a more tight-knitted context of finance and technology.

From Suptech’s perspective, regulatory authorities have sufficient momentum to develop Regtech due to a more complicated and fickle market environment with Fintech applications. Since the 2008 financial crisis, Regtech has reached its unprecedented importance. Regulatory authorities are eager to obtain more comprehensive and accurate data. Moreover, authorities need to proceed enormous data sent by individual financial institutions with innovative technologies in order to improve the regulatory efficiency. Fintech has brought brand-new risk scenes and features that require regulatory authorities to actively respond with advanced technologies alike.

Suptech is comprised of data collection and data analysis. For the former, it can generate automatic and real-time reports, manage data (including data consolidation, data validation, data visualization, and cloud computing), and internally circulate consumers’ and regulated institutions’ information collected by virtual assistants.

For automatic reporting, the Oesterreichische Nationalbank (OeNB) utilizes Austrian Reporting Services (AuRep), owned by seven major banking sectors in Austria, as an intermediary platform to receive data from individual banking sectors and convert them into an accessible, comprehensive, and consistent report and transmit the information to OeNB in a timely manner.

For data management, Regtech can conduct data validation, consolidation, visualization, and cloud computing. Data analysis is applied in four aspects: market regulation, monitoring and analysis of misconduct, micro prudential supervision, and macro prudential supervision.

Bangko Sentral ng Pilipinas (BSP), the central bank of the Philippines, develops a chatbot to answer consumer’s reporting and pays close attention on specific areas through data classification and integration. Financial Conduct Authority (FCA) of U.K. are also trying to use virtual assistants to help regulated institutions understand legal requirements, optimize options for consumers, and evaluate the regulation’s impact by interpreting the law.

“Micro prudential supervision” can be used on credit risk assessment and liquidity risk analysis with artificial neural network. For instance, Deutsche Nationalbibliothek (DNB) is researching on an automatic encoder to examine the abnormalities of the Real Time Gross Settlement System. “Macro prudential supervision” is used to identify financial risks, maintain the financial stability, and so forth. The U.S. Federal Reserve System, European Central Bank, and Bank of English all use heat maps to automatically analyze the data collected by regulated institutions in order to highlight the potential financial stability issue.

Blockchain Is Becoming the Major Component


According to JD Finance, Regtech is going to reach its comprehensive applications on financial regulation. The collaboration between regulatory authorities and regulated institutions will be the development trend. Countries and organizations are reinforcing Regtech’s applications before and after the regulation. In addition, while the regulatory authorities focus on developing and researching new technologies, they are also seeking cooperation between banking sectors and Fintech companies.

In 2015, NASDAQ launched Linq, enabling unlisted companies at stock change market to represent their equities digitally based on the blockchain technology. According to NASDAQ, the “proof of concept” of this technology can reduce 99% of risks. Moreover, both issuers and investors can manage documents online, minimalizing the administrative procedure. It also launched ChainCore and SMARTS Surveillance System with Citigroup and Gemini, respectively. ChainCore realizes global payment management and automated reconciliations while SMARTS provides safety measures to regulate market manipulation and is considered the most widely deployed surveillance system in the world.

Blockchain will be an indispensable part of Regtech. Features like smart contracts and smart surveillance reports will be further developed and applied to establish trust. IBM created Ledger Connect with CLS Group, aiming to use the blockchain technology in various financial fields. As of now, it has attracted more than 9 financial institutions, including Citigroup, to participate in validations and tests.

According JD Finance, Regtech needs to specify its data accessibility and its position in regulation. For example, the information that can be collected and used into analysis as well as the protection of commercial confidential information and personal privacy should all be regulated by law. In order to evaluate and improve the regulatory efficiency, it is also important to consider the reliability of information collected by Regtech and whether it should be a reference or decisive factor that impacts the authorities’ decisions.

What is known for sure is that Regtech is approaching to institutionalization as the Suptech expands its applications. Confluence’s Unity NXT, a regulatory reporting platform, permits mutual funds to repeatedly use the regulatory data set when completing the reporting process through data collection and automated operations. Mutual funds then can meet the quality, expandability, and time requirements of reporting.