The financial landscape has seen rapid changes in the recent past with more focus on the one-stop-shop experience in regulatory requirements. The supervisory agency and regulators focus has changed from just being monitors towards adherence to predictive solution decision-makers for better controls with smart technologies and innovations from Regtech, Suptech and Fintech. The role of these technologies has changed in recent past with the growing need to be more precise in regulatory compliance.
Regulatory Challenges and Role of Supervisory Agencies and Regulatory Bodies
The role of supervisory agencies and regulators have come under increased pressure as they must move from a reactive oversight towards a more proactive/predictive approach in Real-Time Regulatory Compliance. Over the last decade, there has been a growing trend in financial institutions and regulatory bodies to use technology to support new business models and to solve regulatory and compliance requirements more effectively and efficiently.
The recent changes of mergers and acquisitions, economic conditions and consumer behaviors of financial institution – banks and non-banking entrants (including the “BigTech” e-commerce giants) through marketplace platforms and new business models have an impact on regulatory challenges from data privacy to credit, market, and operational risk pillars of systemic “risk exposure and classification for the central banks and regulatory
The Need for Smart Solutions (Supervisory Technologies)
The advent of new technologies to improve decision making empowerment for supervisory agencies, FI and regulators have taken a new shape, the contribution of “Regtech” application of financial technology (Fintech) for regulatory and compliance requirements and reporting by regulated financial institutions are remarkable while the new era of “Suptech” emerging new trends with AI and ML capabilities and Data as key driving factors (Suptech is the term for any application of Fintech for regulatory, supervisory and oversight purposes used by the central banks/monetary authorities).
Role of Suptech
Suptech enables regulators and supervisory authorities to improve efficiency, reduce manual processes and make effective use of data to enhance diligence and vigilance in risk monitoring and management in real-time, improving the resilience and stability of the broader financial system. The Introduction of SupTech made life of supervisory authorities easier, enabling them to make pro-active real-time decisions after implementing data-intensive case management tools.
Data-Driven SupTech Solutions
The major roles of Suptech and benefits are seen in Data Collections & Data Analytics:
Defining the Right Data: The Bank, through its role of defining reporting across the financial sector, plays an important part in shaping how firms approach their own data. Seamless transforming data collections support the improvements to the quality and to the usability of financial sector data, data standards, widely used, represent a public good with wider benefits than just reporting efficiency, and can support private innovation. Efficiency and improvement to Data Collection could therefore complement other initiatives such as (example) the Bank’s renewal of its Real-Time Gross Settlement Service, which can be used to drive the adoption of payments messaging standards and legal entity identifiers; and builds efficiency and resilience of post-trade processes and operations.
Streamlining the manual process of know-your-customer data collection and suitability analysis through compliance management solutions for firms of all sizes and shapes are now able to automate the burdensome work of regulatory change management through AI-powered knowledge automation solutions.
Specifically, the ability to aggregate and analyze large sets of it — is what has fueled the deep learning revolution of the last decade.
Data Analyses, Data Collection, digitalized regulatory reporting and Artificial Intelligence/ML plays vital role in the Suptech ecosystem. The supervisory and regulatory authorities are more keen to catch the bad dreams early to ensure reduce the economic impacts at the very infant stage.
Data Collection: Having robust reporting backed with information on ever-changing regulations is key. Historically, the standardized reporting templates played a vital role in the collections of data and reporting them, however with the advent of new Suptech requirements to supervise better and quicker, no new solutions emerge like Aurep. Austrian Reporting Services GmbH (AuRep) was founded for this purpose. AuRep develops and operates a common reporting platform, or GMP for short, for most Austrian banks. This new platform complies with all new OeNB reporting regulations (Basic and Smart Cubes) and supports the processing of old documents. The processing and preparation of all data for reporting to the OeNB takes place in a central, client-separated software. Compliance with the highest security standards is a matter of course for AuRep. (Aurep.com)
Data Programs and Scope of data
Mckensy Whitepaper on Data Management (McKinsey Abstract): https://www.mckinsey.com/business functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail
Sup Tech Examples:
Oesterreichische Nationalbank (OeNB):
- OeNB has initiated the revamp in reporting platform. With the next-gen solutions from Aurep ((Austrian Reporting Service GmbH)) has developed an advanced reporting platform which is leveraged by supervised entities and supervisors. The transit of data input with data input methodology from Banks and financial institution is streamlined allows greater level of integration helping to resolve the challenges of the speed of data received by regulators and supervisors’ agencies to make quick decisions
- The National Bank of Rwanda has established another variant of the granular data extraction model. Pre-defined templates with guidelines have been shared with all reporting institutions to obtain data electronically. The National Bank ‘pulls’ data based on these templates from firms’ core systems into the National Bank’s data warehouse and performs transformations on them in order to meet reporting requirements for internal and external users.
- BSP has undertaken a pilot project to replace a legacy of e-mailed Excel-based templates with a single schema for submission by each bank via API, with the aim of moving to a ‘pull’ model.
Data Strategies for Suptech – Data Push or Data Pull Strategies
Data Push: Push-based strategies are the default model. Automated the delivery on pre-determined specification, a forwarder is installed close to the source of the data, or built into the data generator/collector and pushes the events to an indexer.
Data Pull: This approach provides significant flexibility by letting you create reports from multiple data sources and multiple data sets, and by letting you store and manage reports with an enterprise reporting server. Pull based cannot be reliable for real-time reports and information. Also, Pull base system most tolerate, its lack of real-time information cannot be best fit for supervisory Financial Institution as they demand real-time reporting with greater insights to financial health conditions of FIs.
Artificial Intelligence /Machine Learning
Supervisors can use machine learning tools to create a “risk score” for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution’s profile, compliance history, reporting behavior, and more.
- Analytics tools are also undergoing rationalization and enhancement. Supervisory focus on analytics. It is now a centralized hub for analytics/related data. Historically, pockets of analytics capability sat in myriad parts of a financial authority – in research and statistics, monetary policy, market supervision, financial crime units and macro surveillance – and such groups will continue to maintain their individual analytics competencies. However, as financial authorities look to keep pace and do more with limited resources, shared-service analytics functions are a priority.
- Supervisors can also use Network Analysis to assess an entity’s exposure to money laundering risk. DNB (De Nederlandsche Bank), for example, analyzes transactional data in order to detect whether related entities are sending funds to the same party through different financial institutions.
- A number of regulators, including ASIC (Australian Securities and Investments Commission), the Bank of Mexico, and the FCA (Financial Conduct Authority), are leveraging natural language processing technologies to audit the promotional materials, prospectuses, and financial advice documents that are produced by financial institutions.
Abstract from FINXTRA, FCA, BIS and World Bank Report
SupTech can be deployed by the regulators to make standardized reporting easier and provide virtual assistance to the customers (banks). With automated data collection the overall data management becomes streamlined and insights can be quickly derived from historical data and patterns.
On the analytics side, SupTech can enable the regulators to have a precise overview of market surveillance and misconduct analysis. Both of these aspects are either missing or difficult to implement using traditional processes. Even when the results are out the time-lapse is quite significant making the insights less effective. Currently, regulators tend to act post happening of a black swan event but using analytics they can forecast and prescribe remediation measures to mitigate the overall risk associated with the financial ecosystem.
AI-Powered Real-Time Monitoring by Suptech
Artificial Intelligence is a magic crystal ball in the world of technology; scoring best range of applications, supported by financial institutions for automating data-intensive tasks and workflows. Advancements in ML, AI and Big Data has led to further enhancement of supervisory processes. For instance, the use of machine learning tools to monitor potential market abuse practices probably has the potential to improve market integrity; authorities such as the ECB and the U.S. Fed are using Natural Language Processing (a form of AI) to help them identify financial stability risks. Another potential application of AI and Machine Learning is to detect collusive behavior and price manipulation in the securities market potential misconducts that can be especially hard to detect using traditional methods.
AI creates Real-time supervision, by looking at data as it is created in the regulated institutions’ operational systems; Exceptions-based supervision: automated checks on institutions’ data and other information automatically collected and analyzed by the supervisory agency identify “exceptions” or “outliers” to pre-determined parameters for expected behavior, triggering supervisory action.
- Automated implementation of supervisory measures. e.g. automatically created direction for capital increases based on automated data analysis and decision-making;
- Algorithmic regulation and supervision in areas such as high-frequency trading, algorithm-based credit scoring, robo-advisors or any service or product that automates decision-making;
- Dynamic, predictive supervision by using machine learning, which could move supervisors to take supervisory actions in a preemptive manner based on predictive behavioral analysis.
Abstract from Ascent/BIS
Suptech has received a mixed reaction from both from Technology world and Financial world. The smart technology integrations of AI and ML have made it next-gen ambitious hope for better, faster and accurate regulatory compliances for all FI requirements. Regulatory compliance is a complex domain space with every changing compliance requirements’ ensure the financial health of the Banks, Firm and FI are well supervised and managed, the strategic role of supervisory agencies and regulatory demand self help one stop out of box solutions which can support end to end monitoring, calculations and assessments with clear reporting and dashboard capabilities, Suptech has a promising futuristic vision with AI and ML solving the problems of supervisor agencies.