What is Suptech? An Overview of the Supervisory Technology

The world of Finance and the financial services rendered within its midst can no longer prevail in the prevailing times, without the engagement of technology in every aspect of their operations. 

 

With the power of the digital world rising, many infrastructures have sprouted over the recent years to support people in handling their money and in making payments. 

 

When we’ve got a massive section of the financial industry making use of technology for executing their operations, predicting future trends and for offering superior services, why should Financial service regulators be left behind? 

 

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They have been setting up fresh technologies for monitoring the markets, overseeing banks, brokerage firms and other financial institutions and for carrying out other administrative activities. 

 

The trend of building purpose-built technologies in the field of offering regulatory oversight has led to the rise of supervisory technology, or what we have come to know as Suptech.

 

Suptech is being developed and applied across the globe by many dominant regulators. These include US’s FDIC and Federal Reserve, FCA and Bank of England in the U.K and Africa’s National Bank of Rwanda. Many super regulators have also been engaged in suptech applications like the Bank of International Settlements, and the World Bank.

 

The unfortunate 2008 Financial Crisis, led to the sprouting of stringent and additional financial regulations, with other regulations not being left far behind. This has resulted in companies facing several time consuming obligations regarding compliance that also hold the risk of penalization.  

 

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This is where RegTech steps up and addresses this issue, aiding these companies in automating their internal data collection, data analysis, attestation, reporting, etc. 

 

Meanwhile SupTech aids regulators, i.e. the supervisors and other regulators of financial institutions and other industries in becoming more efficient, automated and in reducing expenses and errors.

 

 

What is SupTech?
 

According to the World Bank, Suptech “refers to the use of technology to facilitate and enhance supervisory processes from the perspective of supervisory authorities.” 

 

The BIS (Bank for International Settlements) defines suptech as “the use of technology for regulatory, supervisory and oversight purposes.”

 

Suptech implies the adoption of innovative technology like AI and Machine Learning by regulators aka supervisory agencies for supporting supervision.

 

Just like other Regtech solutions, Suptech is focused on maximising efficiency by applying automation, optimizing the operational and administrative operations and by digitalizing the working tools and data. 

 

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The primary goal is reducing the burden extended towards companies and promoting improved reporting, more prompt monitoring and better overall compliance on the part of the regulator, which in turn would lead to lower expenses and improved supervisory resource allocation. 

 

For instance, Suptech applications make use of technologies like machine learning for detecting gaps and disparities in the quality and also for automating the process of data consolidation, cleaning, validation as well as ensuring the quality. In the case of data analytics, the suptech solutions can minimize the hassle of crunching the data by automating it and allow for better analyses. 
 

Towards the end of December 2020, the Reserve Bank of India (RBI) stated that the bank has started making use of Suptech and Regtech technologies for keeping track of its regulated entities. 

 

The Suptech technology has been centered around two primary aspects of financial supervision namely, data collection and data analytics which have been identified by the Bank of International Settlements (BIS). 

 

Under Data Collection, aspects like making use of Suptech applications for reporting, data management and virtual assistance come into the picture. In the present scenario, the transaction data history is sent in varying reporting formats which leads to heavy procedure of data analysis and collection of data. 

 

• In case of Automated Reporting, the activities include data push vs data pull where the the entities being supervised push data through M2M API or the regulators can programmatically pull the data directly from the supervised entities and they also include real time monitoring. 

 

• In case of data management, activities like the validation of the data, data consolidation (in which various sources of structured and unstructured data are combined for analysis) and visualization aka reports are included. 

 

• In the case of Virtual assistance, AI Chatbots are used for addressing consumer complaints and for helping regulated entities. 

 

Under Data Analytics, aspects like market surveillance, misconduct analysis and macro and micro prudential supervision are included.

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SupTech Use Cases

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• In case of Market surveillance, the Suptech technologies permit massive sections of data to be analysed for the purpose of market surveillance and identifying suspicious trading, which includes market manipulation as well as insider trading. 

 

• In case of misconduct analysis, many of the suptech technologies are focused on identifying the potential of any CFT or AML invasions. Meanwhile the machine learning algorithms can aid in detecting any possibility of fraud and also for predicting mis-selling (a sales practice where the product or service is purposely misrepresented or a customer is deluded regarding how suitable it is).

 

• In case of Macro and micro-prudential supervision, machine learning can be adopted for the evaluation of credit risks while Neural networks can be adopted for identifying liquidity risks. 

 

The Suptech technologies can be effective in analysing public sentiment and financial stability, for detecting any scope of emerging risks in the financial system, and in evaluation of policies.

 

 

Risks associated with Suptech solutions

 

While Suptech does indeed present a set of fresh ways for making the regulator’s operations more precise and fast paced, that doesn’t mean that the technology doesn’t come with its own set of challenges. Some such key challenges have been highlighted below :  

 

1. Cyber Risk

 

With cyberattacks becoming more common and regulators becoming more vulnerable to them owing to supervisory processes now shifting on to digital platforms, the more the technologies and digital solutions are applied, the more the scope and entry points for cyber-attacks expands. 

 

This is particularly owing to varying areas becoming interconnected and many platforms being shared or disclosed. Because of these causes, Suptech operations pose the threat of enhancing the overall susceptibility of the supervisor for cyber risks. 

 

 

2. Black Box Issues

 

While AI and Machine Learning technology is capable of producing dependable and intricate insights as long as the input is accurate and the output has been verified, use of AI often results in Black box issues. 

 

In case of a black box the automated decisions are undertaken by lengthy and unpredictable algorithmic interactions which makes it quite a challenge for the supervisors to be able to gauge how certain decisions were taken, thus becoming a hindrance to conducive decision making. 

 

 

3. Legal Risk

 

These risks regarding data protection or system inherent biases are likely to arise whenever the regulators start managing a massive number of sensitive datasets. The use of Suptech raises several issues with regards to accountability.

 

 

4. Operational Risk

 

In this world of automation and digitization, any discrepancy in internal systems, procedures or policies owing to fraud or breaches can have a pretty adverse effect on the regulator’s activities. 

 

Owing to the high degree of interdependencies between the systems and their interconnected nature, any breach in a single system can lead to a domino effect which can prove to be difficult to curb. 

 

 

5. IT Risk

 

There are numerous legacy systems possessed by regulators which might not have been updated and that might work in a bubble. Opting for fresh solutions has been made more complex because of the need of ensuring that they are appropriately linked through existing systems and there is no data loss. 

 

 

Use cases of Suptech

 

As highlighted in a World Bank document, Suptech has many use cases across the world, a handful of which have been mentioned here : 

 

1. Automated Data Reporting

 

In case of data pull approach, an approach in which the supervisory authors directly “pulls” the data via the IT systems of the entities being supervised. 

 

One Automated Data Reporting Suptech solution has been executed by the National Bank of Rwanda (BNR) which is based on the data-pull approach. The approach allows the Bank of Rwanda to gain access to the IT systems of regulated entities and collect the relevant data from the entity’s IT systems.  

 

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The aim behind the approach was minimizing the costs and time, delaying reporting and enhancing the quality, scope and the data credibility.  
 

 

2. Credit Risk Assessment

 

Countries which have executed Suptech solutions for credit risk assessments include the Bank of Italy (BoI) De Nederlandsche Bank (DNB), the Central Bank of the Russian Federation (CBR), China Banking and Insurance Regulatory Commission (CBIRC), and the Bank of Thailand (BOT). 

 

For instance, The Bank of Italy has delved into how loan default forecasting can prove to be advantageous through the adoption of machine learning algorithms, by mixing varying data sources for the purpose and then feeding it into a machine learning tool that produces forecasts of loan defaults, to compare it with standard models. 

 

 

3. Workflow Management

 

Suptech Solutions supporting automated workflows are termed as Regulatory Information and Workflow Management System (RIS) systems. 

 

RIS workflow can be adopted for automating many primary regulatory operations that include off-site and on-site supervision, licensing, monitoring of the compliance of financial institutions, prudential reporting tracking, management data production and the support of enforcement procedures. 

 

As per the modular structure of any RIS solution, a wide number of options are available for bolting on numerous varying tools. It also offers a certain degree of flexibility for executing a RIS solution of different scope, and is an entity wide solution rather than being confined to a single functional area. 

 

An example of this is a regulator opting for implementing only a licensing workflow solution, like the one that had been leveraged by the Bank of Portugal and the Federal Reserve Bank of San Francisco. 

 

 

Conclusion

 

It will definitely be intriguing to observe how the Suptech solutions benefit the regulatory agencies in the coming years as well as how they revolutionize the regulatory world in this journey.