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Suptech tools for prudential supervision and their use during the pandemic

iii

Contents

Executive summary ........................................................................................................................................................................... 1

Section 1 – Introduction ................................................................................................................................................................. 3

Section 2 – Types of suptech data analytics tools for prudential supervision .......................................................... 5

Tools for mainly qualitative data ....................................................................................................................................... 6

Tools for mainly quantitative data .................................................................................................................................... 8

Tools for both qualitative and quantitative data ......................................................................................................... 8

Section 3 – Suptech tool lifecycle observations .................................................................................................................... 9

Section 4 – Suptech usage during the pandemic ............................................................................................................... 10

Section 5 – Practical considerations ........................................................................................................................................ 12

Section 6 – Conclusion .................................................................................................................................................................. 14

References .......................................................................................................................................................................................... 16

Annex 1: List of authorities that responded to the survey .............................................................................................. 17

Annex 2: Suptech use cases ........................................................................................................................................................ 18

Suptech tools for prudential supervision and their use during the pandemic

Suptech tools for prudential supervision and their use during the

pandemic

Executive summary

Financial authorities use suptech tools for a range of activities, including data analytics for

prudential supervision whose use cases have recently grown. An earlier Financial Stability Institute (FSI)

publication found that most suptech tools were used for reporting and misconduct analysis, with relatively

few deployed for prudential supervision (di Castri et al (2019)). The Financial Stability Board (FSB (2020))

found similar results, though it observed a rise in suptech use cases for prudential purposes. The FSB

attributed the increase to the automation of certain repetitive tasks in prudential supervision.

The pandemic prompted authorities to leverage more suptech tools in day-to-day

supervision. Travel restrictions and social distancing protocols severely curtailed on-site inspections and

led to a simultaneous shift of nearly all supervisory activities to an off-site surveillance approach. To help

supervisors assess the prudential soundness of financial institutions remotely – including some tasks that

were previously conducted on-site – authorities with existing suptech tools used them more extensively;

at the same time, they also recognised the need to develop new data analytics tools for prudential

purposes. Therefore, it is not surprising that authorities reported using, developing or experimenting with

71 discrete prudential supervisory tools as of this publication, up from only 12 tools in 2019.

Broader technological developments facilitated the migration of supervisory activities to a

virtual environment and underpinned the wider use of suptech tools for prudential purposes. Data

management platforms, file exchange protocols, collaboration software and communication tools enabled

the shift to virtual supervision, partially offsetting limited on-site inspections. Meanwhile, the growth of

non-traditional data sources that can have a bearing on a firm’s risk profile and the advent of new analytical

tools to help process and analyse data – such as artificial intelligence and machine learning – provided

authorities with opportunities to deploy a range of suptech tools for prudential supervision.

This paper takes stock of suptech data analytics tools used for prudential purposes in 20

jurisdictions and explores the associated benefits, risks and implementation challenges. The findings

are based on responses to an FSI survey by members of its Informal Suptech Network, combined with

follow-up interviews with selected jurisdictions. Suptech data analytics for prudential supervision include

tools to support supervisory risk assessments, such as credit, market, liquidity and operational risks and

their implications for firm-wide earnings, capital adequacy and governance.

The 71 prudential suptech tools examined in this paper are classified into three categories

and subsequently divided into subcategories. The top-tier categories are based on the type of data the

tools scrutinise and are labelled as follows: (i) “tools for qualitative data”; (ii) “tools for quantitative data”;

and (iii) “tools for qualitative and quantitative data”. Within each of the three categories are various

subcategories that classify how the tools are used. Tools that rely on mainly qualitative data represent

slightly more than half of those examined; these tools are used for text analysis, text summarisation,

information classification or sentiment analysis. Tools that mainly look at quantitative data and those that

utilise both quantitative and qualitative data account for approximately 25% of use cases each. The former

is used for risk identification, while the latter may be used for network analysis, peer group identification

or automation of inspections.

Jermy Prenio (Jermy.Prenio@bis.org) and Raihan Zamil (Raihan.Zamil@bis.org), Bank for International Settlements, Kenton

Beerman (Kenton.Beerman@ny.frb.org), Federal Reserve Bank of New York. We are grateful to Joshua Tang, Helio Vale, Joy

Wann and Diana Zaig for helpful comments. Marie-Christine Drexler provided valuable administrative support.

2 Suptech tools for prudential supervision and their use during the pandemic

While suptech tools vary in design and purpose, all share at least one of two overarching

objectives of extracting deeper supervisory insights and enhancing the efficiency of the supervision

process. Tools that scan qualitative data often use natural language processing (NLP) and other artificial

intelligence to comb through an astonishing array of materials to quickly find, summarise, classify and

present relevant information for further review. These tools allow supervisors to consider a broader range

of information in their prudential risk assessments. Tools that rely on quantitative data facilitate

identification of high-risk banks and drivers of specific risks within banks, enabling a better allocation of

supervisory resources. Tools that consider qualitative and quantitative data allow supervisors to assess

relationships between entities that may not be apparent to the human eye; to enable construction of

enhanced bank peer groups, facilitating more consistent supervision of firms with similar risk profiles; or

to automate aspects of the inspection process, freeing up supervisory resources for higher-order tasks.

Suptech tools were widely deployed during the Covid-19 pandemic, particularly those that

scrutinise qualitative data and support risk identification. The migration of on-site activities to off-site

work, in conjunction with various ad hoc reports requested during the pandemic, added to the mounting

stack of existing structured and unstructured data that required review. In the virtual environment, suptech

tools proved indispensable, enabling supervisory reviews of corporate governance and asset quality, both

of which are typically assessed on-site and often drive a firm’s overall risk profile. NLP tools helped

supervisors pinpoint corporate governance risks from voluminous documents that might otherwise not

have been possible. Risk identification tools were also utilised to spot potential credit exposures that may

be misclassified or underprovisioned, providing supervisors with a specific list of borrowers for follow-up.

Notwithstanding these tangible benefits, formidable implementation challenges remain,

hampering wider adoption and acceptance of suptech tools. A key issue is the limited data science

skills of supervisors. To address the skills gap, continued training of supervisors combined with hiring data

scientists may help. Other critical issues involve data quality, particularly the unstructured data which

underpin some suptech tools and the parameters that drive suptech outputs. An overly tight calibration

might lead to the tool missing supervisory issues, while a very loose setting can result in flagging too many

irrelevant issues. These challenges may point to a broader need to develop or update a suptech strategy

that helps to facilitate supervisory buy-in and guide authorities’ deployment of various suptech tools.

As suptech tools take on a greater role in prudential supervision, supervisory judgment

may diminish. Suptech tools are automating lower-value, labour-intensive tasks and supporting higher-

value, judgment-based functions. These trends are now accelerating, particularly the development of tools

that target complex risk assessments that entail judgment. As these tools get operationalised, supervisors

could rely less on their own judgment and depend more on the suptech output to identify key supervisory

issues. If this transpires, it may lead to supervisory blind spots and a broader loss of institutional knowledge

based on the art of judgment-based supervision. While authorities have emphasised that suptech tools

support, rather than replace, supervisory judgment, explicit policies that acknowledge the tensions

between, and outline the respective roles of, supervisory judgment and suptech tool outputs, could help.

Experience with virtual inspections and wider use of suptech tools have sparked a broader

debate on the future of supervision. During the pandemic, authorities demonstrated the ability to shift

all supervisory activities to an off-site stance. This has blurred the lines between on- and off-site roles,

prompting a rethink on the modes of supervision in the post-pandemic, digital era. The shift to virtual

supervision, however, was not frictionless. On the supervisory side, managing remote teams became a

challenge; and while communication tools enabled virtual meetings, there are no good substitutes for in-

person meetings with bank staff, which provide supervisors with critical insights on the quality of a bank’s

internal controls and risk management practices. On the technology front, the pandemic highlighted some

gaps in authorities’ own technological infrastructure, while exposing varied technological capabilities of

supervised firms. While there will always be a crucial role for on-site inspections, there may be scope for

more supervisory work to be conducted off-site, depending, in part, on the evolution of technological

innovations, including the broader deployment of suptech tools in prudential supervision.

Suptech tools for prudential supervision and their use during the pandemic

Section 1 – Introduction

1. FSI Insights no 19 (“The suptech generations”) defined suptech as the use of innovative

technology by financial authorities to support their work.

For this purpose, “innovative technology”

refers to the application of big data or artificial intelligence (AI) to tools used by financial authorities.

“Financial authorities” refers to authorities with supervisory and non-supervisory functions (ie financial

intelligence units without supervisory mandates). As such, suptech use cases can be found in the whole

range of activities that financial authorities undertake – from data collection, including data management,

to data analytics (Chart 1). Within data analytics, suptech use cases can help in market oversight, conduct

supervision and prudential supervision. This paper focuses on suptech data analytics tools for prudential

supervision.

Mapping of suptech to different supervisory areas

Chart 1

Source: Adapted from Broeders and Prenio (2018).

2. Suptech data analytics tools for prudential supervision made up only a small fraction of

total use cases, but this proportion may be changing. Of the 99 suptech use cases examined in FSI

Insights no 19, the majority were for reporting (32%) and misconduct analysis (30%), with only a few for

prudential supervision (12%).

FSB (2020) found a similar pattern in the distribution of suptech use cases

but noted the increased in prudential use cases in recent years. It attributed this increase to the relatively

rule-based nature of some prudential tasks. Authorities therefore were able to easily codify some of these

assessments in suptech tools, thus introducing efficiencies in the supervisory processes. Indeed, compared

with the suptech data analytics tools for prudential supervision examined in 2019, the number of tools

examined for this paper represents a significant increase (Chart 2).

di Castri et al (2019).

Ibid.

4 Suptech tools for prudential supervision and their use during the pandemic

Reported suptech data analytics tools for prudential supervision

Chart

Source: FSI surveys of central banks and supervisory authorities.

3. The pandemic has constrained supervisory activities and may have provided an impetus

for the development of more suptech use cases for prudential supervision. On-site inspections have

been severely limited or non-existent in almost all jurisdictions. The pandemic forced supervision work to

focus more on off-site monitoring, using whatever data and analytics tools supervisors had. Authorities

with operational suptech tools found them quite useful under the circumstances. At the same time,

authorities considered additional use cases that would have been useful given limited on-site inspections.

The shift to off-site activities during the pandemic, plus the expectation that the “new normal” might

continue to mean less on-site activities, may push authorities to leverage more suptech tools on a

permanent basis.

4. This paper provides an overview of the state of play of suptech data analytics tools for

prudential supervision in a number of jurisdictions around the world. It benefited from 21 responses

to a survey sent to members of the FSI’s Informal Suptech Network (see Annex 1 for a list of authorities

that responded to the survey). Survey responses were supplemented with interviews of some responding

authorities, to discuss their suptech tools in detail and/or clarify their responses. Section 2 describes and

classifies these tools according to the data they analyse and/or their objectives. Section 3 offers some

observations on authorities’ practices throughout the suptech life cycle, including governance,

identification of use cases, deployment to supervisors and measurement of effectiveness. Section 4

examines how suptech tools are being used during the pandemic and describes areas where they proved

to be particularly useful. Section 5 discusses practical considerations in using suptech tools, including

lessons learned during the pandemic. Section 6 concludes.

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