The project is being developed in the following phases.

Data-driven insights start with large quantities of structured granular data. A key component of an Ellipse-type platform that would enable supervisors to be data-driven is a system for the digitally enabled collection and processing of granular and standardised data. This means that those data are:

• Consistently understood by all stakeholders

• Can be repurposed for different use cases

• Represented in such a way that allow programmable code to reference those data to generate the reporting of regulatory metrics.

A number of regulatory authorities have been exploring different ways in which to replace template-based, aggregated regulatory reporting with granular data and digital regulatory reporting [1]. The vision of regulatory reporting using granular data removes the need for multiple templates, allowing supervisors the opportunity to constantly re-purpose common data points for different analytical use cases.

However, granular reporting requires a common understanding by authorities and financial institutions of what those data are, so that financial institutions can map their operational data to a common “input” before the required reporting can be generated. Important initiatives around data standards, taxonomies and data models are being developed for this purpose [2]. However, data standardisation more generally remains nascent globally.

A move to machine-executable digital regulatory reporting is closely linked with the need for data standards. There are significant benefits in the medium- to long-term for financial institutions and authorities to move towards a digital reporting framework, for the reasons highlighted in this background section.

Nevertheless, the initial investment and costs needed to achieve this outcome can steer decisions based on business incentives and risk appetite. While data standards initiatives would ultimately benefit from – and require – financial institutions and authorities to adopt a greenfield approach, the perceived scale of such an exercise could also deter stakeholders from committing to its implementation.

We propose that in the absence of global or national data standards, alternative options could be explored as a first step to bringing stakeholders closer to a common understanding of data needs. These options can include:

1. Bringing together authorities that have a mandate to collect data, to see if there are similarities or commonalities in the type of data being collected.

2. Engaging with industry on their use of data models and taxonomies for that specific product to explore wider extensibility.

3. Establishing small working-level projects using a narrowly scoped and specific use case and agreeing common data attributes and their definitions “in principle”.

4. Exploring ways in which the requirements for the reporting of those data can be published in programming languages.

Project Ellipse is a proof of concept (POC) launched by the Bank for International Settlements Innovation Hub (BISIH) to explore how supervision could become insights-based and data-driven using an integrated regulatory data and analytics platform.

Regulatory authorities, as the ultimate end users of the platform, would be able to digitally extract, query and analyse a large quantity of data from diverse sources. These data could then be relevant to current events in real time and visible via dashboards, informing them of early supervisory actions that may need to be taken.

Project Ellipse is being developed in two phases. This documentation contains information on the background for this project, and details on the POC for a cross-border data model explored in Phase 1. Further information on Phase 2 will be provided by the end of 2021 and towards the end of Q1 2022.

To meet these challenges in the digital age, authorities could benefit from “on demand” access to timely and integrated sources of data to help support and inform their supervisory assessments. Several possible solutions are therefore explored in this project.

Supervisors today rely heavily on regulatory reporting to inform them of potential risks that may be forming in regulated entities, which can have broader implications for the financial system. However, there are challenges with the information that supervisors receive from regulatory reports, such as the ones listed below.

The challenge is how supervisors can form an accurate picture of exposures and have predictive insights into emerging risks using these data sets. Project Ellipse therefore explores how supervisors can better identify and assess emerging risks in real time to inform them of early supervisory actions that may need to be taken.

Project Ellipse explores how stakeholders can gain an understanding of the requirements needed to shift to digital reporting, without needing to commit and invest significant resources upfront. For Project Ellipse, we adopted the following approach to explore the concept of cross-border digital reporting:

1. Taking two authorities from different jurisdictions, to see if a common understanding of data reporting needs could be achieved cross-border and between two different reporting regimes. In this POC, the authorities were the BoE and the MAS.

2. Working on a narrowly defined and small use case for a regulated product that was common to both authorities. We used a subset of reporting requirements for retail mortgage loans.

3. Using an existing industry data model to test its extensibility to a different product domain to see if the components and attributes could be repurposed. We tested the Common Domain Model (CDM) developed by the ISDA, which is an open-source machine executable data model.

Despite the varied and sometimes bespoke nature of some mortgage products, we found that mortgage transaction data and the respective reporting requirements aligned around three main components:

1. The Loan

2. The Borrower

3. The Collateral

Based on these components, as well as taking into consideration the type of data that is needed for both authorities for supervisory purposes, a minimum common set of requirements was developed for the collection of data for retail mortgages. Legislative or regulatory requirements that were jurisdiction-specific were excluded for this exercise.

To find a common understanding of data collected for regulatory purposes across reporting regimes, the Project Ellipse team used the reporting requirements for retail mortgages in the UK and Singapore.

Retail mortgages were selected for this exercise because while the collection of this data is common across authorities globally, the templates and formats used to collect these data are highly heterogenous. To illustrate, Ellipse based its modelling exercise on the following two regulatory reporting requirements in the UK and Singapore:

• UK: Product sales data reporting (Mortgages) PSD001 (PSD001)

• Singapore: MAS Notice 645 Computation of Total Debt Servicing Ratio for Property Loans (MAS 645)

The former is an example of reporting requirements being presented in a template, while the latter is an example of requirements being integrated within prudential instruments.

The aim of the initial exercise was to take these respective reporting requirements, with a view to determining whether common baseline requirements could be established. This required a common understanding of the rationale for the data collection, the type of information that is needed for supervisory purposes and what information was “optional”.

Taking the common baseline set of requirements, we were able to drill down into the three main components further to reflect what we considered the key attributes of retail mortgages that were relevant for this POC.

These data attributes were also intended to be used to calculate or aggregate supervisory metrics. For example, "loan size" and "value of property" would be the key inputs to derive the loan-to-value measure. Other types of aggregation would also be enabled, such as loan-to-income, total loans issued by postcode or total loans issued by purpose (eg owner-occupied or buy-to-let investment).

Agreement on common data attributes also necessitates an agreement on their definitions. During the process of identifying attributes for retail mortgages, definitions were sourced from either the respective jurisdictional frameworks or third-party sources where existing definitions did not meet the needs of the respective reporting requirements.

These definitions are solely to inform the data attributes of the POC and are not reflective of the actual prescribed definitions found in either reporting regime. However, it is intended to illustrate the exercise of agreeing to a definition “in principle” for the modelled attributes, as well as to highlight the importance in a cross-border context of definitions that are applicable in other jurisdictions for similar products. Converging on a common understanding of products and their definitions is an important step towards normalising their use in a standardised manner.

Digital reporting requires data attributes to be represented in a way that allows programmable functional logic code to reference and generate the reporting of regulatory metrics. This is otherwise referred to as reporting being “machine readable and executable”. Project Ellipse sought to explore whether existing industry data standardisation initiatives could be re-purposed for different use cases, such as retail mortgages. Of particular interest were those initiatives that were open-sourced and geared towards being machine readable and executable.

The CDM developed by ISDA, is an open-source, standardised, machine-readable and machine-executable blueprint for how financial products are traded and managed across the transaction lifecycle. [3] The product scope of the CDM includes over-the-counter (OTC) derivatives, cash securities, securities financing, and commodities. To ensure re-usability across different markets, the CDM is designed as a composable model whereby financial objects can be constructed bottom-up based on building-block components.

For these reasons, we modelled the POC mortgage attributes using the CDM. Testing the CDM’s use and extensibility for other product domains such as mortgage loans was key to the POC, as globally applicable common data models that can be used across products could minimise the number of data models in use by financial institutions and reduce mapping burden. In addition, as the CDM is open-sourced, this allows the model to be more widely accessible for testing within existing environments.

To achieve standardisation across products and asset classes, the CDM identifies logical components that fulfil the same function and normalises them, even when those components may be named and treated differently in the context of their respective markets. We followed the CDM design principle of normalising concepts such as quantity, price and party in the representation of financial transactions.

The CDM identifies that, regardless of the asset class or product type, a financial transaction always involves two counterparties trading (ie buying or selling) a certain financial product in a specific quantity and at a specific price. This approach means that a single logical concept such as quantity can represent concepts that may be named and captured differently across markets: eg notional or principal amount etc.

Following this approach, we took the modular components that exist in the CDM and reused concepts such as product, loan, price, collateral and party to reflect the mortgage attributes that characterised loan, borrower and collateral. We also extended the model to reflect attributes that were specific to the POC around collateral (eg property) or the credit profile of the borrower (eg credit details).

A demonstration of the modelling and programming of the regulatory reports using the Ellipse mortgage CDM is shown below.

Using the CDM, executable code automatically generated from the model definitions enabled us to simulate the re-creation of the retail mortgage reports for Singapore and the UK referencing the same model.

We took dummy mortgage transactions with the following parameters:

• New / first retail mortgage loans

• Owner-occupied

• One main borrower

• A 25-year loan, fixed rate for first 5 years

• Reversion to the firm's standard variable mortgage rate after 5 years

Taking these data feeds and generating the functional logic code based on each jurisdictional requirement that referenced the Ellipse mortgage model, we were able to execute the regulatory “reports” that we derived the attributes from, namely PSD001 and MAS 645.

Phase 1 of our project illustrates the potential for process efficiencies that may be gained when adopting machine executable reporting using common data models. It could also increase the volume of granular data available to supervisors, which is needed to enable the use of advanced analytics. Further exploration between regulatory authorities would be needed to validate these findings and to see whether the exercise could be extended to other reporting use cases.

Building on this first use case, Phase 2 will explore the integration of granular data sets with unstructured data, using AI and ML to extract insights from these data sources to highlight correlations between current events and supervisory metrics. Insights extracted from the mined data would be displayed as early warnings for supervisory attention via dashboards.

Project Ellipse explores the feasibility of cross-border digital reporting. This POC is intended as a first step towards bringing authorities and stakeholders closer to a common understanding of data that is collected by authorities globally.

Our exploration confirms that:

• Regulatory reporting requirements can be expressed in unambiguous machine-readable logical reporting instructions underpinned by a consistent data model;

• Technical standardised programmatic specifications of the steps for generating regulatory reports can be published alongside regulation and ensure clear understanding at the most granular level of the expected data;

• Executable libraries can be automatically generated and published alongside regulations to assist accelerated implementation;

• If a common data standard was agreed to and implemented, financial institutions may no longer need to interpret reporting instructions and submit aggregated data by use case; and

• With additional logical instruction based on the same data model, supervisors may also be able to automatically query the underlying transaction data and generate regulatory metrics referencing that standardised data.

The following section further describes the data attributes of the CDM Mortgage Schema that seeks to follow the data structure of the Financial Products Markup Language (FpML). Selected examples of the CDM data attribute definitions are used as illustrations to help explain various dimensions of the model, and include data samples and code snippets to help demonstrate the CDM structure, where applicable.

Contract Details

Contract details: defines specific attributes that relate to contractual details of a trade; it describes the key documentation and party contractual information of the model.

• The Documentation Identification set of data attributes represents the legal document(s) meta data that governs a trade and associated contractual product terms, either as a reference to such documents when specified as part of the CDM, or through identification of some of the key terms of those documents, such as the type of document, the document identifier, the publisher, the document vintage and the agreement date.

"documentationIdentification": {
      "otherAgreement": [
           {
              "identifier": "1234XXX23",
              "otherAgrementType": "Retail Mortgage 2016",
              "version": "1.0",
              "unadjustedDate": {
                "day": 6,
                "month": 3,
                "year": 2016
              }
            }
          ]
        }    

• Within Other Agreement is a set of attributes that defines agreement executed between parties, it consists of the following additional attributes:

  • Identifier is a metadata attribute that is used to identify the agreement.

  • Other Agreement Type is a metadata attribute that shows the agreement executed between the parties and intended to govern product-specific transactions between those parties.

  • Unadjusted Date describes a date field that is subjected to adjustment based on (day, month and year).

• The Governing Law sub-attribute of Contract Detail represents the law governing the trade and associated contractual product terms.

"governingLaw": "GBEN"

• The Party Contract Details, sub attribute of Contract Details, represents additional contractual information provided by each involved party.

"partyReference": {
    "externalReference": "party1",
    "globalReference": "1ab3e71c"
    },
   "naturalPersonRole": [
       {
           "personReference": {},
           "role": "MainBuyer"
       }
  ]

• Party Reference is a set of attributes that describes both the external and global reference that links to this particular data model.

• Natural Person Role is a set of attributes to specify the role(s) that natural person(s) may have in relation to the contract.

  • Person Reference is an attribute that references the natural person to whom the role refers to.

  • Role is an attribute that specifies a person role that is distinct from the party role.

Party

A set of metadata key that represents, without a qualification, as to whether this party is a legal entity or a natural person.

• The Party ID identifier associated with a party and the scheme standards that is being used (ie 20 digits LEI code).

"partyId": [
  { 
  "meta": { "scheme": "http://www.fpml.org/coding-scheme/external/iso17442" }, 
  "value": "ML5798312-1" 
 }

• The person set of data attributes consists of first name and surname.

"person": {
      "value": {
        "firstName": "James",
        "surname": "Smith"
      }
    }

Credit Details

Credit Details is set of data attributes that is associated with the borrower's credit information. It consists of income, employment, and whether the borrower has an existing mortgage which is denoted as as a boolean flag.

"creditDetails": { 
     "hasAMortgage": "False",
     "employmentStatus": "Employed",
     "income": [
      {
        "incomeAmount": {
                 "value": {
                    "amount": 5000,
                     "unitOfAmount": {
                           "currency": {
                             "value": "GBP"
                             }
                           }
                         }
                       }
                    }

Tradable Product

A tradable product represents a financial product that is ready to be traded, ie included in an execution or contract, by associating a specific price and quantity to this product plus an (optional) mechanism for any potential future quantity adjustment.

"tradableProduct": {
      "counterparty": [
        {
          "partyReference": {
            "externalReference": "party1",
            "globalReference": "1ab3e71c"
          },
          "role": "PARTY_1"
        },
        {
          "partyReference": {
            "externalReference": "party2",
            "globalReference": "e6c82f54"
          },
          "role": "PARTY_2"
        }
    

• Counterpartyattributes is an enumerated value (ie Party1 or Party2 ). The Counterparty attribute can then be positioned in the product (eg to specify which counterparty is the payer, receiver etc) with a counterparty type, that is positioned outside of the product definition, and allows the Counterparty attribute to be associated with an actual party reference.

  "priceQuantity": [
    {
      "meta": {
        "globalKey": "27a2ef6f"
      },
      "price": [
        {
          "meta": {
            "location": [
              {
                "scope": "DOCUMENT",
                "value": "price-1"
              }
            ]
          },
          "value": {
            "amount": 0.0164,
            "unitOfAmount": {
              "currency": {
                "value": "GPB"
              }
            },
            "perUnitOfAmount": {
              "currency": {
                "value": "GPB"
              }
            },
            "priceType": "INTEREST_RATE"
          }
        }
      ],
      "quantity": [
        {
          "meta": {
            "location": [
              {
                "scope": "DOCUMENT",
                "value": "quantity-1"
              }
            ]
          },
          "value": {
            "amount": 250000,
            "unitOfAmount": {
              "currency": {
                "value": "GBP"
              }
            }
          }
        }
      ]

• Price Quantity attribute defines the settlement information as an exchange between two parties of a specified quantity of an asset (the quantity) against a specified quantity of another asset (the price). The settlement can be either cash or physical. In the case of non-cash products, the settlement of the price/quantity would not be specified here and instead would be delegated to the product mechanics as the price/quantity values.

  "product": {
    "loan": {
      "creditAgreeemtDate": {
        "meta": {
          "globalKey": "3f105d"
        },
        "value": {
          "day": 30,
          "month": 4,
          "year": 2021
        }
      },
      "loanLeg": [
        {
          "rateSpecification": {
            "fixedRate": {
              "meta": {
                "globalKey": "0"
              },
              "rateSchedule": {
                "initialValue": {
                  "address": {
                    "scope": "DOCUMENT",
                    "value": "price-1"
                  }
                }
              }
            }
          }

• Product attribute defines the underlying product details that is the ready to be traded (ie included in the contract or execution).

  • Loan Leg identifies a loan by referencing a product identifier with a set of optional attributes.

  • Rate Schedule is a set of attributes defining a schedule of rates or amounts in terms of an initial value and then a series of step date and value pairs.

  • Credit Agreement Date specifies the closing date (the date where the agreement has been signed) for the loans in the credit agreement. Funding of the facilities occurs on (or sometimes a little after) the Credit Agreement date. This attribute is used to help identify which of the company's outstanding loans are being referenced by knowing which credit agreement it belongs to.

  • ISDA Standards Terms Supplement term: Date of Original Credit Agreement.

Trade Date

Trade date attribute specifies the date which the trade was agreed. The trade date attribute is based on (day, month, year).

"tradeDate": {
  "meta": {
    "globalKey": "3f105d"
  },
  "value": {
    "day": 30,
    "month": 4,
    "year": 2021
  }

Trade Identifier

Trade Identifier is a set of data attributes that consist of the 2 sub attributes. It represents identifier(s) that uniquely reference a trade for an identity issuer. A trade can include multiple identifiers (ie a trade that is reported to multiple jurisdictions, and has an associated USI (Unique Swap Identifier) UTI (Unique Trade Identifier).

"assignedIdentifier": [
      {
        "identifier": {
          "meta": {
            "scheme": "http://www.fpml.org/coding-scheme/external/uti"
          },
          "value": "UITD7895394"
        }
      }
    ]

• Assigned Identifier refers to the issuer and the identifier and its associated version that provides the ability to associate multiple identifiers to one issuer and is consistent with the FpML Party Trade Identifier.

  Copy

   "issuer": {      
         "meta": {
         "scheme": "http://www.fpml.org/coding-scheme/external/iso17442"
        },
        "value": "54930084UKLVMY22DS16"
     }

• Issuer is an attribute when specified explicitly alongside the identifier value (instead of being specified by reference to a party).

Collateral

Collateral is a set of metadata attributes that defines the obligations of the counterparty that is subjected to credit support requirements.

"propertyType": "PrivateResidential",
"dwellingType": "ApartmentOrFlat",
"physicalAddress": {
"street": "Sun Street",
"city": "Holbrokk",
"country": {
     "meta": {
          "scheme": "http://www.fpml.org/coding-scheme/external/iso3166"
          },
     "value": "GBR"    

• Within the collateral data type, property information such as the physical address, dwelling and property type would be specified.

    "ValuationDate": {
      "meta": {
        "globalKey": "3f105d"
      },
      "value": {
        "day": 15,
        "month": 4,
        "year": 2021
      }

• Valuation date defines when payment will occur relative to the valuation date.

"PropertyValuation":
  {
  "address":
   { 
   "scope": "DOCUMENT", 
    "value": "price-2"
   } 
  },
  "PurchasePrice": {
    "address": { "scope": "DOCUMENT", 
    "value": "price-3" 
    }
  }

• Property Valuation set of attributes specifies final valuation price. This price can be expressed either as an actual amount/currency. The value attribute can reference to another value specified in the swap document.

Ellipse is led by the BIS Innovation Hub's Singapore Centre. Please contact the team at singapore.centre@bisih.org.

Ellipse Terminology and Description

We use the following terms throughout this document.

Acronyms used in this document

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Marketing cookies or beacons, also often referred to as targeting cookies, are used to collect identifiable personal information about visitors of websites. Such cookies identify browsing behaviours, personal interest and product preferences, tracing individual users in support of advertising or marketing campaigns. The BIS does not use or analyse marketing cookies.

Which cookies do we use?

The BIS use of cookies is limited to the following types of cookies:

Statistics cookies, also referred to as webmetrics or performance cookies, collect data about how visitors use and navigate websites. These cookies count visits and identify web traffic sources by collecting user data in an anonymised and aggregated format.

The BIS uses the following statistics cookies:

Third-party cookies are set by parties other than the website owner. They enable third-party features or functionality to be provided on or through the website. In cases where visitors of a BIS website follow a link which takes them to a social media homepage, an external application or third-party website, they are leaving the BIS website realm and are subject to the cookie policies of the respective third party. The degree to which third parties are able to place cookies depends on the cookie control preference settings of your browser. The following third-party cookies may be set on a BIS Site:

Third-party cookies related to social media

BIS webpages link to social media websites such as Twitter, LinkedIn, Instagram and or YouTube and may also show embedded social media content. They also link to RSS feeds and BIS videos and podcasts. Social media providers may place cookies, or other tracking technologies, including those set by Google Analytics, or may direct the visitor to websites hosted outside a BIS Site.

Third-party cookies related to service and collaboration partners

The BIS engages service and collaboration partners to facilitate and support BIS processes and services such as recruitment and e-learning, or collaboration services used by the BIS Innovation Hub. Cookies placed by our service and collaboration partners on a BIS Site are limited to support technical functionality, including statistics, preference and security cookies.

How do you manage cookies?

Most browsers allow you to manage if and/or how cookies are set and used, and to clear cookies and browser data. Your browser may have settings that let you manage cookies on a site-by-site basis. Please refer to your browser instructions to learn more about your options for managing cookies.