AMBD Case Study

Autoriti Monetari Brunei Darussalam (AMBD) was established as the central bank of Brunei Darussalam in 2011. With the developing and ever-changing financial landscape in the domestic and global financial sectors, AMBD recognised the importance of investing in a future-ready data collection, analysis and supervisory system that could grow and keep up with the ongoing evolution. In 2016, as the first step in this journey, AMBD undertook efforts aimed to revamp and modernise the existing data compilation and reporting processes in order to deliver quality data via insightful reports that will inform supervisory decision making.

Prior to project implementation, there were four main problems associated with AMBD’s processes for receiving and analysing data from reporting institutions.

The compilation process required copying and pasting data across numerous files and formats, including from hardcopies submitted, into a ‘Master’ Excel file which was both tedious and time consuming. Data validation checks were also done manually, along with comparisons to historical data to check for inconsistencies. Another downside of manual processes was that they can be prone to human errors.

Data quality may be unreliable; with the possibility of data being copied incorrectly, or missed altogether due to the manual compilation process. Manual checking of the submitted data opened up the possibility of failing to flag invalid or incorrect data. Non-standardised reporting by entities also meant that for comparability, additional data cleansing steps were required.

Data stored by each unit was focused in a standalone ‘Master’

Excel file with little visibility of the data as a whole and frequent

copies being made for individual users needing to work on

the data. Sharing and collaborating on this data proved to be difficult as one will have to manually extract the specific data needed from this ‘Master’ Excel file in order to share with the other users.

AMBD was restricted to the functionality available in Excel for reporting on data received from the industry. This proved challenging, particularly for creating reports based on granular data.

AMBD chose Vizor for this project because they are a well-recognised company that has vast experience as a solution provider for financial data collection and regulation technology.

After the tender process and due diligence trips to understand other users’ experiences, AMBD selected Vizor Software to implement the Centralised Statistical System (CSS). The CSS was launched in May 2017 not just as a data collection and management system for data analysis, but also as a more efficient RegTech tool for the financial regulators.

With CSS, data submission is streamlined into a central location, significantly reducing manual intervention and processes. Each regulated entity has an on-line account to undertake the submission of their data, achievable through the upload of Excel templates. This is something they are already familiar with, which reduces the barriers of utilising a new application.

The submission process, coupled with the automatic validation and centralised storage, is a dramatic improvement from the old process as it minimises manual interventions.

With the implementation of CSS, the following processes have been improved:

• Data Validation checks are automatically triggered;

• Notifications are distributed automatically throughout the returns lifecycle; and

• Data is collected through a centralized platform and is stored in a central database for robust reporting.

With CSS, AMBD has moved away from copying and pasting data across workbooks for data storage. All submitted data is automatically stored, ready for analysis, reporting and forecasting.

As regulated entities upload data into CSS, the data is automatically checked for any validation issues and run against specific business rules as defined by each Unit. If the Entity fails any rules, they are notified and can be prevented from submitting their data until the rules are passed.

All submitted data is stored in a database, and is eventually pushed into a Data Warehouse. As a result, the data can be used across different units (where applicable) to generate and share reports, and undertake cross-validation checks across multiple returns. This allows for easier sharing and collaborating where each user is given access by owner of the data to the specific data they need for supervisory analysis.

Data is collected and stored through CSS in a way that allows in-depth, robust reporting across AMBD. The solution ties-in with the Business Intelligence and analytical tools, which offer a deep visualisation of the data collected, and the generation of granular reporting.