1. Revamp approach to collect data from banks.

2. Improve ingestion, analytics and visualisation of data collected from banks.

3. Participate in MAS-wide data management and transformation initiatives.

4. Support the maintenance of the compliance toolkits used by banks to facilitate their understanding and compliance with MAS’ requirements.

1. User requirements documentation. 

2. Implementation of data platforms for data collection, ingestion, analytics and visualisation

3. Keep compliance toolkits relevant and updated.

1. Real-world experience of implementing data collection, data ingestion and data visualisation for actual business use cases.

2. Deepen understanding of data management in a corporate setting.

3. Pick up project management experience and skills relevant for data related projects.

4. Understanding of MAS banking supervisory approaches.

5. Stakeholder engagement with different departments and workgroups with MAS.

1. Keen interest in banking and financial services industry.

2. Experience in creating Tableau Dashboards.

3. Well-versed in scripting languages commonly used in data analytics, preferably Python.

4. High self-drive and ability to work independently.

5. Good analytical and communications skills.

(i) Produce write-up of research findings

(ii) Build a monitoring framework to track bank profitability against global benchmarks

(iii) Presentation of key findings to team and department management

Gain experience using data platforms to extract data and develop a monitoring framework to inform policy decisions   

Grow understanding of bank business models and financial metrics   

Build up research skills by reviewing literature and breaking down a complex phenomenon into analysable key metrics   

Hone presentations skills in using data to tell a story/showcase key findings

- Use data analytics to analyse the investment returns of MAS’ external funds, and perform performance reporting and attribution.

- Build dashboards to visualize the analytical results, to compare fund characteristics.

- Build straight-through processes to extract data from external and internal databases, to support analytical work.

As part of MAS Internal Audit Department's transformation, we are looking for a candidate to join the Data Analytics team within IAD in developing data analytics solutions for the department.

The candidate will be attached to the IAD’s DA Team to implement DA projects (details will be provided later).

The scope of work includes:

(1) Preparing (Extract, Transform and Load) and exploring data;

(2) Building rules and checks;

(3) Automating DA projects, including creating dashboard;

(4) Coordinating with internal stakeholders on the requirements for (1) to (3); and 

(5) Documenting work done and conducting briefing to IA staff. He/she will also get to learn about the Internal Audit function, and how DA and automation help to identify risks and generate insights.

• End-to-end process to build and automate a DA project 

• Business process re-engineering

• Business knowledge of the functional area

The Monetary and Domestic Markets Management Department implements Singapore’s exchange rate centred monetary policy, manages banking system liquidity, and issues and manages Singapore Government Securities on behalf of the Government.   

As part of MDD’s digital and data transformation, we are seeking to enhance the use of data for market intelligence and policymaking. Working across different teams in MDD, you will develop or enhance dashboards to support MDD’s monitoring of capital flows and key funding markets. You will also be responsible for analysing in-house and public data to support policy reviews.

Ability to deploy data visualisation and analytics skills; first-hand insight into the conduct of monetary policy and management of liquidity conditions in Singapore; involvement in key policy reviews

It will be a research project to trace the evolution of the statistical units in key central banks in terms of building up of capabilities and focus areas. A case study on ECB’s statistics unit should be included given its recency in establishment, on how it meets the demands from the formation of the EU. The key objective of the project is to draw lessons from the various experiences and learn about the latest statistical thinking and approaches in major central banks.

The intern will get to apply statistical skills and knowledge to a research project and have the experience of contributing to research work in a central bank.

Intern will be tasked to develop functionalities that support a data cataloguing solution where data owners can input business metadata, which are then exposed to a wider pool of users for search, discover and access, via appropriate workflows.

Data management and web development skills. In the former, the intern will learn what is needed in order to make datasets "searchable", which is a key part of good enterprise data management. For the latter, the intern will have hands on experience implementing solution(s) using common web development tools.

The project aims to study the volatility of securities listed on SGX and evaluate the effectiveness of volatility control mechanisms applied on these securities for the period 2019 - 2022.

The intern will have the opportunity to apply statistical modelling techniques on real-world datasets and gain an appreciation on the policy consideration in the regulation of securities markets.

The aim of this project is to build a tool using machine learning to identify FIs with higher likelihood of breaching financial requirements. The work includes the following:
1. Data preparation using financial data from regulatory returns and other sources; 
2. Training and validating the performance of different machine learning models;
3. Presenting the outcome for discussion and evaluation; and
4. Operationalising the tool.

1. Applying machine learning techniques to financial data; and
2. Understanding the supervisory concerns of MAS in regulating financial institutions

The aim of this project is to build an automated tool which uses Natural Language Processing (“NLP”), machine learning and rule-based techniques to identify applications (e.g. license, key appointment holders etc) with higher likelihood of gaps in terms of suitability, business plans etc, for further review or attention. Where appropriate, the tool can also help to support straight-through processing for certain applications in the longer term based on its risk profile. The work includes the following:

1. Re-designing existing form template(s) based on the agreed business rules/triggers, where appropriate;
2. Data preparation by extracting the relevant data from the submitted application forms via systems;
3. Training and validating the performance of different approaches (e.g. rule-based, NLP, machine learning models, hybrid models etc);
4. Presenting the outcome for discussion and evaluation; and
5. Operationalising the tool.

1. Applying NLP/machine learning techniques to real-life authorisation data; and
2. Understanding the supervisory concerns of MAS in regulating financial institutions

The aim of this project is to come up with a usable model to predict potential distress at MAS' supervised financial institutions. The work includes the following:
1. Literature review of other jurisdictions' methodologies in predicting financial distress;
2. Coming up with the appropriate indicators to determine financial distress and variables contributing to it (i.e. the independent and dependent variables);
3. Building machine learning models to predict financial distress; and
4. Presenting the outcome for discussion and evaluation.

1. Applying machine learning techniques to financial data; and
2. Understanding the supervisory concerns of MAS in regulating financial institutions