Rethinking Supervisory Technology

Dr. Li Xuchun

Head of Supervisory Technology Office

Data Analytics Group

Monetary Authority of Singapore

AGENDA

Value of AI & DA

Big Data to Smart DataApplying Smart Data

Machine Learning & NLP

MAS SupTech

How MAS uses AI & DA to enhance supervision

(SupTech) – Initiatives & Infrastructure

SupTech Use Case

Market Surveillance

WAR IS 90%

INFORMATION

-Napoleon Bonaparte

Why the hype today?

Availability of Data

The 5Vs of Data

Value

Volume

Veracity Variety

Velocity

Big Data

Smart Data

DIFFERENCE OF SMART DATA & BIG DATA

The 5Vs of Data

SMART

DATABIG

DATA

ACTION

INSIGHT

DIFFERENCE OF DATA

BUSINESS

OBJECTIVES

DATA

FROM BIG DATA TO SMART DATA

Which should come first, business objectives or data?

What should I do differently as a senior manager?

Anthony ScriffignanoChief Data Scientist, Dun & Bradstreet

You lead with the problem.

You never lead with the data,

and you never lead with the technology.

Business Understanding

Data Understanding

Data Preparation

Modelling

Evaluation

Deployment

DATA

CONFIDENTIAL

FROM BIG DATA TO SMART DATAStart with the BUSINESS OBJECTIVES

CONFIDENTIAL

FROM BIG DATA TO SMART DATA

Enforcing data QUALITY

FROM BIG DATA TO SMART DATA

Using suitable METHODS

Supervised

Unsupervised?

+1

-1

Algorithm familyData Labels

Semi-supervised

Number of

samples

Threshold

%10 %20 %30

TP/TN

TP/TN

TP/TN

FROM BIG DATA TO SMART DATA

Checking for ACCURACY

FROM BIG DATA TO SMART DATA

Training (80%) Testing (20%)

Model

Build (“Train”)

Model

Test (“Validate”)

Model

Operationalise

Model

Historical Data Live Data

Achieving GENERALISABILITY

Easily understood. Who is the audience? What

is their level of technical expertise? Do they

understand the model?

•

Yield simple and direct

insights. Do the results make

sense?

Serve the business objective.

Does the model answer the

problem statement?

Look into the

black box!

FROM BIG DATA TO SMART DATA

•

•

Ensuring INTERPRETABILITY

SMART USE OF DATA: 7 Habits of the New Paradigm

Know the question you are trying to answer

Understand the data inside out

Find the right algorithm for the job.

Complexity may not be better

Be aware of limitations in the data,

algorithms, conclusions Data-driven insights

Competency

EffortProcess

1

Be skeptical. Question the data, assumptions, results

Automate to free up time to contextualise the results

Experiment. Don’t be afraid of trying and failing, and trying

again. Go for a “fail-fast” approach

2

3

4

5

6

7

• Lack of project management

• Over interpretation/extrapolation of results

• Absence of culture for converting insights into

actionable outcomes

• Perceiving data as technology projects

PITFALLS TO AVOID

How is your organization:

collecting, storing and using your data?

using data to drive insights and decision-making?

making analysis repeatable and shareable?

WHERE ARE YOU ON YOUR DIGITAL JOURNEY?

Computer programs that automatically improve their performance through experience (data)

Subfield of artificial intelligence

Building blocks

MACHINE LEARNING

Named entity recognition

Machine translation

Syntactic parsing & tagging

Automatic question answering

Sentiment analysis

NATURAL LANGUAGE PROCESSING

Automatically adapts and customizes to individual users

Discovers new knowledge from large datasets

Mimics humans and replaces certain monotonous tasks

Develops systems that are too difficult or expensive to construct manually

MACHINE LEARNING & NLP

Source Prepare Analyze Use

• What data do I

need?

• Where do I find

it?

• In what format

can I get it?

• Where do we

store it?

• Who can

access it?

• Who cleans and

validates it?

• How do we know

if it is valuable?

• How do we

extract insights

from it?

• What tools can

we use for the

analysis?

• How do we turn

insights into action?

• How do we

visualize data and

present insights?

• How do we best

distribute insights?

DATA VALUE CHAIN

MAS SupTechMission

Enhance financial supervision through the use of Artificial Intelligence and Data Analytics

Shape the SupTech landscape through collaboration and mutual exchange

Collaborate with RegTech ecosystem to overcome current challenges from compliance and supervision.

Big Data

Automation

Visualization

AI & ML

Usage of granular data

Data cleaning

Data consolidation

Data quality check

Interactive dashboards

Network graphs

Supervision of FI

Market surveillance

AML detection

Usage of financial data and alternative data

OUR INITIATIVES

MAS Supervisors

DataCollectionGateway

3rd Party Datasets

Enterprise Systems

Centralised Data Storage

Collection Storage Analytics Usage

Experimentation& development

EnterpriseApplications

Data Processing:

Data Cleaning

Data Storage

DATA ANALYTICS INFRASTRUCTURE

Automation Trade Analysis Prediction

Objectives

APOLLO

Expert Reports

Feature Engineering

Create prediction

engine

Export the predicted outcome

Human expert review

Improve model using new

training data

Build model with created features

Add trading data

Process

APOLLO

Feature Evaluation

Thank You!