Embed Size (px)
Citation preview
Effectively
Leveraging Data
Analytics within
Internal Audit
Ronnie Ip
MD & Head of Audit of Greater China
DBS Bank (Hong Kong) Ltd
DBS is a leading financial services group in Asia, with over
280 branches across 18 markets. Headquartered and listed
in Singapore, DBS has a growing presence in the three key
Asian axes of growth: Greater China, Southeast Asia and
South Asia. The bank’s capital position, as well as “AA-” and
“Aa1” credit ratings, is among the highest in Asia-Pacific.
• Asia’s Best Bank
• Best Private Bank in Innovation
• Asian Bank of the Year
• Safest Bank in Asia
• Best Bank in Asia-Pacific
• Most innovative Private Bank in the World
• Hall of Fame for Internal Audit Excellence
2015 Key Awards:
3 3
THE WORLD IS CHANGING RIGHT BEFORE OUR EYES
YESTERDAY’S SCIENCE FICTION IS TODAY’S REALITY
BIOMETRICS MOBILE
BIG DATA
ARTIFICIAL INTELLIGENCE
SOCIAL
CLOUD
ROBOTICS
AUGMENTED REALITY
BLOCK CHAIN
is shaping the Future of Banking
4
Rapidly Changing Environment
Economic Volatility and Weakening Credits
5
Rapidly Changing Environment
Increased Regulatory Expectations
6
Rapidly Changing Environment
Digital: Innovation, Disruption and Threats
7
Challenges
Sampling
Audit Cycle
Resources
Past & Reactive
8
Evolution of DBS Group Audit
Checklist
Risk Based
Integrated
AGILITY • Proactive
• Preventive
• Productive
• Predictive
9
Business Units ≡ F1 Teams/ Drivers
BU/SUs Entrust Audit to Monitor, Guide & Predict Risk & Controls
Audit Team ≡ F1 Pitwall crew
Planning Fieldwork Issue Discussion
Reporting
A New Imagery……
Support Units
11
The Future of Auditing
Integrating the 3 Lines of Defence
Shift Left
12
Before Manual data extraction
Manual review
Too much work!
With CA
Automated data download
Automated checks
Continuous report generation
Continuous Auditing – An Overview
Continuous Auditing Approach
[1] What are the events
that I want to predict
before they occur? e.g. tires are about to
burst
[2] What data do I tend
to observe whenever
these events occur? e.g. loss of traction,
overheating tires
[3] Predictive Risk
Management To build a model that
trigger alerts when
similar observations are
seen
Predictive Auditing – Basic concept
14
15
Branch Analytics Model branch risk behaviour 100+ features used in predictive model Enables laser-sharp focus on branches that need
attention Winner of Institute of Engineers Singapore Award 2015
Trading PIITSStop Modeling trading behaviour anomalies
from historical cases Use of expert rules to infer risk profiles Machine-enabled comprehensive
surveillance on trading activities; early detection of risks before they escalate
Data Analytics Projects Deployed in Audit
Engineering Award -by Institution of Engineers Singapore (IES) 1st Financial Institution awarded by IES
Confidential
DBS Bank Ltd (DBS)
DBS Group Audit
Data Analytics Dept , I2R
Data Analytics in Branch Risk Profiling
17
OVERVIEW Data-driven approach in profiling
branch risk, based on 100+ input features
Model is 50% more accurate that the manual heuristic approach in identifying branches with control gaps
For branches selected, model informs focus areas for audit coverage
Model already in use for 2015’s selection of branches
Data Analytics in Branch Risk Profiling
18
IMPACT STATEMENT PRODUCTIVE
Improved productivity & efficiency – valuable audit resources targeted at higher risk branches
PROACTIVE
Continuous surveillance on branch risks
PREDICTIVE & PREVENTIVE
Early detection enables risks to be mitigated before escalating
GREATER ASSURANCE
in the adequacy & effectiveness of bank’s internal controls
CUSTOMER CONFIDENCE
in the integrity of the financial industry
Predictive Analytics Technology for Audit
Our Innovation
Heterogeneous Data Integration
Branch Transaction Data
Branch Service Health Check
HRMS Data
Cash Discrepancies
Customer Complaints
ROR Risk Events
Engineering Practice
Data Visualization
Predictive
Analytics
Technology
5. Live Testing
2. Feature Engineering
3. Training of Model
4. Validation of Model using
historical data
1. Data Preparation
Confidential
DBS Bank Ltd (DBS)
Trading PIITSStop (Potential Incidents & Irregularities in Trading, Sales & Services)
DBS Group Audit
Data Analytics Dept , I2R
Toshihide Iguchi Daiwa Bank
1984 - $ 1.1bn
Nick Leeson Barings Bank 1995 - £827m
Kweku Adoboli UBS
2011 – $1.1bn
22
Rogue Traders
Bruno Iksil JP Morgan
1984 - $ 1.1bn Jerome Kievel
DATA INPUT DATA ANALYTICS
TECHNIQUES DATA OUTPUT
Internal Transactions &
Interactions Records
• Murex Transactions
• P&L Data
• Market Risk Data
• Chat Logs (Bloomberg,
Reuters, Yahoo, Jabber,
Email)
External Data
• Financial news feeds -
Google, Yahoo, ORX,
Regulatory Websites,
Financial Times,
Bloomberg,
• Market Data –
Bloomberg, Reuters,
Exchange, Markit
• Rule-based Anomaly
Detection
• Expert Systems -
Markov Chains
• Supervised Learning
with Labelled Data
• Clustering
• Concept Drift
• Text Mining
• Topic Modelling
• Lexical Analysis
• Patterns , trend
detection & escalation
• System generated alerts
& scoring
• Customizable reports &
dashboards
• E-Communication
surveillance
• “What If” Scenarios
Detection of
Trading Irregularity
or
Highlight Behavioral Patterns
To Predict
Trading Irregularity
Trading PIITSStop Framework
Normal Trader
High amount of Wash Trades
Concentration to Single C/party
Increasing Trend of Wash
with Losses
Significant
Loss : Gains
Pair Ratio Significant
Loss : Gains
$ Ratio
Net Loss > Threshold
Correlation of Buy vs. Sell Time Gaps
Hits on Sin Words with Same paired
Trade Party in Same Period
Increasing Trend of Wash
Trades
Concentration of Transactions at
Month End
Correlation of Buy/ Sell
Spread
Wash Trades Concentrated on
Single
Broker
Hits on Sin Words with Same paired Trade Broker in
Same Period
Wash Trades Concentrated on
Single
Broker and Counterparty
0
0.35
High Cancel & Amends vs. Cluster
Trend of PL Spikes from
Cancel & Amends
0.75
0.10
0.35
0.45
0.50 0.65
0.75
0.80
0.90 0.50
0.75
0.95
0.75
0.35
0.75
PL Manipulation [17%]
Wash Trade for Passing PL [17%]
Wash Trade for Brokerage
Churning [17%]
Wash trade for Volume
Churning [17%]
Principle & Risk Scoring Methodology
26
CRANE
ERICA
Data Analytics Projects in the Pipeline
Validation on Demand by Grp Audit
CRA – Continuous Risk Assessment
27
Challenges in DA Implementation
DA Journey
Data
Historical Cases
Resources
Stakeholder
Tech Support
Costing
Mindset
Board & Mgmt
Support
Mindset Resources
Board & Mgmt
Support
28 Thank You
Today’s Conventional Audit “The Future of Auditing
is Auditing the Future”
29
Q & A
