machine learning in banking: what mid ... - iqpc corporate · machine learning in banking: what...
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MACHINE LEARNING IN BANKING: WHAT MID- AND BACK-OFFICE WORK CAN IT
AUTOMATE?
What problem are we talking about?
? QUALITY
DATA RAW DATA
DATA SOURCING, COLLECTION, EXTRACTION, VALIDATION, RECONCILIATION, REMEDIATION
What human approaches have failed to solve the problem?
onshore / offshore headcount
What machine approaches have failed to solve the problem?
point tools
What machine approaches have failed to solve the problem?
rules-based automation (RBA)
What new approach solves the problem?
Machine Learning: Programs that program themselves Automating automation Watch-and-learn computing
How is machine learning different?
Rules-Based Automation Machine Learning
How does it work?
training data
machine learning
model
(registries, databases, documents, feeds, etc.)
raw data
quality data
Why is machine learning the future of work?
“Enterprises that ignore the emergence of smart machines do so at their own peril. Smart machines offer competitive and transformational business benefits that are available primarily to early adopters.”
- Tom Austin The Rise of Smart Machines January 29, 2015
What processes can machine learning optimize and automate? Compliance & Risk
Entities & Instruments FATCA Bios Verify Company Locations AIFMD Business Card Data AML | KYC Business Classification KYC Remediation / Sanction List Company Description Basel III Executives Information BCBS 239 Filings Extraction EMIR Hierarchies FATCA People Authority Products and Services LEI Mapping Web Activity MIFID Bank Qualified Bonds Corporate Actions ETF Attributes Actions Change Detection GIINS Registry Filings Bond Announcements GIINS SEC Filings Deep Links Announcement Extraction Legal Entity Monitoring M&A Transaction Status Loan Data Extraction M&A Transactions Announcements Dividend Announcements Monitoring News Extraction Shares Outstanding Monitoring Realtime Dividend Announcements
Audience ideas: What other labor-intensive mid- / back-office processes could benefit from machine learning?
What are the pain points in data operations?
Case: SSI Processing
Case: SSI Processing What is the problem?
Human Problems: • Training and retaining off-
shore headcount • “Fat finger” keystrokes • “Black box” = no audit trail • Inefficient priority queue
management
Machine Problems: • Fragmented systems • OCR error-prone • Inability to automate due to
varying formats
Costly, risky, slow.
Case: SSI Processing
Machine learning impact:
Quality: 100% accuracy through automatic validations and escalations Cost: 80% reduction in BPO contract in one year Speed: Extraction time reduced from 15 minutes to 30 seconds
Reduced risk & cost + faster
Case: Corporate Actions | Dividend Extraction
Case: Dividend Announcements What is the problem?
Human Problems: • Workforce cannot
elastically scale • Language limitations for
exotics coverage • Extraction work is a poor
use of SME time
Machine Problems: • OCR error-prone • Exceptions handling • Varying sources and formats
break scrapers
“Bursty” work, limited coverage, costly, slow
Case: Dividend Announcements
Machine learning impact:
Quality: 99.7% automation of single announcements; new coverage of exotic markets (e.g., Qatar) Cost: SME burden reduced from 15 people to 2 people Speed: Extraction time reduced from 6 minutes to 5 seconds
Improved coverage, reduced cost, radical increase in speed
Audience Ideas: A discussion of the processes you think could benefit from machine learning.
