The document discusses the application of AI in finance, focusing on regulatory compliance, risk management, and auditing. It outlines various approaches to risk management, the role of natural language processing (NLP) in data-driven compliance, and the benefits of AI in operational risk management. Additionally, it highlights the integration of AI tools and human intelligence to enhance data analysis and monitoring within financial sectors.

1. AI in Finance:
Regulatory Compliance, Risk Management, and Auditing
Natalino Busa - Head of Data Science
https://en.wikipedia.org/wiki/File:DTI-sagittal-fibers.jpg

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Head of Applied Data Science at
Teradata
On most networks:
@natbusa

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What’s in the next hour?
Machine learning and cognitive computing for :
• Regulatory Compliance
• Process and Financial Audit
• Operational Risk Management
• Data Management
Discussion
• Q&A
• Ideas, use cases
Recommendations
• Data computing systems
• Tools, and Skills

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A brief history of Regulatory Compliance

5. 5http://www.bibler.org/images/overlay/reni-moses_with_the_tables_of_the_law.jpg

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It does not scale well ...

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Moving towards digital
Design QueriesVerify?
Comply?
Run Queries
Check Reporting

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Meet the band at the bank
Business developer
Data Scientist
Database Admin
Devops

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Approach #1: avoid risk

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Approach #2: Accept Risk

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Approach #3:
Data-Driven Regulatory Compliance

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Data-driven
Compliance
❏ Distributed systems: time
❏ Complete, Correct, Coherent Information
❏ Human and Machine Errors
❏ Incorrect Interpretation of Regulations
❏ Attacks, Frauds, Exploits

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Auditing + Compliance + ORM
Counting Things
Known Knowns
Talking about the:
Expressive Power of SQL

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AI and Machine
Learning to the rescue
NLP - Process Mining
Anomaly Detection - Knowledge Graph

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What is NLP? Transforming free text into
structured data and back
Computer Science + Linguistic
Based on Algorithms,
Machine Learning and Statistics

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What problems does NLP solve?
Question Answering
Machine Translation
Text Summarization
Topic Extraction
Semantic Search
Spelling correction
Sentiment Analysis

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LDA:
extract topics from documents

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Semantic Search
The ... sleeps on the mat
w(t-1) w(t+1) w(t+2) w(t+3) w(t+4)
embedding
w(t)
dog, cat

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Semantic Search Word2Vec, Par2Vec, Doc2Vec
https://arxiv.org/pdf/1405.4053v2.pdf
https://arxiv.org/pdf/1301.3781v3.pdf

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NLP to SQL
Transform Natural Language Question to Database SQL queries

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Google: open source NLP parser
scoring 95% in grammar accuracy
https://github.com/tensorflow/models/tree/master/syntaxnet

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Deep Learning in Language Parsing

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Operational Risk Management
- Benefits -
1. Reduction of operational loss.
2. Lower compliance/auditing costs.
3. Early detection of unlawful activities.
4. Reduced exposure to future risks.
Establish
Context
Risk assessment
Identification
Analysis - Evaluation
Risk
Treatment
Monitor & Review
ORM

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Unknown unknowns in ORM
Looking for something
we don’t know we should be looking for
• Unexpected Services Failing
• Suspicious Access schemes
• Fraudulent Transactions
Looking for Patterns,
Looking for Anomalies
IT Risk
Model Risk
Legal Risk
CyberSecurity
Fraud Risk
Conduct Risk
Money Laundry

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Network Traffic Patterns Classification

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Network Intrusion detection
http://billsdata.net/?p=105
It contains 130 million flow records involving
12,027 distinct computers over 36 days (not
the full 58 days claimed for the entire data
release).
Each record consists of: time (to nearest
second), duration, source and destination
computer ids, source and destination ports,
protocol, number of packets and number of
bytes
Techniques: TDA, Dimensionality Reduction
https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction

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AI + Human Intelligence
- It’s a closed loop -

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AI Tools
http://www.shivonzilis.com/machineintelligence
https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-
0

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AI based solutions: Enterprises
http://www.shivonzilis.com/machineintelligence
https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-
0

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AI based solutions: Industries
http://www.shivonzilis.com/machineintelligence
https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-
0

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AI based solutions
AI Tools: Exist but may be too narrow in scope
Data + Libraries + Scientists: Still the way to go for customization

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Thank you.
natbusa

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bonus slides

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Hype it right.
Understand the limitations of current systems
Google’s Self-Driving Car Program Odometer Reaches 2 Million Miles
http://www.wsj.com/articles/googles-self-driving-car-program-odometer-reaches-2-million-miles-1475683321
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
http://www.nature.com/articles/srep26286
Why is AI so difficult?
http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
http://www.forbes.com/sites/gilpress/2016/10/31/12-observations-about-artificial-intelligence-from-the-oreilly-ai-conference/
http://www.tor.com/2011/06/21/norvig-vs-chomsky-and-the-fight-for-the-future-of-ai/
https://www.safaribooksonline.com/library/view/oreilly-ai-conference/9781491973912/video260721.html
Down the rabbit hole.
https://youtu.be/_1Cyyt-4-n8
https://youtu.be/u6aEYuemt0M
https://youtu.be/bEUX_56Lojc