The document discusses artificial intelligence (AI) in the context of financial services, defining AI as the capability of machines to perform tasks that typically require human intelligence, including learning and problem-solving. It outlines the drivers of AI adoption in the financial sector, the building blocks of AI technologies such as machine learning and natural language processing, and highlights various AI applications in finance. Additionally, it addresses the barriers to adoption and the potential impact of AI on global GDP.

1. Artificial Intelligence (AI) for
Financial Services
Özgür Karakaya - 01.11.2017
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2. British Pound Crash - 07.10.2016
2[1]
[2]

3. AI Definition
"The science and engineering to make intelligent
machines" - John McCarthy
Intelligence:
"the ability to learn or understand or to deal with new or trying situations"
specifically:
● the ability to reason
● the ability to solve novel problems
● the ability to act rationally and efficiently
● the ability to act like humans
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[3]

4. AI Definition - Intelligent Machines
For an application to be truly intelligent:
1. Discover: ability to learn from data to support the development of applications
a. Detection of emergent phenomena
b. Enterprises can now discover answers to questions they didn’t even know to ask
2. Predict: what will happen in the future
a. Classification, regression and ranking
b. Requires explanation and trust
3. Justify: outcomes that are recognizable and believable
a. Explain outcomes assertions, as well as to be able to diagnose failures
b. Justification and transparency build trust
4. Act: process of operationalizing an intelligent application
a. Detect and react when data distributions evolve
5. Learn: always learning, live in the workflow and constantly improving
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5. AI Definition - Programming
Programming Without AI Programming With AI
A computer program without AI can answer the
specific questions it is meant to solve.
A computer program with AI can answer the
generic questions it is meant to solve.
Modification in the program leads to change in its
structure.
AI programs can absorb new modifications by
putting highly independent pieces of information
together. Hence you can modify even a minute
piece of information of program without
affecting its structure
Modification is not quick and easy. It may lead to
affecting the program adversely.
Quick and Easy program modification
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6. AI World Market - Companies
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7. AI World Market - Investment in AI
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8. AI World Market - Investment in AI
8[7]

9. AI World Market - Global GDP Impact
9[8]

10. Adopting AI
10[7]

11. 11[9]

12. Drivers of AI adoption in Financial Services
Business Factors
● Clients are already used to it
expecting the same from financial
services
● Technological shift
Exponential progress
● Business shift
Information processing
● Complexity
High-dimensional processes
Technological Factors
● Post "big data" era:
real chance to extract value from idle
data
● Computing power & cloud:
build and run intelligent apps & services
● Open banking:
comprehensive user data
● Open source AI components and
Maturity in technology 12[10]

13. Forces Driving AI Deployment
13[11]

14. AI Stack
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15. AI Foundation - Data and Analytics
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16. AI Building Blocks
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17. AI Building Blocks - Machine Learning & Deep Learning
Popular Applications of machine learning:
Customer Service, Personal Finance Management, Wealth
Management, Fraud/Risk management
Popular Applications of deep learning:
Fraud Detection, Relevant offers to customers based on their likes and
preferences, Better Decision Making
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18. AI Building Blocks - Machine Learning & Deep Learning
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19. AI Building Blocks - Natural Language Processing (NLP)
Popular Applications of natural language processing:
Consumer Sentiment Analysis, Virtual Assistants, Intelligent Bots,
Automatic Summarization and Answering
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20. AI Building Blocks - Speech Recognition, Visual
Recognition
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Popular Applications of Speech Recognition:
Authentication, Virtual Assistants, Mobile Payment, Wearable Devices
Popular Applications of Visual Recognition:
Authentication, Converting Physical Documents
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21. AI infused business use cases
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22. Contract Intelligence (COIN) - JPMorgan
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23. Haraka - MyBucks
23[21] [22]

24. collect.ai
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25. Narrative Science
25[24]

26. Dataminr
26[25] [26]

27. AI Chatbot Erica - Bank of America
27[27]

28. NAO - Bank of Tokyo
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29. Eyeprint-ID - YapıKredi
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30. Voice-ID - HSBC
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31. Regtech
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32. AI Finance Summit
32[33]

33. Key Barriers of Adoption
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34. Automation Potential by Occupation
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35. References
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[1] https://www.technologyreview.com/s/602586/algorithms-probably-caused-a-flash-crash-of-the-british-pound/
[2] https://www.exchangerates.org.uk/news/16476/the-british-pound-flash-crash-2016-gbp-theories-analysis.html
[3] https://www.merriam-webster.com/dictionary/intelligence
[4] https://s3.amazonaws.com/cdn.ayasdi.com/wp-content/uploads/2017/06/20174822/UnderstandingAyasdi_WP_061617v011.pdf
[5] https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_overview.htm
[6] https://www.venturescanner.com/artificial-intelligence
[7] https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-artificial-intelligence-can-deliver-real-value-to-companies
[8] https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
[9] https://www.cbinsights.com/research/ai-fintech-startup-market-map/
[10] https://www.edgeverve.com/artificial-intelligence-powered-banking/
[11] https://www.infosys.com/aimaturity/
[12] https://www.slideshare.net/sfamilian/working-with-big-data-jan-2016-part-1/7-CONTEXT_WHATS_BIG_DATABIG_IN
[13] https://www.newscientist.com/article/2132086-deepminds-ai-beats-worlds-best-go-player-in-latest-face-off/
[14] https://www.neotalogic.com/2016/02/28/artificial-intelligence-in-law-the-state-of-play-2016-part-1/
[15] https://www.xenonstack.com/blog/log-analytics-with-deep-learning-and-machine-learning
[16] http://searchbusinessanalytics.techtarget.com/news/450419131/Deep-learning-vs-machine-learning-The-difference-starts-with-data
[17] https://www.slideshare.net/Dataversity/smart-data-webinar-advances-in-natural-language-processing
[18] http://recognize-speech.com/
[19] https://www.ibm.com/blogs/bluemix/wp-content/uploads/2017/05/Watson-VR-Concept.jpg
[20] https://www.bloomberg.com/news/articles/2017-02-28/jpmorgan-marshals-an-army-of-developers-to-automate-high-finance
[21] https://za.getbucks.com/
[22] https://www.mybucks.com/documents/corporate/Haraka%20Release%20EN.PDF
[23] http://finovate.com/category/collectai/
[24] https://www.forbes.com/sites/tomgroenfeldt/2016/04/18/narrative-science-dynamically-automates-summaries-of-financial-information/#7bd85d7f1f5a
[25] https://www.ft.com/content/ba5dc7ca-b3ef-11e7-aa26-bb002965bce8
[26] https://www.dataminr.com/finance
[27] https://www.cnbc.com/2016/10/24/bank-of-america-launches-ai-chatbot-erica--heres-what-it-does.html
[28] https://www.theverge.com/2015/4/21/8460841/nao-robot-ufj-bank-japan
[29] https://play.google.com/store/apps/details?id=com.ykb.android
[30] https://www.eyeverify.com/news-events/first-european-bank-launches-eyeprintid
[31] http://www.bbc.com/news/technology-39965545
[32] https://www.ft.com/content/fd80ac50-7383-11e6-bf48-b372cdb1043a
[33] https://theaisummit.com/finance/