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Similaire à BBS-248 Artificial Intelligence (AI) for Financial Services
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BBS-248 Artificial Intelligence (AI) for Financial Services
- 1. Artificial Intelligence (AI) for Financial Services Özgür Karakaya - 01.11.2017 1
- 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 3 [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 4 [4]
- 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 5[5]
- 6. AI World Market - Companies 6[6]
- 7. AI World Market - Investment in AI 7 [7]
- 8. AI World Market - Investment in AI 8[7]
- 9. AI World Market - Global GDP Impact 9[8]
- 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 14 [10]
- 15. AI Foundation - Data and Analytics 15 [12] [13]
- 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 17 [15] [16]
- 18. AI Building Blocks - Machine Learning & Deep Learning 18 [10]
- 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 19 [17]
- 20. AI Building Blocks - Speech Recognition, Visual Recognition 20 Popular Applications of Speech Recognition: Authentication, Virtual Assistants, Mobile Payment, Wearable Devices Popular Applications of Visual Recognition: Authentication, Converting Physical Documents [18] [19]
- 21. AI infused business use cases 21[10]
- 22. Contract Intelligence (COIN) - JPMorgan 22[20]
- 23. Haraka - MyBucks 23[21] [22]
- 27. AI Chatbot Erica - Bank of America 27[27]
- 28. NAO - Bank of Tokyo 28 [28]
- 31. Regtech 31 [32]
- 33. Key Barriers of Adoption 33 [10]
- 34. Automation Potential by Occupation 34 [7]
- 35. References 35 [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/
Notes de l'éditeur
- CAIO - Chief AI Officer CPG - Consumer packaged goods (CPG)
- number of companies?
- Business Factors: Clients already used to it: apple siri, amazon alexa, smart services,recommendation engines, expecting the same in financial services Technological Shift: Challenger banks, AI can be the biggest technological shift ever - computer rev, internet rev, smart phone revolution Business Shift: Finance business is shifting from managing financial transactions of clients to processing their information and provide them intelligence. Same happened for car industry: it was a business of engines , motors to self-driving cars making it business of information processing Financial Institutions are complex organization, high dimension processes where AI solutions are well suited - brain power, computing power and big data Technological Factos: Post big data era: AI is taking up where big data left o to give enterprises a real chance to extract value from their idle data resources Open banking has led to more comprehensive customer data, banks can build better models,and more intelligent apps and services Better access to computing power & cloud helps build and run intelligent applications Maturity from descriptive to prescriptive and predictive analytics to contribute to evolution of AI
- The main difference is that expert systems are rule based systems while modern machine learning (ML) are based on statistical modeling of data. That is, an expert system uses if-then statements when doing inference while an ML system projects the input into some model space
- Machine Learning: Classical Regression, Decision Trees, Dimension Reduction, Bagging&Boosting, Support Vector Machines, Neural Networks, Bayesian Networks, Text Mining, Recommendation Engine Data mining vs machine learning: data mining presents the findings to human beings for their attention, machine learning adjusts its program and actions on its own. Machine Learning: Machine Learning is simply, the ability of computers and other smart machines to learn without being “taught” or programmed. Deep Learning: Deep Learning is a subset of machine learning, accomplished through a hierarchy of artificial neural networks, which resemble human brain architecture, complete with a web of neuron nodes. Traditional programs take a linear approach to building analyses, deep learning systems mimic the human brain and its non-linear style of working.
- Machine Learning: Classical Regression, Decision Trees, Dimension Reduction, Bagging&Boosting, Support Vector Machines, Neural Networks, Bayesian Networks, Text Mining, Recommendation Engine Data mining vs machine learning: data mining presents the findings to human beings for their attention, machine learning adjusts its program and actions on its own.
- Natural Language Processing (NLP) is a technique computers use to analyze, understand, and make sense of text and human language.
- Settlements Processing: Takas işlemleri Reconciliation: Reconciliation is an accounting process that uses two sets of records to ensure figures are correct and in agreement. It confirms whether the money leaving an account matches the amount that's been spent, ensuring the two are balanced at the end of the recording period.
- $9.6 B tech budget and 20000 developers
- No credit scores : Farmers, small business owners Default ratio %7 No market knowledge
- 25M Euro Receivables
- 32 languages
- 32 languages
- PwC : PricewaterhouseCoopers - Audit - Management Consultancy DRYIce: NLP SapientRazorfish provides business model growth, new product and service innovation, customer experience, enterprise digital transformation, IT modernization, omni-channel commerce platforms,
- PwC : PricewaterhouseCoopers - Audit - Management Consultancy DRYIce: NLP SapientRazorfish provides business model growth, new product and service innovation, customer experience, enterprise digital transformation, IT modernization, omni-channel commerce platforms,