<FINTEGH MANAGEMENT www.fintech-ho202TJ^u
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This project has received funding from the European Union's Horizon 2020 research and innovation 111 |\|
programme under grant agreement No 825215 (Topic: ICT-35-2018 Type of action: CSA) |\/| U  III X
FINancial supervision and TECHnology compliance training program
Overview of the Horizon2020 project Oleg Deev
Financial Technologies
- The Financial Stability Board (2017) defines Financial Technology (FinTech) as "technologically enabled financial innovation that could result in new business models, applications, processes, or products with an associated material effect on financial markets and institutions and on the provision of financial services"
- Peer to peer lending, robot advisory asset management and crypto payments are examples of Financial Technologies, enabled by big data analytics, artificial intelligence and blockchain technologies.
- "This creates new dynamics, such as the growing importance of a larger user base for network effects" (VP Dombrovskis, 26 February 2019)
- While innovation in finance is not a new concept, the focus on technological innovations and its pace have increased significantly.
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Financial Technologies
- Financial Technologies are changing the nature of the financial industry, creating many opportunities to offer more inclusive access to financial services (European Commission, 2018):
- competitive prices,
- better user experience,
- wider inclusion.
- Financial Technologies may bring higher risks
- underestimations of credit risks,
- market risk noncompliance,
- cyber risks (cyber attacks and frauds),
- All amplified by the interconnectedness of fintech platforms, which generates systemic risks.
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Financial Technologies (BCBS)
Sectoral innovations
I     Amerns, clearirj and settlement services     I Investment management capital-raising services H I services
Market support services
Crowdfunding
Lending marketplaces
Mobile banks
Credit scoring
Retail
Mobile wallets
to-peer transfers
Digital currencies
Wholesale
Value transfer networks
FX wholesale
Digital exchange platforms
High-frequency trading
Copy trading
Portal and data aggregators
Ecosystems (infrastructure, open source, APIs)
Data applications (big data analysis, machine learning, predictive modelling)
Distributed ledger technology (blockchain, smart contracts)
Security (customer identification and authentication}
Cloud computing
Internet of things / mobile technology
Artifical intelligence (bots, automation in finance, algorithms)
E-trading
Robo-advice
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Future of
Banking
(BCBS)
Scenario
Better bank
New bank
Distributed bank
Relegated bank
Disinfprmediated bank
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Service provider (product and risk management)
Customer interface
Incumbents revamp legacy with a modern digital dienl interface
New banks build for digital and an enhanced digital customer experience
Incumbents
Digital interface
igital intprf Digital interface
Incumbents
Fintech Bigtech
>
Aggregators of financial services
built by fintech/bigtech
Fintech providing full service eg DLT, P2P... Bigtech providing full service eg DLI, VIP...
Customers
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Motivation
Across the board, we are working to strike the right balance between risks and opportunities, so that Europe can benefit fully from new technologies in the financial services sector.
Valdis Dombrovskis Vice President of the European Commission
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Motivation
- There is a strong need to improve the competitiveness of the European fintech sector, introducing a framework for a common risk management approach across all countries, that can supervise fintech companies without stifling their economic potential.
- A framework that can help both fintech and supervisors:
- Fintech firms that want to grow and scale-up across Europe need advanced regulatory technology (RegTech) solutions;
- the supervisory bodies' ability to monitor innovative financial products proposed by fintechs is limited, and advanced supervisory technology (SupTech) solutions are required.
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Horizon 2020 FIN-TECH project
- The FINancial supervision and TECHnological compliance training program (under the Ell's Horizon2020 funding scheme) aims at building a fintech risk management platform which measures risks to make fintech innovations sustainable.
- A platform that aims to automatize compliance of fintech companies (RegTech) and to increase the efficiency of supervisory activities (SupTech).
- The aims will be achieved through a knowledge exchange hub, which will eventually lead to a European sandbox laboratory, that evaluates risks and opportunities of fintech innovations.
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Project Framework
- Research framework
- builds risk management models and selects use-cases for big data analytics, artificial intelligence and blockchain applications in finance (according to the guidelines of international regulators): 6 research workshops
- SupTech framework
- shares use-cases to all 27 EU national regulators via on-site training: 27*3 workshops
- RegTech framework
- shares use-cases to fintechs and banks in 6 European hubs: 6 coding and data modality workshops
- Validation framework
- improves use-cases based on all received feedback: from regulators, supervisors, fintech, banks and external advisors
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Project Network
- 8 international regulators (FSB, BIS, IMF, OECD, EBA, ESMA, EIOPA, ECB) - guide and prioritize the research
- 24 research partners - research and develop fintech risk management models
- 28 national supervisors (all EU countries) - participate give feedback in SupTech workshops
- 6 European fintech hubs - participate and give feedback in RegTech workshops
- 5 European bank risk managers and 5 non-European advisory board members - evaluate the developed fintech risk management models
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Project Network
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SupTech workshops
Topics	Applications	Associated risks
Big Data         P2P finance		Credit risk Fraud detection Systemic risk
Artificial Intelligence	Robo-advisory Asset management Cognitive computing	Market risk Compliance Risk profile matching (MiFID) Cyber and operational risks
Blockchain	Crypto-assets and exchanges	Market risk Cyber and operational risks ICO's fraud detection Money laundering
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SupTech workshops at CNB
Course	Dates
Big Data Analytics in Finance	March 14-15, 2019 May 6-7, 2019
Artificial Intelligence Applications in Finance	September 26-27, 2019 December 9-10, 2019
Blockchain and Financial Crypto-assets	February-May 2020 (TBA)
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Big Data Analytics in Finance
Big data analytics in finance = Financial data science
-A process that consists of:
1. The acquisition and management of big data (Computer Science)
2. The discovery of regularities and/or relations: (Statistical Science)
3. The acquisition of new domain specific knowledge (Economics and Finance)
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Big Data Analytics in Finance
- Two main ways to learn data science models:
- Statistical Learning: models explainable and reproducible but less efficient
- (Machine Learning models: models efficient but less transparent and reproducible)
- Classification of learning models: -descriptive (unsupervised) models
cluster analysis (community detection) graphical models (network models)
- predictive (supervised) models
regression models (deep neural networks) tree models (random forests)
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Peer to Peer Lending
- Among FinTech applications that rely on big data analytics, innovative ones are those based on peer-to-peer (P2P) financial transactions, such as peer to peer lending, crowdfunding and invoice trading.
- The concept peer-to-peer captures the interaction between individual units, which eliminates the need for a central intermediary.
People
Centralized system
People
P2P system
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17 Overview of the FIN-TECH project
Peer to Peer Lending: Opportunities
- improved rates of return compared to those
available on bank deposits, together with
relatively low fees for borrowers -improved financial inclusion - P2P platforms are
able to provide financing to borrowers unable to
access bank lending -improved quality, speed of service and user
experience to both borrowers and lenders
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Peer to Peer Lending: Risks
- While both classic banks and P2P platforms rely on credit scoring models for the purpose of estimating the credit risk of their loans, the incentive for model accuracy may differ significantly:
- In a bank, the assessment of credit risk of the loans is conducted by the financial institution itself which, being the actual entity that assumes the risk, is interested to have the most accurate possible model.
- In a P2P lending platform, credit risk of the loans is determined by the platform but the risk is fully borne by the lender.
- Another factor that penalizes the accuracy of P2P credit scoring models is that they are (still) based on limited data.
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Peer to Peer Lending: Proposal
- P2P platforms operate as social networks, which involve their users and, in particular, the borrowers, in a continuous networking activity.
- From the P2P platform perspective, network data models should be employed, to improve credit risk measurement accuracy.
- From a supervisory viewpoint, the modelling of network data should be allowed, so that P2P lenders produce more reliable credit risk estimates.
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Course "Big Data Analytics in Finance" at MUNI
- Background Training in Statistics
- Simple and multiple linear regression
- Machine learning prediction methods
- Logistic regression
- Network analysis
- Hands-on coding examples
-3 use-cases in P2P lending risk management - Presentations of Czech Fin-Techs?
21 Overview of the FIN-TECH project
Questions at registration:
Field of study
Finance/economics   ■ Mathematics   ■ Law   ■ Other
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Questions at registration
Mean, median, standard deviation
Strongly agree    ■ Agree    ■ Neither agree nor disagree
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Questions at registration
Histogram
Strongly agree ■ Agree ■ Neither agree nor disagree ■ Disagree ■ Strongly disagree
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Questions at registration:
Linear regression
Strongly agree ■ Agree ■ Neither agree nor disagree ■ Disagree ■ Strongly disagree
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Questions at registration:
Statistical software
Strongly agree ■ Agree ■ Neither agree nor disagree ■ Disagree ■ Strongly disagree
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Questions at registration:
Programming
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28 Overview of the FIN-TECH project
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29 Overview of the FIN-TECH project
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Workshop evaluation
https://www.fintech-ho2020.eu/ free/app/evaluation-suptech-praque
Please fill in the evaluation form:
- Name -Affiliation
- Department
- Position -Role
- E-mail
- Comments at the end of the form
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