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Pilots Overview BDVA Event


FinTech and InsuranceTech case studies digitally transforming Europe's future with BigData and AI
The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. The insurance and finance services industry is rapidly transformed by data-intensive operations and applications. FinTech and InsuranceTech combine very large datasets from legacy banking systems with other data sources such as financial markets data, regulatory datasets, real-time retail transactions, and more, improving financial services and activities for customers.

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Pilots Overview BDVA Event

  1. 1. 1This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Jose Gato Luis Associated Head of AI & Robotics ATOS Research and Innovation INFINITECH - Pilots Overview BDVA Event - 14th May
  2. 2. 6This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Personalized Usage Based Insurance Products
  3. 3. 7This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Categ . Personalized Usage-Based Insurance Pilots Pilto #11 Personalized insurance products based on IoT connected vehicles Description Partners Improve the risk insurance profiles using the information collected by connected vehicles and applying IoT, HPC, Cloud Computing and Artificial Intelligence technologies ● ATOS ○ Connected Car Platform ○ IA Platform ● CTAG ○ On board units ○ Real-Time/Historical data ○ 80 vehicles during 4 by day ● Gradient ○ Anonymization Service ● Dynamis ○ Requirements ○ insurance company’s data
  4. 4. 9This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Pay as you drive Connected Car Platform AI Platform ● BigData Tech ● Anonymization ● Gathering, filtering ● AI/ML ● DNN, Temporal Regression ● Training/Deploy ment Insurance company Are you a good driver??
  5. 5. 10This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Fraud Detection Connected Car Platform ● BigData Tech ● Anonymization ● Gathering, filtering Insurance companyAI Platform ● AI/ML ● Clustering Model ● Training/Deploy ment Policy Agreements Different drivers
  6. 6. 11This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Main Data Sources CAN/BUS 80 Vehicles 4h/day Pre-Historical data ~600 GB City of Vigo Traffic Events ~1 TB on demand Different cities simulated data Insurance company data
  7. 7. 12This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Insurance company New adapted services Bad drivers and fraud costs No data from IoT/Connected Real Life Challenges Data providers Quality/available data Combine data sources Standards protocols Data intelligence How to extract intelligence? What does “good driver” means? Complexity combination of technologies Privacy
  8. 8. 13This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Sandbox and test bed Testbed (NOVA Data Center) Servers Storage Network Pilot 9 Pilot 14 Use Case 1 Use Case N Use Case 1 Use Case N Use Case=Sandbox (K8s-Namespace) App=POD K8s Cluster Cloud Shared Infrastructure (not On- premises) Orchestration of different components
  9. 9. 16This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Concrete example about: exchange of health data in a secure way in the INFINITECH
  10. 10. 17This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Categ . Personalized Usage-Based Insurance Pilots Pilto #12 Real World Data for Novel Health-Insurance productsDescription Partners Customized products in a win-win case for insurer- customer based on the health activity and the risks of each person. ● ML/DL algorithms, Partners’ IoT platforms & Data Governance building blocks for consent management and data anonymization ● 100s of individuals/users that will be engaged in the pilot by RRD; ● 100s’ of Citizens’ feedback datasets; ● 1000s’ Nutritional information datasets; ● Simulated of activity datasets from 1000s of patients based on the simulation module of the Healthentia platform, ● SILO ● ISPRINT, ○ On board units ● RRD ● Gradient ● Dynamis
  11. 11. 18This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Personalized Retail and Investment Banking Services
  12. 12. 19This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Categ . Personalized Retail and Investment Banking Services Pilot #5a Smart and Personalized Pocket Assistant for Personal Financial Management Description Partners Smart Services for bank customers • Smart alerts: prevent possible overdrafts • Smart automations: identify recurrent payments • Smart expense advisor: categories compared with other “similar” customers • Smart recommendations of bank’s products • Smart sentinel: protection based on alerting on potential anomalies ● Liberbank ○ Final User ○ Data Provider ● GFT Spain ○ Integrator of LIBERBANK ● UNIVERSITY OF PIRAEUS ○ Machine Learning developer ● CrowdPolicy ○ Machine Learning developer
  13. 13. 20This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Colour references Technical Architecture Overview Customer Profile PFM Enriched Account Tx Bank Product Offering Smart Alerts Smart Automat. Proposals Smart Recomm endation Smart sentinel alert ETL Account Transact. Updates Account transaction forecaster Account transaction forecast Overdraft forecaster Account transaction loader Account transaction Automation proposer Unexpected transaction detector Bank’s product recommender Transaction sentinel Smart Expense Advice Smart Expense Advisor Data Processing Data Storage Machine Learning Legacy software Onlinebankingmiddleware Android App iOS App Web App
  14. 14. 21This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Software Infrastructure Overview Customer Profile PFM Enriched Account Tx Bank Product Offering Smart Alerts Smart Automat. Proposals Smart Recomm endation Smart sentinel alert ETL Account Transact. Updates Account transaction forecaster Account transaction forecast Overdraft forecaster Account transaction loader Account transaction Automation proposer Unexpected transaction detector Bank’s product recommender Transaction sentinel Smart Expense Advice Smart Expense Advisor Onlinebankingmiddleware Android App iOS App Web App
  15. 15. 24This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Testbed Servers Storage Network Use Case 1 Pilot 5a Use Case 2 Use Case N Sandbox and test bed Cloud or OnPremises (tbd). Maybe mix solution Orchestration of different components Use Case=Sandbox (K8s-Namespace) App=POD K8s Cluster
  16. 16. 25This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Financial Crime and Fraud Detection
  17. 17. 26This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Categ . Financial Crime and Fraud Detection Pilot #9 Analyzing Blockchain Transaction Graphs for Fraudulent Activities Description Partners Fraud Detection Blockchain crypto currencies and tokenized assets that are obtained fraudulently. Transactions and tokens: ● Ethereum, Bitcoin (public not regulated) ● Also regulated chains like GUSD A final transaction ends up into a bank product. Holding stable coins that originated from fraudulent. Construction of the massive blockchain transaction graph ● Aktifbank (AKTIF) ○ Responsible for user interfaces and regulations and banking services. ● Bogazici Univ. (BOUN) ○ Responsible for HPC software development for big blockchain data and parallel graph analysis.
  18. 18. 27This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance • Transaction graph sizes are big and growing. • Currently transactions-per-second is low on public blockchains: Bitcoin (7 tps) and Ethereum (15 tps). Ethereum performance is expected increase in future releases. • Hyperledger reported to achieve 3500 transactions- per-second in cloud environment: https://www.ibm.com/blogs/research/2018/02/architecture-hyperledger-fabric/ • A parallel / distributed graph system is needed whose performance can scale by simply increasing processing nodes on an HPC cluster. Transaction Graph Sizes Bitcoin transaction count: 527 Million Source: https://www.blockchain.com/charts/n- transactions-total Ethereum transaction count: 700 Million Source: https://etherscan.io/chart/tx As of May 2020
  19. 19. 28This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance • HPC Cluster with 16-32 nodes with a total of around 1TB memory is expected to handle the current transaction sizes. • As the graph size increase, these requirements will increase and cluster node count and memory size can be scaled. • HPC cluster supporting MPI (message passing interface) is needed. • External Metis or Scotch software can be used to partition graphs in order to minimize communication volume between processors. http://glaros.dtc.umn.edu/gkhome/metis/metis/overview https://www.labri.fr/perso/pelegrin/scotch/ HPC Requirements
  20. 20. 30This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Technologies Blockchain Transaction Dataset Preparation Component Descriptio n Extracts Bitcoin, Ethereum and major ERC20 token transactions (such as Gemini USD (GUSD), Tether USD (USDT), Tether Gold (XAUT), Statis Euro (EURS) and Turkish BiLira (TRYB) ) from blockchain. Owner BOUN BDVA Layer Data Management Scalable Transaction Graph Analysis Component Description Constructs distributed/partitioned transaction graph in parallel using MPI. It will utilize graph and machine learning algorithms to analyse fraudulent transactions. Owner BOUN BDVA Layer Data Processing Architectures/Data Analytics User Interface for Blockchain Transaction Reports and Visualization Component Description Will provide user interaction with the Scalable Transaction Graph Analysis component within the bank and collect/manage user as well as annotated blacklisted blockchain addresses . It will utilize OpenAPIs (REST APIs) to submit queries and and provide visualization based on received results using vis.js graph drawing package Owner AKTIF BDVA Layer Data Visualization and User Interaction
  21. 21. 31This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Architecture
  22. 22. 32This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance BDVA Reference Model BDVA RA
  23. 23. 36This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632 Flagship initiative for Big Data in Finance and Insurance Q&A Jose Gato Luis Associated Head of AI & Robotics ATOS Research and Innovation

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