BigData in Banking
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1) Large banks are challenged by the vast amounts of data they hold as their most valuable asset, but few know how to effectively analyze and leverage this data. 2) Setting up a "Big Data Factory" can help optimize data processing and analysis across the bank, reducing costs by up to 70% by standardizing data preparation. 3) The factory would provide unified access and analysis of both traditional and non-traditional internal and external data sources to various departments to help with tasks like customer acquisition, risk management, and operations optimization.
BigData in Banking
- 1. BigData in Banking Challenges and Solutions Arshavsky Andzhey Director, Big Data dept., SberBank Avarshavsky.sbt@sberbank.ru andzhey@mac.com 2015
- 2. 3 Innovations like killers – destruction stages of standard banking system ① Internet & social networks Control and choice ② Screens and Smartphones Anyplace any time ③ Mobile wallet Out of cash and plastic cards ④ Accounts without Banks No bank accounts ⑤ BigData Cros-system personalization and targeting *Бретт Кинг, Банк 3.0
- 3. 4 BIGDATA as the development of approaches to the use of data Information like competition differentiator Information like innovation enablement Information as strategic asset Information for business analysys Data for business “Day by day operations” “Datawarehousing” Thevalueofinformationforbusiness “Information in business context” “Business innovations based on information” “Adaptive business strategy” Information usage methods maturity + INTERNET AND OPEN DATA BIGDATA in Banking
- 4. 5 BIGDATA In Banking Information challenges in large Banks (XL) Data is the most valuable asset in all XL banks A few know how to apply data for solving even this day challenges A few know how to leverage internet, external or open data sources to understand clients better and attract new customers
- 5. 6 The Key challenge with data analysis Through the development of the Big Data Infrastructure which solves the challenges with data pre-processing and attribution thru building intelligent data processing Framework, the company will be able to optimize labor costs by reducing works on data preparation of data for the development of business applications up to 70%! BIGDATA in Banking It is estimated (by Gartner), 70% of the time spent on analytical projects are dedicated to bringing, cleaning and data integration, mainly due to the following problems: The difficulty of locating data due to the carelessness among disparate business applications and business systems To be more than appropriate for analysis, data require reengineering and reformatting The acquisition of data for analysis in a specified format creates a huge burden on the teams that own the systems data source . Often the same data is requested or purchase by a variety of departments and business units, which creates additional work and chaos The need for process setup regular data exchange
- 6. 7 Data and Analytics tools as shared resource Client Product Transactions Location …. Instruments RISKS Dept. RETAIL Dept. OPERATIONS Dept. SEQURITY Dept. CORPORATE CLIENTS Dept. HR BIGDATA in Banking BIGDATA to a lesser extent, about the data size and is more about the opportunity to work with many different data types, formats and applications with powerful analytic capabilities.
- 7. 8 Sources of business growth and execution excelence BIGDATA in Banking Client ПРИВЛЕЧЕНИЕ УДЕРЖАНИЕ ПРОДАЖИ ПЕРВИЧНЫЕ ВТОРИЧНЫЕ КРЕДИТЫ РИСКИ ЗАДОЛЖЕННОСТИ АНТИФРОД ВНУТРЕННИЙ ВНЕШНИЙ HR ОПТИМИЗАЦИЯ ПРОЦЕССОВ ① ② ③ ④
- 8. 9 Data Factory conception Big Data Factory should enable data processing in a uniform manner for all platforms, functions and customers. To build easily changeable and easy to use data processing operating model with the required level of trust for both traditional and not so traditional data sources Tasks: Information trust Traditional and not so traditional data sources BIGDATA in Banking • Delivery information • Information integration (Cleaning, Transformation, Mapping, Improvement) • Information search • Access to information • Study hypotheses • Learning models and information analysis • Backup/ Cleanup/ Restore • Administration • Lifecycle management • Data quality • Reference data • Record linkage and the resolution of contradictions • Classification • Reporting • Internet data • Data virtualization
- 9. 10 ЦК Супермассивов данныхBIGDATA PLATFORM HIGH-LEVEL CONCEPTION
- 10. 11 BIGDATA in Banking Data Factory Scenarious The experts of the subject areas of the Bank's business need to access the organization's data for research, sampling, annotation and modelling Data Scientists works on new models Marketing is looking for data for the new compains Security services looking for data for drill a suspicious transaction Retail unit wants to make the best proposal to the client …….. Daily activity The need for ad hoc access to diverse data Support analysis and decision making To use the terminology subject matter experts when accessing data Providing the same easy access to data in spreadsheets, with the ability to scale to huge volumes and distribution on a huge variety of types of information while protecting sensitive information and optimizing it storage systems.
- 11. BIGDATA in Banking Data 2 profit process Task formalization DATA PREPARATION DATA EXPLORATION ADDITIONAL INDICATORS ALATITICS & MODELING MODEL VALIDATION MODEL PRODUCTIZATION EFFECIENCY MONITORING 12 ①
- 12. 13 HDFS, row data Data exchange Data preparation, processing and analytical layer Analytical Views Ad-hoc analytics Development factory Streaming Big Data applications. Integration. marts API BIGDATA in Banking Possible architecture
- 13. 14 BI & BIGDATA Traditional BI Big Data Based on DWH Precession is crucial Flat data scheme Long time 2 market hi-end hardware Based on Hadoop and Spark Any precesion Complex and variable data schemes Ad-hoc analytics Short time 2 market New data sources Low cost Both approaches are valid BIGDATA in Banking
- 14. 15 BIGDATA in Banking Is not expensive - OPEN SOURCE does work Low cost No vendor lock Community support APPLICATION LAYER Spark Hadoop SQL NoSQLDB
- 15. 16 BIGDATA in Banking Thanks and good luck!