Uploaded byLee Schlenker
Group 3 - BBVA
BBVA underwent a transformation process focused on digitalization and improving the customer experience. It developed new data analytics capabilities including algorithms and machine learning models to gain insights from customer data and optimize processes like trading. This allows BBVA to better predict trends, evaluate outcomes, and take actionable steps to create opportunities for customers.
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Group 3 - BBVA
- 1. Case Study onBBVA’s road to transformation Case Study Group 3 February 2019 Pratik Tripathy 17126 Kalyan Chakravarthi P. 17013 http://DSign4.education
- 2. Introduction ©2016 L. SCHLENKER Structure Introduction BusinessModel Data Analytics road map The Perception? Prediction Evaluation Actionable outcomes
- 3. https://www.bez.es/179615884/Espana-incumplira-tambien-el-nuevo-objetivo-de-deficit-en-2016-con-un--segun- BBVA-Research.html • Revenue generatingServices: • Banking Solutions • Investment Services • Insurance Services • Wealth Solutions • Mobile Banking • ATM Banking Introduction Technology
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- 5. This Photo byUnknown Author is licensed under CC BY-SA-NC • During 2017, the BBVA Group made significant progress on its transformation process, firmly underpinned by the Group’s Purpose and six Strategic Priorities. • New standard in customer experience • Digital sales • New business models • Optimal capital allocation • Unrivalled efficiency • A first-class workforce • BBVA focuses on digitalization and customer experience under a new tagline: “Creating Opportunities”. Introduction Technology
- 6. https://accionistaseinversores.bbva.com/microsites/cuentasanuales2017/assets/img/estrategia-4q17-2.pngby Unknown Author islicensed under CC BY-SA-NC • BBVA has a customer-oriented business model that offers a differential service with one very ambitious goal: to be leaders in customer satisfaction across its global footprint. • BBVA increased its customers empowerment in 2017 by • expanding the number of products available on a do-ityourself basis, allowing them to interact with BBVA at any time and from any place. Business Model Technology
- 8. Referhttps://accionistaseinversores.bbva.com/microsites/cuentasanuales2017/assets/img/estrategia-4q17-1.pngence • The Algorithmicstrategies & Data Science (ASDS) unit co-ordinates initiatives around the automation of the trading processes and generating intelligence from data. • Exploiting reusability and synergies across asset classes making distinction between equities, fixed income and FX less valid in terms of how they are traded. • The following features are reatined: • Trend • Cyclical components • Seasonal components • Random componets The perception? Technology
- 9. • Atalya • Itaims to exploit data by using advanced analytics tools to provide BBVA’s Bus with intelligence. • Hidalgo • Seeks to create platform where a single implementation of a trading algorithm runs seamlessly across markets and instruments. Prediction! Technology This Photo by Unknown Author is licensed under CC BY-SA
- 10. • Timecop performsthe process using the following engines; • ARIMA(AutoRegressive Integrated Moving Average) • Holt-Winters(Exponential Smoothing) • LSTM (Long Short Term memory) and • VAR (Vector AutoRegression) Evaluation Technology
- 11. • Continuous feedback •Enhanced trading experience • Cross-asset trading strategies Actionable Outcomes Technology
- 12. • BBVA wantsto fill a gap in the world of Auto Machine Learning to simplify the work of data scientists in time series. • It aims to reduce the non-creative work of selecting the best model according to the characteristics of the time series, leaving more time for data scientists to select the features and their integration in environments that provide the appropriate value. • It allows to perform temporary analysis in short periods of time, being able to know the anomalies and predictions of hundreds of series at the same time and opening new worlds for the comparison of evolutions of thousands and millions of series in a simple way that did not exist to date. Conclusion Technology
- 13. • https://www.bbvacompass.com/personal.html • https://www.bbva.com/en/timecop-once-upon-a-time- the-time-series/ •https://accionistaseinversores.bbva.com/microsites/cu entasanuales2017/en/management-report/strategy- and-business-model/index.html Bibliography Next Steps
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Editor's Notes
- #11 ARIMA (AutoRegressive Integrated Moving Average) It is a dynamic model of time series that uses variations and regressions of statistical data to find patterns that allow making a prediction of the future. Holt-Winters (Exponential Smoothing): It’s one of the best ways of forecasting the demand of a product in a given period, Holt-Winters considers the level, trend and seasonality of a certain series of times. It incorporates a set of procedures that make up the core of the family of time series of exponential smoothing. LSTM (Long Short Term memory) The LSTM model is one of the traditional neural networks and they are widely used in prediction problems in time series because their design allows remembering information over long periods and facilitates the task of making future estimates using periods of historical records. VAR (autoregressive vector) It’s a system of as many equations as series to be analyzed or predicted, but in which no distinction is made between endogenous and exogenous variables