Data analytics in FI
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This document discusses data and analytics in the financial industry. It notes that 90% of the world's data was created in the last two years and provides examples of the massive amount of data created every minute on social media platforms. It outlines the process of acquiring data, developing analytics through cleaning, testing and modeling the data, and improving processes by integrating analytics into business strategy. Key areas of analytics in finance are described as descriptive, diagnostic, predictive and prescriptive. The customer lifecycle is also summarized as acquiring, activating, growing and retaining customers through risk scoring and behavioral modeling.
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The future is always uncertain. To be truly future-ready, companies need the ability to quickly learn and adapt and to foster a culture of continuous curiosity and experimentation. But how can we facilitate rapid learning throughout the organisation? What will the future of learning look like for you? How can we ensure our organisations become engines of growth through learning?
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There are many phases in the software development cycle, from requirements to development and testing, but at the tail of the process, is an often overlooked aspect: deployment and delivery. With the paradigm shift of delivering on-site software to offering software-as-a-service, Site Reliability Engineering is beginning to take a greater role in product delivery.
This session aims to give a glimpse of the work that goes into site reliability engineering (SRE) and effort that goes into keeping a service going 24/7.
Product Management in The Trenches for a Cloud Service
More often than not, people’s perception of Product Management is usually centred around the definition, management and prioritisation of software features and functionality. While that is largely true, it is also one of many things that a Product Manager needs to focus on, given limited time and resources.
This session aims to provide an unfiltered view of how Product Management looks like in the context of Enterprise Cloud Applications development, the challenges confronting Product Managers, and the tradeoff decisions to be made in order to overcome these challenges.
All this, while shipping a working product with each release that will surprise and delight the end user.
Overview of Data and Analytics Essentials and Foundations
As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision.
This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.
Predictive Analytics
With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes.
Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.
Feature Engineering for IoT
In this digital transformation era, we have seen the rise of digital platforms and increased usages of devices particularly in the area of wearables and the Internet of Things (IoT). Given the fast pace change to the IoT landscape and devices, data has become one of the important source of truth for analytics and continuous streaming of data from sensors have also emerged as one of the fuel that revolutionise the emergence of IoT. These includes health telematics, vehicle telematics, predictive maintenance of equipment, manufacturing quality management, consumer behaviour, and more. With this, we will give you an introduction on how to leverage the power of data science and machine learning to understand and explore feature engineering of IoT and sensor data.
Master of Technology in Software Engineering
This document provides information about the Master of Technology in Software Engineering program at NUS. The program focuses on designing scalable, smart, and secure software systems and products. It offers both part-time and full-time study structures, with the part-time program taking 2 years and full-time taking 1 year. Students can choose a structured route taking set courses each semester, or a flexible route completing graduate certificates at their own pace over 5-7 years. General admission requirements include a bachelor's degree in engineering or science with a minimum GPA, 2 years of work experience, and passing an entrance test and interview. Important application dates for the 2023 start are also provided.
Master of Technology in Enterprise Business Analytics
This document provides information about the Master of Technology in Enterprise Business Analytics program at NUS-ISS. It discusses what data science is, who should take the program, sample job profiles of graduates, the courses taught in the program, and the stackable certificate structure. The program can be completed through a structured route of taking certificates back-to-back over 2 years part-time or 1 year full-time, or a flexible route of taking courses anytime over 7 years to earn the Master of Technology degree. Admission requires a bachelor's degree, minimum GPA, English proficiency, 2 years of work experience, and passing an entrance test and interview.
Diagnosing Complex Problems Using System Archetypes
In today’s VUCA world, we are faced with problems coming in fast and furious. In order to resolve such problems quickly, we need to first understand the problems. One of the techniques to understand complex problem is through the use of system archetypes. System archetypes are patterns of behaviour of a system. Let’s us explore some of the system archetypes in this session as well as tips on how to resolve them.
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Data analytics in FI
- 1. Data and Analytics in FI Abhisek Ghosh Senior Director, VISA Inc.
- 2. In the last decade… 2010 2015 2020 Enterprise VoIP Social Media Sensors/Devices 90% of the world’s data was created in last 2 years
- 3. In 60 seconds… 4.1M “likes” in FACEBOOK 350K feeds in TWITTER 300h uploads in YOUTUBE 51K app downloads from APPLE 1M videos played in VINE 1.7M “likes” in INSTAGRAM 77Kh of video in NETFLIX 285K snaps in SNAPCHAT
- 4. From data to intelligence… ACQUIRE DATA 1 DEVELOP ANALYTICS 3 IMPROVE 5 CLEAN DATA 2 TEST 4 Data integration and data management Data analytics Integration with strategy and business
- 5. The Analytics Pyramid… Descriptive WHAT HAPPENED? • Activities • Results Diagnostic WHY IT HAPPENED? • Exploratory analysis • Correlations Predictive WHAT’S NEXT? • Models/Scores • Forecasts Prescriptive HOW TO MAKE IT HAPPEN? • Recommendations • Prioritizations Easy/Rudimentary Complex/Effective Hindsight Foresight Insight
- 6. Customer life cycle… Acquire • Profile • Scorecard • Risk Score Activate • Early month on book • Segmentation Grow • Marketing Campaigns • Response Models • Upgrade • Behaviour Score Retain • Attrition Models • Reactivation Models RISK Application Score Behaviour Score AML PD/LGD Collection Score
- 7. Going back, future outlook… 2010 2015 2020 Enterprise VoIP Social Media Sensors/Devices 9 : 1 0.5 : 9.5 1 : 9 0.5 : 9.5 Structured : Unstructured 5% : 95%
- 8. Looking back, thinking forward… ACQUIRE DATA 1 DEVELOP ANALYTICS 3 IMPROVE 5 CLEAN DATA 2 TEST 4 Data integration and data management Data analytics Integration with strategy and business ACT ADJUST ADOPT ADAPTALIGN
- 9. THANK YOU