Data analysis and data science techniques help Banking, Financial Services and Insurance (BFSI) to create insights, make data-driven and smarter decisions, and manage the challenges they face.
In this regrad, I will talk about the triggers of using data analysis and data science techniques in BFSI and how these techniques including Machine learning can help BFSI.
I will also go through some BFSI case studies with the help of data analysis and data science techniques.

Mahsa has been a Data Mining and Machine Learning expert since 2013. She is a Ph.D. qualified data scientist from the University of Melbourne focusing on time series data mining. Currently she works as a senior data scientist at NAB. Before that, She worked at Findex Co. as a data scientist. She has hands-on experience in time series segmentation, anomaly detection, pattern recognition, classification and clustering algorithms, adaptive and incremental learning, time series forecasting, anomalous event detection, neural network, fuzzy systems, and SVMs. Mahsa's passion is applying her knowledge to build practical and business solutions for data analysis problems.

I am planning to put on a slideshow to cover:-

Introduction
Ethics and Data science - Two sides of a coin
World leaders and problem solving
Sustenance
demonstrate our capability at EY
Conclusion

Experienced Data Consultant with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Data Engineering, Open Source ML Implementations, and Cloud services.

Text analytics is one of the top analytics trends and business users are understanding the value of their raw text. By mining the text data, they can spare operational costs, help with anticipating the future and reveal experiences which were beforehand not accessible. In this presentation, I will talk about five significant use-cases of text analytics that enhance customer experience

A Ph.D. qualified data scientist with years of commercial experience and a strong mathematical background who has won several national and international mathematics awards. I take advantage of my mathematical ability in mining and analyzing data to bring value to businesses. As a passionate data scientist, I have participated in Kaggle and data challenge competitions several times and have won several prizes. With years of professional experiences, I have been working on different types of data including time-series, spatio-temporal data (trajectories & GPS), health data, graphs (networks), customer data, bank data, text logs, and sensor data.

I will explain how the modern digital company is aproaching digitalization, will give examples and tips how to achive fast growth and retain customers. I will then demo a brain storming session with real time planning interactive board on how to lunch digital product and calculate budget and model process of customers uptake.

Nikola is the SVP, Chief Data Scientist at PT Smartfren Telecom. He leads data analytics and data science projects from strategy development, execution to delivery with regard to the collection, manipulation and AI based analysis of data for various business functions and departments including customer experience, customer on-boarding, marketing, sales, network operations etc.

Customers for long have faced a complex, impersonal and inconsistent experience when interacting with an organisation. Poor CX is estimated to cost business a staggering $122 Billion a year. It’s time we start to re-imagine the data life cycle, how its collected, stored and use for a customer’s benefit and gain competitive advantage with a complete history or pervious transactions and interactions.

Satya is an experienced people leader and practioner strategically optimising and managing marketing process, marketing automation capability and governance. Currently he is the Vice President at Citibank Australia and has over the past 15 years and held Marketing Capability & Optimisation roles with Accenture Australia, The Star Entertainment Group, Tabcorp, NRMA, Bankwest, and HSBC.
He has championed taking businesses on the journey from the present state to the future state through providing support, guidance and leadership with the understanding of business problem and connecting the dots across people, process and technology. campaign orchestration and decisioning engines - development and implementation of a data driven marketing culture with a shift from product siloed to customer first.

Practical aspects in AI/ML implementation. How various emerging technologies can be effectively integrated with AI/ML to create solution.

Vikram is Director FinTech at SP Jain School of Global Management where he has designed Asia’s first interactive classroom led FinTech program featuring Blockchain, API Banking, AI/ML and IoT labs. He has authored several white papers and articles on FinTech domain. He has an extensive experience in banking, financial services, Fintech, consultancy and training domain. He is also associated as a mentor with various FinTech startups and Fintech focused funds across the globe. He is Fintech ambassador for Maharashtra Govt.'s Uday Fintech platform.
He is also heading research arm of Fintech VC Fund Varanium. He has been a successful banker for more than a decade where he has made key contributions in Financial & Investor Strategy, Business Intelligence, Balance-sheet Management and Business Development & Technology Solutions. He has been a visiting faculty at various prestigious global institutes

Preserving privacy when processing data from multiple sources with machine learning is always a challenge. Organizations may want to perform collaborative data analytics while guaranteeing the privacy of their individual datasets. Combining multiple data sources to support a better algorithmic outcome improves accuracy of prediction, but it may come at cost of confidentiality, if sensitive information is not accurately protected.

Azure Confidential Computing adds new data security capabilities to the cloud and specifically to machine learning processing. By using trusted execution environments (TEEs) to protect your data while in use, with confidential computing, you can use machine learning algorithms across different organizations to better train models, without revealing the processed data.

This session presents the benefits of Azure Confidential Computing in an ML scenario, where two separate health institutes collaborate on data analysis and prediction using Azure Machine Learning, and still mask any sensitive information to protect the privacy of their patients.

A session about protecting data in the cloud with confidential computing.

The use case is for organisations to share data with full confidentiality protection.

Analytics is growing in importance to let the Product people know how the application or platform is getting used. This metrics is important in determining how to increase customer engagement. QAs need to make sure that right set of events are getting raised on specific user actions to ensure we have valid metrics in mind. Automating these scenarios would ensure that we have regression suite in place.

Organizations have to use data and analytics to compete in today’s knowledge economy. At the same time, organizations have to operate in highly regulated industries. This session will explore how organizations can find a balance between using data to compete and managing data as an asset by making three shifts to modernize their Data Governance function.

Maritza Curry has worked in data and analytics roles since 2001. She has been focused on helping organizations, both in South Africa and Australia, to formulate and execute their data strategies since 2012 and became Head of Data at financial services company RCS Group in November 2019. BNP Paribas Personal Finance owns the RCS Group and Curry represents RCS within the BNPP PF Chief Data Officer community. In 2020, Corinium selected Curry as one of the Global Top 100 Innovators in Data and Analytics. She has an MBA from the University of Stellenbosch Business School.

People spend a surprising amount of energy maintaining the status quo. Why? How to prevent it? How to convince them? And what to do if they are right?

After 17 years in executive roles (telco, e-tail, finance), he decided to drop management, switch his career and focus only on data science.