12 big data and financial services marketminisites.qaiglobalservices.com/stc2012/paper_...

12/11/2012

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© 2010 Wipro Ltd - Confidential

Big Data and Financial Services Market

Presented By

Rupesh Garg and MeharunissaSheikh (WT01 - HLS)

Wipro Technologies.

© 2010 Wipro Ltd - Confidential2 © 2010 Wipro Ltd - Confidential2

Agenda

3

4 Big Data Sources in Financial Services

1 What is Big Data

2 How it impacts the business and life?

How it helps Financial service Industry

5 Case Study

Market Landscape

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What is Big Data?

“A massive volume of both structuredand unstructureddata that is so large that it's difficult to process with traditional database and software techniques.”


Big Data Types

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Volume, Velocity and Variety

Big Data spans three dimensions:

VolumeVelocity

Variety

Petabytes per day/week

Unstructured data, web logs, audio,

video, image

Real-time capture and Real-time analytics


ABCs of Big Data

https://communities.netapp.com/community/netapp-blogs/netapp-360/blog/2012/05/23/big-data-a-practical-approach-to-all-the-hype

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Big Data – Salient Features and Drivers

http://shhrota.com/2012/01/02/the-big-in-big-data/


Big Data – Market Forecast

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� Larger Data Sets of Historical Data are Needed : Larger market data

sets containing historical data over longer time periods and increased granularity are required to feed predictive models, forecasts and trading impacts throughout the day.

� Manage New Regulatory and Compliance Requirements: New

regulatory and compliance requirements are placing greater emphasis on governance and risk reporting, driving the need for deeper and more transparent analyses across global organizations.

� Increased Focus on Enterprise Risk Management: Financial

institutions are ramping up their enterprise risk management frameworks, which rely on master data management strategies to help improve enterprise transparency, auditability and executive oversight of risk.

� To discover consumer behavior pattern: Financial services companies

are looking to leverage large amounts of consumer data across multiple service delivery channels (branch, Web, mobile) to support new predictive analysis models in discovering consumer behavior patterns and increase conversion rates.

How it helps Financial Services Industry


� Drive to Unlock the Value of Data in Operations Departments: Advances in big data storage and processing frameworks will help financial services firms unlock the value of data in their operations departments in order to help reduce the cost of doing business and discover new arbitrage opportunities.

� Adoption of Predictive Credit Risk Models: Predictive credit risk

models that tap into large amounts of data consisting of historical payment behavior are being adopted in consumer and commercial collections practices to help prioritize collections activities by determining the propensity for delinquency or payment.

� Credit Scoring -> Digital payment histories can allow individuals to build credit

histories, making them candidates for loans and other credit-based financial services.

� Threat Analysis:

� Fraud Detection: In NJ State, there are lot of false insurance claims, hence

insurance companies has put high claims there

� Mobile Money services: Data gleaned from it can provide deep insight into

spending and saving habits across sectors and regions.


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Big Data helping Banking and Financial Services


Big Data helping Banking and Financial Services

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� Mobile Proliferation: Mobile applications and internet-connected devices such

as tablets and smartphone are creating greater pressure on the ability of technology infrastructures and networks to consume, index and integrate structured and unstructured data from a variety of sources.

� Financial data includes "pre-trade" such as bid/ask data necessary to price a financial instrument and post-trade data such as the last trade price and other transaction data.

� Better reporting and yield-management tools that provide deeper visibility into transactions and help meet regulatory requirements.

� Beyond trading, Big Data applications within banking and capital markets include Risk Analytics; Price Discovery; Fraud Analytics; Customer Behavior Analytics, etc.

� Lastly, a Gartner 2010 data management survey of banks and investment services firms underscores the vast potential of Big Data IT solutions within the banking and capital markets sector.



What are the sources of collecting data

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� “Big data” is defined as a capability that allows companies to extract value from large volumes of data. Like any capability, it requires investments in technologies, processes and governance. The research firm IDC forecasts that the big data services and technology market will grow in value from $3.2 billion in 2010 to $16.9 billion in 2015

� The first dimension that is considered is labeled business objective. When developing big data capabilities, financial companies try to measure or experiment. When measuring, organizations know exactly what they are looking for and look to see what the values of the measures are. When the objective is to experiment, companies treat questions as a hypothesis and use scientific methods to verify them.

� The second dimension that is considered is labeled data type. In their normal course of functioning, financial companies collect data on their operations (e.g., sales) and capture it in their database that has a structure or schema. We call this transactional data. In other instances, companies deal with data that come from sources other than transactions and are typically unstructured (e.g., social media data). This combination results in four quadrants, each representing a different strategy: performance management, data exploration, social analytics, and decision science

Big Data sources in financial Services


Big Data strategies:

� Performance management:

The data used for this analysis are transactional, for example, years of customer purchasing activity, and inventory levels and turnover. Managers can ask questions such as which are the most profitable customer segments and get answers in real-time that can be used to help make short-term business decisions and longer term plans.

� Data Exploration

Data exploration makes heavy use of statistics to experiment and get answers to questions that managers might not have thought of previously. This approach leverages predictive modeling techniques to predict user behavior based on their previous business transactions and preferences.

� Social Analytics

Social metrics are critical since they help inform managers of the success of their external and internal social digital campaigns and activities. For example, marketing campaigns involving contests and promotions on Facebook can be assessed through the number of consumer ideas submitted and the community comments related to those ideas. If the metrics indicate poor results, managers can pivot and make changes. For example, low Facebook engagement may mean more interesting and

Big Data sources in financial Services

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Big Data source in financial services


Data through the Mobile Financial Services Lens

� Regulatory Proportionality: Finding appropriate uses for mobile-generated data will require regulation similar to that needed for mobile financial services. In both situations, regulation must keep pace with new technology and protect consumers without stifling innovation or deterring uptake. The development of sensible data standards could increase uptake of both mobile financial services and individual data security.

� Consumer Protection: As with mobile financial services, proper regulation and data ownership processes must be put in place to prevent the theft or misuse of sensitive information.

� Market Competitiveness: In the long term, adequate competition is essential to ensure a wider range of affordable services and interoperability. However, private-sector companies should be encouraged to allow access to non-sensitive data that can benefit populations and deepen their own understanding of individual behavior. Such cooperation may also help telecom operators realize that creating interoperable mobile money systems can benefit them over the long term.

� Market Catalysts: For both the data commons and mobile money, government can serve as a catalyst to ensure legitimacy. This will require open and transparent governance, as the idea of government access to an individual's financial information could discourage uptake of mobile financial services.

� End User Empowerment & Access: Individuals must have a moderate degree of financial literacy, affordable access to a mobile device, and a mobile network connection, in addition to control over their own information.

� Distribution and Agent Network: Analyzing transactional data could determine where there is demand for additional mobile money agents.

http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf

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How Big Data is helping in testing

� In today's Big Data era, Large samples of data provide a powerful tool for testing hypotheses. Applying small-sample modeling to large samples not only wastes the "super-power" advantages but can also lead to incorrect and misleading conclusions.

� Testing using big data becomes more pointed and can handle more complex hypotheses, including more control variables, quantifying more subtle and rare relationships, improving robustness checking, strengthening model validity and generalizability, developing insights through analysis of subsamples, and making inferences even in the presence of some violated model assumptions.


Big Data Can Mean Big Returns in Retail

� Retailers have data from sales completed at stores, sales completed online, calls to customer service, visits to particular web pages, clicks on banner and search ads, merchandise returns, inventory management, supply chain transactions, and more. Now add to that the social data that consumers share in the form of product reviews, social media profiles, status updates, Facebook “likes,” and by “checking in” at physical locations through online services like Foursquare. This data helps greatly in predicting.

� Retailers now have the opportunity to see website traffic for a particular product and compare it to the sales. Before, if a product wasn’t selling it would be removed from the line. Now, managers can readjust pricing; ensure there are enough colors and sizes, and any other aspects that take a look to a sale.

� Big data for retail means a chance to see why a sale didn’t occur. Is it product selection? Pricing? Store display? Ineffective promotional material?

� Big data is incredibly advantageous for retail managers, preparing them to better meet customer expectations and maintain high operational efficiency.

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� Operational efficiency is essential when retailers always want to know what their customers need before they even know they need it.

� For example, using big data retailers now can see, through data from store cards, cashed-in coupons, and purchase history, when a customer may need a refill on a product. This data gives retail marketers the upper hand, sending the low stocked customers promotional material – urging them to buy the refill.

� According to Gartner, organizations that succeed in harnessing big data to better understand their customers and predict changing demand stand to outperform their industry peers by 20 percent.

� There’s a window of opportunity for retailers to gain competitive advantage over the next year by starting to extract value from big data – to enhance customer loyalty, increase sales, streamline operations and more.

�



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� Most large Wall Street banks are looking for better ways to handle large datasets. Bank of America Merrill Lynch, for example, is using big-data techniques to manage petabytes of data for regulatory compliance and advanced analytics. The bank is using technology from Hadoop, an open source framework that supports data-intensive distributed computing, allowing data to be crunched over a distributed network of computers.

Other Market Tools to use:

� IBM Data Center

� HP Financial Services etc. etc.

� Technology Providers: Most of the large database players also provide technology that could help with big data, including IBM, Oracle, Sybase, Tableau Software and Teradata. Smaller players include Vertex and Attivio, a provider of unified information access.

Case Study


Insights from Big Data

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� http://www.networkworld.com/slideshow/52214

� http://smartdatacollective.com/brett-stupakevich/52268/10-trends-shaping-big-data-financial-services

� http://www.insiderlearningnetwork.com/go/network/reglogin

� http://www.insiderlearningnetwork.com/go/network/reglogin

� http://www.authorstream.com/Presentation/ecastrom-1392560-microsoft-big-data/

� http://www.bigdataforfinance.com/bigdata/2012/02/white-paper-big-data-solutions-in-capital-markets-a-reality-check.html?goback=%2Egmp_4294677%2Egde_4294677_member_96532661#more

� http://www-01.ibm.com/software/data/bigdata/

� http://www.referencedatareview.com/blog/us-data-transparency-coalition-focuses-federal-data-capital-markets

� http://slashdot.org/topic/bi/big-data-top-priority-executives-mckinsey-survey/

Refrences

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