Making Sense of Big Data - The Highs and Lows of Big Data in Financial ServicesKeith Prince – IBM Industry Solutions Executive, EMEA

“If we look at our financial services clients, tick data is prevalent—they’re holding 40,000 to 50,000 positions and a universe of 7 million to 8 million securities. With that going back 15 years, it’s a lot of information. If you look at compliance, there are things like phone transcripts — you’re storing every phone call made in and out of a bank and again, that’s an enormous quantity of data. We’re seeing ways of communicating outside of the telephone proliferating as well. Whereas before you’d see email archives getting up to thousands of terabytes, you’re now tracking and monitoring instant messaging and social media.”

What Is Big Data & Big Analytics?"Big Data & Big Analytics" are terms applied to data sets whose size is beyond the ability of commonly used software tools and data management processes to capture, manage, and process the data within a valuable elapsed time.

When Was The Big Data Phenomenon Born?

1948

Challenges Facing Financial Services Firms

Customer Engagement & Trust

Performance & Growth in Downturn

Balancing Risks & Rewards

Operational Efficiency and Cost Reduction

Global Market Volatility

Increased Regulatory Pressures

Business leaders frequently make decisions based on information they don’t trust, or don’t have1 in 3

83%of CIOs cited “Business intelligence and analytics” as part of their visionary plansto enhance competitiveness

Business leaders say they don’t have access to the information they need to do their jobs1 in 2

of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions60%

Challenges Facing Financial Services Firms

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ts • Develop client analytics and new segmentation strategies

• Optimize client channel interactions• Attract and retain specialized skills• Re-establish the brand/customer trust• Turn clients into advocates• Enhanced product development

• Add new data quickly & cost effectively• Break line of business silos• Modernize IT delivery• Ability to use analytics anywhere• Protect investment in business analytics

• Adjacent & End-to-End Risk Modeling• Create a risk-aware culture• Improve/automate compliance frameworks• Continuously measure and forecast risk• Address risk models, scenarios, stress

testing and data quality

Challenges Facing Financial Services Firms

Challenges Facing Financial Services Firms

BIG DATA DEMO

Building Insight Is A Step-by-Step Process

CAMPAIGN SELECTION

CAMPAIGN MEASUREMENT

PROPENSITY MODELLING

SIMPLE OPTIMISATION

FULL OPTIMISATION

CATEGORIZE, DESCRIBE & MODEL PROSPECTS & CUSTOMERS

FORECAST CAMPAIGN PERFORMANCE

CAMPAIGN EXECUTION

Segmenting Value In A Social Context

High Value

Potentially High Value

Medium Value

Low Value

Transaction Based

Rewards Seekers

Loyalty Matters

Community Spirited

Social Activist

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engagement analytics

Brand Advocates‘I want all my friends, family and community to benefit from my positive experiences’

• Active communicator• Values oriented & chooses

brands that match

• Engages with multiple high value brands

• Significant wallet share

Engaged Consumers‘I like being recognized for sharing my experiences and ideas’

• Highly engaged over long term

• Connects with FFF

• Wants a more personalized experience

• Enjoys being served

Smart Purchasers‘I want the best products for my family’& lifestyle’

• Low-Medium engagement but RFV is high

• Information is important

• Sees rewards as part of price

• QVC important

Price Conscious‘I need to buy the best value product’

• Low engagement & value• Shops for simplicity & price

• Only needs basic product• Not brand conscious

Things Can Also Go Wrong As Well As Right

• Bad data is inevitable as volume and variety increases• Big Data technologies aren’t a universal hammer• It’s not about acquiring more data, it’s about understanding context• How quickly can you react to unforeseen events?• Sometimes the data is the question – data finds data• More data should lead to better prediction• Sometimes, you already have the data you need to make a start• Big Data can help you think – what if this was different?• Single Version of The Truth – or all versions of the truth?• Adopt a policy of Test & Learn• Structured and unstructured are data

Getting It Right From The Start

BI / Reporting

BI / Reporting

Exploration / Visualization

FunctionalApp

IndustryApp

Predictive Analytics

Content Analytics

Analytic Applications

IBM Big Data Platform

Systems Management

Application Development

Visualization & Discovery

Accelerators

Information Integration & Governance

HadoopSystem

Stream Computing

MPP Data Warehouse

• Ensure exec sponsorship for using Big Data to make it easier and more cost effective to deliver, manage and change

• Design-in acquisition, storage and use of the best detail (geocode & timestamp everything, risk-to-value analysis vintages)

• Design-in flexibility to update data and deliver insights/outcomes at varying speeds and times

• Testing requirements will ask big questions of the data infrastructure, for example• Capture all data/signals around the creation and use of the model(s), data, validation

and sign-off by internal compliance owners and governance oversight committee('s)• Design data and auditing flows to easily cope with adjacent models and more than one

exchange standard• Maximise integration, attribution and correlation of diverse alternative data:• Help to incorporate long tail walk throughs for modelled and un-modelled risks• Need to use robust data interpolation techniques to upscale short term history (climate

model based on <100 years of observations when we need 1 in 200/300 context)• Need multiple information infrastructure delivery models to test and implement scalable

architecture (insourced/outsourced, managed, cloud, MPP, Hadoop, etc)

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BIG DATA EXPERIENCES

11 TRILLION CALCULATIONS IN 6 MINUTESTop 5 Investment Mgmt Firm200-1000x speed up of real-time analysis incorporating 100,000 simulations & 27 economic sensitivities for 400,000 securities across 150 funds. At 10% of the previous system’s TCO.

CONTINUOUS ASSESSMENT OF CATASTROPHIC EVENTSGlobal Re-InsurerCatastrophe modeling utilizes diverse data sources, including real-time weather and event monitoring, reduced from 7 days to 2 hours helps to minimize portfolio risk , maximize underwriting opportunities and optimize capital reserves.

MAKING SENSE OF 40MILLION EMAILS A MONTHA Top 3 US BankMining emails for consumer sentiment and employee effectiveness, analysing voice data for changes in tone and inflection has helped to increase the Promoter universe.

ANALYZING DATA @ MARKET SPEEDGlobal Exchange GroupStays ahead of the game despite trade speeds measured in microseconds, data volumes compound at 100% pa, peak volumes can double in a day and 4TB of fresh data every day.

THE ANALYTICS PLATFORM FOR INTERNAL MODELING & ORSAA Top 3 UK InsurerCollecting all customer, policy, financial and market data to explore competing approaches and create the most accurate capital and integrated risk models.

DYNAMIC RISK BASED PRICING A New market EntrantDynamic pricing and driver risk evaluation based on miles covered, time of journey, location utilising data from a telematics device in the insured vehicle.

Case Study Description Relevance to FS Clients

Enhanced Customer Profiling

Consumer profiling for offer targeting and optimisation by the merchant/issuer Stimulates usage and provides opportunities for campaign mgmt

Merchant Offer Targeting Offers linked to consumer’s location and proximity to qualifying (or new) merchants Trigger/event based marketing service

Social Media Analytics Classification of intent, sentiment, influence and engagement Addition of social dimensions to consumer profiling across brands/time

Lifestage Merchant Campaigns

Enhanced/enriched consumer profiling to recognise lifestage events Enables merchants to develop pro-active campaigns for lifestage signals

Customer Experience Management

Use all available information sources to understand and monitor ‘experience’ Issuer and merchant relevance but also indicative of quality of service

Customer Data Integration CDI for consumer and commercial entities and ability to graph interrelationships Extends influencer network analysis beyond ‘the brand’ – yield and uplift impact

Fraud Detection Expand data inputs to improve detection and prevention of loss due to fraud Economic value of accurately fraud events to improve detection & loss prevention

Catastrophe Modelling Ingestion and analysis of massive amounts of ‘event’ data for catastrophe modelling

Ingestion and correlation of a wide array of data to predict impact on engagement

Market Smart Service FICO’s marketing analytics service for it’s customers – expansion of ad-hoc analytics

Flexible analytic appliance to provision data and analytic services at a low cost point

Transaction Analysis Consumer spend and merchant category profiling across all history + fraud for credit card assocation network

Broad spectrum analysis of all transaction data for profiling and opportunity analysis

Marketing Services Direct behaviour based marketing for customer incentive and loyalty programs – retail + CPG + health

Creation and delivery of incentive programs

Campaign Management Reduction in campaign management latency – event lag Campaign execution in less time and at less cost

Nielsen Customer Insight Improving the delivery of data services from one of the largest global brands Delivering data services and insights that are easy to consumer to tight SLA’s - at volume

Big Data Success – By Design

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