Banking on Big Data: Harnessing Big Data to drive valuable BigDecisions презентация

$8.84 Billion
2013

178,600+
Employees Globally

20, 000+
Projects in 40 countries

25+ Regional Sales Offices
75+ Global Development Centres

Financial Services: 42.3%
Healthcare: 25.4%
Retail, Manufacturing & Logistics: 21.1%

20.4%

of banking organisations globally have invested in big data1

>34%

Between 2012- 2017, the uptake of big data analytics amongst
larger enterprises in the UK will more than double to ~30% of organisations 3

30%

of large global companies will have adopted big data analytics for at least one Security or Fraud Detection Use Case by 2016 (up from 8% today) 2

25%

Business value

Big Data & Analytics

Traditional Analytics & BI

Improved understanding of customer
Information based business decisions
Deeper insight into risk
Increase revenue
Reduce cost
Mitigate risk and ensure compliance

Additional data sources to enrich customer profiles
Variety of unstructured information to better understand context
Real time analysis

Structured & transactional customer data
Ad hoc & retrospective pattern analysis

Deal with aggressive and innovative non-bank competitors by leveraging data as an asset

Develop new and reliable sources of revenue & increase business value of customer relationship through analytics

Incorporate mobile banking as a regular delivery channel & develop a strategy around social media to personalise engagement with customers

Achieve & monitor regulatory compliance across Line of Businesses and Business Functions

Improving branch & channel efficiency and effectiveness
Helping to drive high value, high touch traffic back to branches

Customer
Centricity

Improved targeting of customer segments
Moving from a product to customer focus
Better management of sales leads across channels
Inclusion of customer incentives to influence behaviour

Reduce Costs &
Increase Revenues

Branch, ATM
Online, Mobile
Omni-channel
Channel management & integration

Everyone’s Mobile

Sentiment analysis
Social media analysis
Credit analysis
Customer profitability & lifetime value
Predictive analytics

Customer Insight

Ability to process increased volume & variety of data
Cost effective technology

Technology Advancement

Risk & capital management, Risk adjusted pricing
Portfolio risk management, Fraud/AML

Risk & Compliance Management

Example Outcomes

Profile

Contact History

Transaction

Models

Big Data Analytics

Customer View

Integrated Web Intelligence

The Web Visit
What searches?
How did they get you?
Page navigation

Research
What do they look at?
What do they search?
Do they dig deeper?

Purchase Path
Which product?
How far into the process?
What’s looked at during purchase?

Social Media

Verbatim
Blogs
Tweets
Postings
Reviews

Internal Text Data

Research
Verbatim
Ad-hoc
Longitudinal studies
NPS/Satisfaction

Other Direct Contact
Branch interview Records
Branch Enquiries
Manger Notes
E-Mails

Call Center
Queries
Complaints
Service issues

Micro-segmentation

Higher Quality Leads

Better Fraud Detection

More accurate Propensity Models

Multi-channel Customer Sentiment

Achieving compliance with evolving regulatory norms

Strategic planning for the BASEL III world

Capital & risk strategy

Implementation management

Challenges

Response

Timely prediction and reduction of churn

Include customer contact (e.g. call centre transcripts) & social media data
Analyse customer sentiment
Model and score churn propensity

Cross and Up-selling

Efficient and precisely targeted marketing
Increased Cross- and Up-sell

Analyse & model response behaviour
Select campaign addresses based on micro-segmentation

Data Offloading

Performance & scalability

Increased performance and storage
Enabled power of analytics on wealth of data

Segmentation

Client lifestyle analysis and spend prediction
Increased customer satisfaction

Advanced analytics to enhance client lifestyle analysis and profiling
Predictive analysis of spend

Domain

Comprehensive risk profiling
Improved risk evaluation

Refine risk profiling models frequently to adapt to dynamic business environment

Rapid Value Delivery

Flexible, Agile & Economical

Relevant Business Apps

Intuitive, Focused and Bite-size BI & Analytics

Sophisticated BI & Analytics

Leverage Universal Data

Select proven Technologies

Agility for Business Change

Easy to Build and Manage

Spend time on BI & Analytics, where it matters most (not on building infrastructure)

Manage all-structures of data with Universal Data Management

Deliver subject areas in weeks, not in months or years

Faster business value realisation with proven set of technology options

Install, configure & customize, don’t develop

Smarter fraud detection capabilities reduce losses and improve recoveries
Direct fulfillment of all CRO needs, providing them with business discovery tools and services
Proactive risk management across LoBs and product lifecycles with stress testing and scenario analysis

RFC Data and Analytics Platform

Flexible systems and processes to accommodate changing regulatory requirement

Holistic risk assessment, fraud detection and compliance application that ensures adherence to constantly changing regulatory requirement

What?

App Features

Benefits

Enhance fraud detection and prediction algorithm
Historical data set was stored in a Hadoop cluster (100+ nodes)
Ran several algorithms to prepare clustered data
Neural network algorithm was developed

Real-time Reporting @ Leading Financial Services Company

Response time was significantly improved

Substantial performance gains were realised in data service aggregation scenarios by reducing the number of data service calls from RTM

PoC for conversion from Cognos to BOBJ
Infrastructure – Installed Hadoop, HBase, MySQL Performance tuning
NoSQL database for real-time service
Big data archive management for cost effective archival and retrieval

20% improvement in lead conversion

Operational cost savings of 10 – 20%

Greater NPS and customer satisfaction

Captured last 55 years of customer data involving 23 Million Customers, 13 Million Policies, 60 Million Claims, 8500 Active Products
Segmentation of customers leveraging machine-learning techniques
Churn analysis at each individual cluster level with combinations of net-worth, transaction volume and churn rate

Customer Segmentation and Churn Prediction @ Leading insurance major

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Big Data and Analytics is a key driver in the financial services sector to help businesses run better & run different

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Start small, think big. ROI on Big Data and Analytics is often too big to ignore