business analytics modern banking: business based · 2017-01-23 · customer care banca...

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Modern banking: Business based on customers' advanced

analyses.

Business Analytics

Financial Services Market.

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Big Data VisionConsultancy Adoption, Use Cases and TechnologyPlatform and assetsData and Analytics: practical cases

Index

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TO MANAGE TODAY THE WHOLE INFORMATION POOL OF ORGANIZATIONS,

INDIVIDUALS AND DIGITAL SOCIETY REPRESENTS A FIRST-MAGNITUDE

CHALLENGE

01. BIG DATA VISION

Diya Soubra's

Data Science Central

Multichannel, self-service, social networks, real-time information.... The role of the IT in knowledge management is increasing in importance, being necessary to properly manage the complexity generated by sheer volume, speed and information variety.

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01. BIG DATA VISION

Data need to be well-managed (Data Management), handled in a scientifically-sound

way (Data Science) to uncover the useful knowledge for the business; to be properly

communicated (Data Visualization), well-stored well and well-guarded (Data

Governance) and –especially- to be properly made available to decision makers.

Data Management

DataVisualization

DataScience

Data Governance

THE INFORMATION TURNS INTO RELEVANT ASSETS, AND THEIR

MANAGEMENT INTRODUCES NEW CHALLENGES THAT CONCERN DIFFERENT

ORGANIZATIONS AREAS …

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These new “data-driven”

management models need some

specific aspects to be rethought:

� Business Orientation: new

formulas to compete and

generate business

� Organization: new

relationships and government

models

� Technology: adoption of Big

Data technologies and

knowledge generation

� Skills: data scientists, executive

profiles information / data

oriented, etc.

People

Visualization

SmenticModels

AnalyticsModels

New Data Sources Enrichment

Metadata/ Mater Data

Data Qualiity

Integration

…… INTRODUCING NEW INFORMATION-ORIENTED MANAGEMENT MODELS

AND PROCESS-ORIENTED, ANALYTICAL VS "TRADITIONAL" MODELS

01. BIG DATA VISION

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Business Orientation

Data management a knowledge generation

Technology

Data and analytics as

key competitiveness factor

BIG DATA ASSUMES THE POSSIBILITY TO CHANGE THE GAME RULES

INVOLVING DIFFERENT AREAS AND ROLES IN THE ORGANIZATIONS…

01. BIG DATA VISION

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Business Case

Business CaseBusiness

OrientationBusiness

Orientation

TechnologyTechnology

Analytical skills and

information management

Analytical skills and

information management

Needs analysisNeeds analysis

AI Potential (models, tools, algorithms)

Business

Tendencies

Business Potential

Opportunity

Business Use Case selection

Business Use Case selection

EconomicTurn around,

Impact Risk

Data Sources

Modelode análisis

Pilot Design

BusinessFollow up

Pilot and results analysis

Pilot and results analysis

Pilot AnnlysisPilot AnnlysisPilot Development

Pilot Development

Deployment

Deployment

Detecciónnuevas

fuentes y canales

Best Practices mercado

Operative

Strategy

OK

Analysis

Results of OperativeSatretgy

Analysis Results

Pilot Go LivePilot Go Live

Change Manag.

Plan

Go Live

Model

Operation

Operation

Business

AI

OK

Analytics GapsCurrent

TechnologyNew technology

requirements

Reference Architecture

Performance Gaps Current Technology

Model Operation

Technological Advice Technology

Implementation

Solutions Bechmarking

… THUS WE PROPOSE TO OUR CUSTOMERS A WORKING METHODOLOGY

TO IMPLANT BIG DATA INITIATIVES AND SCENARIOS

01. BIG DATA VISION

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Big Data VisionConsultancy Adoption, Use Cases andTechnologyPlatform and assetsData and Analytics: practical cases

Index

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TO ADOPT TODAY AN INFORMATION AND INTELLIGENCE-ORIENTED

STRATEGY REQUIRES LOTS OF DECISIONS THAT GO BEYOND THE

TECHNOLOGY

02. CONSULTANCY ADOPTION, USE CASES AND TECHNOLOGY

On-premise

Analytic Capabilities

Deployment Options

Info Types(variety)

LatencyDecision-makingResponsibilitiesAudience

OrganizationTool Capabilities

Real-Time

Batch

Fully-automated

Decision-support

Business

Analytics

Business

Analytics Descriptive, Diagnostic, Predictive, Prescriptive

IT-centricStructured

Unstructured

Business-centric

Cloud / Hybrid

Pre-defined, Tabular, Desktop

Exploration, Virtualization, Mobile

Internal

External

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THE BIG DATA VISION IS MAINLY DETERMINED BY DECISIONS ABOUT

GOVERNMENT AND EXTENSIVE INFORMATION USAGE …

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Storing informationStoring information Data usageData usage Development strategyDevelopment strategy


• Border among transactional an informational. Where do countable systems come from?

• Are informational systems kept on-line with the transactional ones?What not structured information need to be stored ?

• What external information is managed and stored? Is it meaningful to create cross-sectors alliances

• How are laws and regulation gaps managed ?

• Information democracy versus exclusive experts use

• Information like asset entity versus functional areas: the end of functional datamarts

• Intensification of analytical and collaborative function

• One big data for country, geography or only one?

• Predefined use cases and learning use cases

• Evolutionary use cases or vs new business scenarios

• End to end design versus opened, scalable and time redefinable design

• New solutions "open" versus consolidated brands

• Multiple tools use, followed by "natural selection"

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… ... THIS INVOLVES RETHINKING THE ENTITY BOUNDARIES AND WORKING

WITH VARIOUS INFORMATION, STRUCTURED AND UNSTRUCTURED,

GENERATED BY THE BANK ACTIVITY OR BY EXTERNAL SOURCES

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The different flavors of informationThe different flavors of informationE

xter

nal

Inte

rnal

Structured Unstructured

Census Business Information

Market Research

Credit Bureaus

Commercial

Data

Public Data

Open Data

WeatherMobile Payments

Meter’s, RFID, GPS

Sensor

Monitoring

Geoinformation

Communities, Blogs

Social Media

Data

Twitter, Tumblr

Facebook, LinkedIn

Web logs Reports

Email

Operational

Data

Enterprise

“Dark Data”

Contracts

High Velocity

Transactions

Source: Gartner

Contact Center

Specialized webs

City flows


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Usage and Analytics Information Architecture

CRM

SCM

Regulations

ERP

Products Data Mart

Risks Data Mart

Financial Data Mart

Business Data Mart

Current Situation Big Data Situation

Usage must be generalized, allowing the information socialization to anyone according to his level,

generates , reports, consultations, analyses that in case of repeating can serve to the rest. Therefore the

user's interface must be exceedingly simple

Architecture

Data Pool

DATA BELONG TO ENTITIES, NOT TO DEPARTMENTS OR USERS. BIG DATA

LEAVES THE CONCEPT OF SPECIFIC DATAMARTS MANAGED BY BUSINESS

AREAS TO “DEMOCRATIZE” INFORMATION UNDER REQUIRED SECURITY

MECHANISMS


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BIG DATA MUST BE A SCALABLE SOLUTION WITH "UNLIMITED" STORAGE

CAPACITY, WHICH COMBINES THE SOLVENCY RECOGNIZED IN TRADITIONAL

SUPPLIERS WITH THE AGILITY AND FLEXIBILITY OF OPEN SOURCE

SOLUTIONS

Big Data infrastructure sample

The big technology companies are

adopting the more innovative and open

technologies, combining the

current state-of-art with technological

strength and business support

The need to process huge data volumes comes from co mpanies like Google, YouTube, Facebook, etc, who work on the boundaries of storin g capabilities


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Index

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03. PLATFORM AND ASSETS

Indra Smart Plataform for Analytics

ITHINK

PROSPECT

SOFIA 2

ICLOUDBROKER

ANALYTIC LAB

DDS

IICOLABORA

IREALLITY

SMART AGENT

Technologies for

information supplying,

normalization, geo-

localization, enrichment

of structured and

unstructured data…

• that supply

information to the

descriptive and

predictive

behaviors modeling

lab

• which facilitates

development and

advanced graphical

visualization

• automation of rules

and actions, as well

as the scenarios

simulation for the

users

OUR SOLUTIONS ARE SUPPORTED BY A PROPRIETARY PLATFORM THAT

INTEGRATES OWN AND THIRD PARTIES’ TECHNOLOGIES

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03. PLATFORM AND ASSETS

THE INTERNAL ENTITY INFORMATION OFTEN IS INSUFFICIENT TO

UNDERSTAND AND TO PREDICT BEHAVIORS, BEING NECESSARY TO

INTEGRATE NEW RELIABLE SOURCES OF INFORMATION ACROSS SERVICES AS

THE INDRA ANALYTIC LAB

Customer Knowledge

Business Data

• Customers

• Products

• Branches

• Transactions

• Forecasts

• Models

Business Data

• Customers

• Products

• Branches

• Transactions

• Forecasts

• Models

Social Market

Perceptions

• Complaints, claims

(Customer Care)

Social Market

Perceptions

• Complaints, claims

(Customer Care)

Reviews and comments in

Social Networks

Reviews and comments in

Social Networks

Indra Analytic Lab

• Census variables Panels

(homes and companies)

• Indra Listen +Open data

• Smart cities sensoring, …..

Indra Analytic Lab

• Census variables Panels

(homes and companies)

• Indra Listen +Open data

• Smart cities sensoring, …..

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Index

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04. DATA AND ANALYTICS: PRACTICAL CASES

THE FINANCIAL SECTOR IS GOING TO BE A RELEVANT CONSUMER

OF BIG DATA…

BANCA

Areas

Data type Relative value

Volume

Speed

Variety

Dark Data

Forecasted Impact�Area situation:

� The Bank traditionally managed in real-time huge volumes of data for fraud and risk detection, attitude for Product and Services contracting, segmentation, trading etc.

� Financial entities are big consumer of analytical and BI solutions� Big Data assumes an enhancement opportunity in a high-competition market

�Data type: The most common data are text and numbers –strongly structured-. Few multimedia data get generated and used, apart from very specific areas (example: security images)�Obstacles:

� Dependancy from big already in-place investments in business and technology for traditional analytic systems� Little differentiation in data; data sources typically internal� Data and decentralized internal sources growth (self-service model)� Restrictive regulations for compliance, security, confidentiality

�Boosting factors:� Include external data sources (Internet, social networks, ...)� Decision automation in expert knowledge and real-time information intensive areas� Changes in financial reporting, auditing and compliance regulations, requiring a higher level of transaction details

and a greater historical depth� New Business, Product and Services models� Penetration/adoption of new technologies: mobile payment methods, mobile banking, social networking, cloud

� Use cases: � External sources acquiring –internet, social media- for better risk evaluation, contracting attitude, fraud detection� Decision automation: trading, payment media fraud, cash, ...� New analysis technologies for customers behaviour – customer insight� Big information volumes management due to regulations change in financial reporting, auditing and complianc� Competitive positioning and online reputation survey through open sources information analysis� Automatic behaviour patterns and cyber-security threats detection� New Business, Product and Services models based on mobile payments (mobile loyalty programs, localized

services, ...)

The challenge is to actually

implement use cases to

«monetize» Big Data

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� Strategic planning and reporting for factory networks, HHRR; commercial objectives

� Objectives development simulation, P&S campaigns launching

� Management model analysis (traditional, hub&spoke, co-sharing, virtual, ...) for micro-areas and contexts

� Competitive environment analysis (P&S, geography, competences, trends) from open sources

� Brand image analysis and communication

� Cyber-security threats management

� Analytics for HHRR management, talent, innovation

� Purchases advanced optimization

� Billing advanced analysis

� Internal/external sources integration for better risk evaluation

� Financial and Risk reporting: enhanced speed and precision

� Risk liquidity management

� Integrated productivity management on different dimensions like product/service hyerarchy, customer type, geographical area, ...

� Advanced accounting of financial instruments

� Customer service quality enhancement through historical records analyses and advanced analytical methodologies

� Portfolio analysis for specific products and services (loans, ...)

� Fraud detection, automated real-time decision making

� Critical processes monitoring, alarm generation

� On-line channel information analysis

� Customer knowledge enhancement due to RT, 360°, integrated vision of information from different channels

� Sentiment Analysis from open sources

� Customer segmentation, cross-selling opportuninties exploitation, marketing campaigns optimization

� Multi-channel integrated analysis

Sales, Marketing and

Customer Care

Banca Transaccional Corporate areasFinancial and Risks Sales network

New «digital»

Business models

� New mobility-based services and mPayment: loyalty mobility services, pattern recognition and customer behaviour habits.

� Products and Services demand characterization, offer/demand real-time alerting

Use cases to enhance productivity, competitiveness, ...New Business models

… THAT CAN BE APPLIED TO ALMOST EVERY BUSINESS AREA


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Mejora de productividad en sucursales (estimación global e histórica)Monitorización y análisis en tiempo real por tipo de transacción (frecuencia, drill-down)Analítica Real-time: predicciones , correlaciones y segmentación de operaciones según rendimiento.Panel de Notificaciones y control de Métricas

REFERENCES ON ANALYTICS AND BIG DATA (BANKING)


Real-time analyses of the service provided for the whole ATM´s network of the bank in Spain and Portugal (more than 7.000 ATMs) . The goal is to measure the service levelprovided by the ATMs.“Premium” customers identification , particularly the “hidden” ones to the Bank management (transactions with BBVA but resources in other companies)

Enhancement in agencies productivity (global and historical estimate)Monitoring and real-time analysis for transaction kind (frequency, drill-down)Real-time Analytics : predictions, relationships, operations segmentation by performance.Notifications and metrics control Dashboard

Log analysis Big Data Platform in PRODUBAN: analyze Machine Learning solutions that, starting from the logs of different systems, allow to extract new information that for example can help in a predictive infrastructure issue or failure identification.

Competitive Intelligence and technological surveillance with open sources information

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OTHER PILOTS AND ONGOING PROOF OF CONCEPTS


� Commercial: Optimization of branch networks

� Commercial: Budget objectives according to potential of branches

� Commercial: Enrichment of customer segmentation with environmental

information

� Estates: Automatic estate estimation

� Risk: Lost Given Default.

� Customers: Influence Models

� Customers : Advanced customer care.

� Customers : Paydowners

� ……

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Mónica León Santamaría – [email protected]

Centros de Competencia – Business Analytics

Avda. de Bruselas, 35 28108 Alcobendas

Madrid EspañaT +34 91 480 50 00F +34 91 480 50 80

www.indra.es"© Copyright INDRA SISTEMAS, S.A. 2012. Todos los derechos reservados. Todos los datos e información contenidos en el presente documento constituyen una obra cuya propiedad intelectual y/o industrial pertenece a INDRA SISTEMAS, S.A. La reproducción, comunicación pública, distribución, modificación, cesión y cualquier otro acto que no haya sido expresamente autorizado por escrito por INDRA SISTEMAS, S.A. quedan prohibidos.

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business analytics modern banking: business based · 2017-01-23 · customer care banca...

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