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Maximizing Banking Value with Big Data Analytics Chiara Bartalotta, Big Data Engineer Rome, May 2016 Big Data Banking Insights

Big Data Banking Insights - Riccardo Torlonetorlone.dia.uniroma3.it/bigdata/S4-Unicredit.pdf · Rome, May 2016 Big Data Banking Insights . UniCredit Business Integrated Solutions

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Maximizing Banking Value with Big Data Analytics

Chiara Bartalotta, Big Data Engineer

Rome, May 2016

Big Data Banking Insights

UniCredit Business Integrated Solutions

Introduction • UniCredit at a Glance • Data & Analytics Organization • The New Big Data Engineer • Big Data Challenges

Data Processing Steps Machine Learning Use Cases Technology

2

1

2

3

4

5

AGENDA

UniCredit Business Integrated Solutions

3

* Source: UniCredit Company Profile, data as at March 31, 2016

UNICREDIT AT A GLANCE Introduction 1 2 3 4 5

• Banking operations in 17 countries

• International network spanning: ~50 countries

• Global player in asset management: 218.7 bn in managed assets

• Market leader in Central and Eastern Europe

leveraging on the region's structural strengths

Over 143.000 employees

7.839 branches

UniCredit Business Integrated Solutions

4

UniCredit Business Integrated Solutions is the first concrete milestone within the Group Strategic Plan 2012 announced in November 2011 to be achieved. Owned by UniCredit, is created from the integration and consolidation of 16 Group companies (among them UGIS, UCBP, URE, UC) and is dedicated to providing services in the sectors of Information and Communication Technology (ICT), Back Office and Middle Office, Real Estate, Security and Procurement. A new business model, unique in the European banking sector, focused on Business needs (i.e. Commercial Banking, Global Markets, CEE), not only on providing services.

AUSTRIA

ITALY ROMANIA

POLAND

HUNGARY

UK

SLOVAKIA

GERMANY � ~ 10.000 Fte’s*

( y/y pro forma)

■ 11 Countries**

■ 3 Wholly-owned subsidiaries*

* Data as at December 31, 2014. ** UniCredit Business Integrated Solutions operates also in 2 branches, one located in New York and one in Singapore.

CZ REP.

� NET EQUITY: € 367,537,088* � TOTAL REVENUE: €

2,438,382,428*

� NET PROFIT: € 3,896,618*

UNICREDIT BUSINESS INTEGRATED SOLUTIONS: IDENTITY CARD Introduction 1 2 3 4 5

UniCredit Business Integrated Solutions

DATA & ANALYTICS ORGANIZATION

5

Data Scientists

� Data acquisition � Data-flow management � Multiple data sources

integration � Data cleaning

� Mathematical and Statistics algorithms inception

� Machine Learning techniques application

� Value from data extraction

� Systems deployment and migration

� Application management and monitoring

� Data center operations monitoring

� Scope definition � Cost, time and quality

control � Customer relationship

� Architectural Design � Innovation inception � Big data solutions

development � New technologies exploration

UniCredit Business Integrated Solutions Big Data Teams

Infrastructure Data Ingestion

Developers

Project Managers

Introduction 1 2 3 4 5

UniCredit Business Integrated Solutions

Introduction

UniCredit Business Integrated Solutions

Software Engineering

Analytical mindset Mathematical

knowledge Distributed computing experience

Business focus

Database systems proficiency

THE NEW BIG DATA PROFESSIONIST 1 2 3 4 5

6

UniCredit Business Integrated Solutions

DATA & ANALYTICS NEEDS…

7

Big Data Engineers The candidate will enter our team which develops the next-generation technologies that change how millions of customers connect, explore, and interact with the bank. As a big data engineer, you are expected to give your contribution to projects as our fast-paced business grows and evolves. We need our engineers to be versatile and passionate to tackle new problems as we continue to push technology forward. With your technical expertise you manage individual projects' priorities, deadlines and deliverables. You design, develop, test, deploy, maintain, and enhance software solutions in a big data environment.

Data Scientists As a Data Scientist, you should be passionate about using data to drive advanced analytics. You are able to work cross-functionally with both business and big data engineers to effectively deliver actionable results. You are a well-rounded top performer who is able to quickly grasp data and critically think through strategic issues the next. You are able to communicate effectively while delivering complex data-driven findings. The ideal candidate is an independent, solution-oriented thinker able to apply analytical rigor and statistical methods, and driving toward insights and solutions.

Introduction 1 2 3 4 5

UniCredit Business Integrated Solutions

BIG DATA CHALLENGES

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Secure data storage and operation logs, data provenance and lineage

Secure computation in distributed programming methodology and non relational data base architecture

Infrastructure Security Data Management

Performance, system responsiveness, disaster recovery, resources allocation

Scalability

Access polices, laws and regulations, customer and employers data protection

Privacy

Shortage of skilled workers, small communities, lack of awareness

Talent Gap Unclear picture of Big Data World, many technologies existing making the choice hard, hard to tell Big Data and Not Big Data apart

Uncertainty

Raising costs of infrastructure and data storage, high costs of operation and management and integration within existing ecosystems

Costs

Pay attention to…

Introduction 1 2 3 4 5

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases Technology

9

1

2

3

4

5

MOVING ON

UniCredit Business Integrated Solutions

DATA PROCESSING STEPS

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1 2 3 4 5 Data Processing Steps

Storage Processing Acquisition Exploration Elaboration

UniCredit Business Integrated Solutions

DATA PROCESSING STEPS

11

1 2 3 4 5 Data Processing Steps

Storage Processing Acquisition Exploration Elaboration

UniCredit Business Integrated Solutions

DATA ACQUISITION

12

Trustworthiness of the original sources

Data Quality and Provenance Different sources: application databases (OLTP), datawarehouse (OLAP), log files, documents, etc.

Data Structure: different types of format

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

DATA PROCESSING STEPS

13

1 2 3 4 5 Data Processing Steps

Storage Processing Acquisition Exploration Elaboration

UniCredit Business Integrated Solutions

DATA PROCESSING

14

� Anonymization � Filtering of irrelevant data � Data sampling � Data conversion and integration � Dataset joining

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

DATA PROCESSING STEPS

15

1 2 3 4 5 Data Processing Steps

Storage Processing Acquisition Exploration Elaboration

UniCredit Business Integrated Solutions

DATA STORAGE

16

� Scalable and distributed systems � Redundancy � Disaster recovery � Different technologies

Availability Consistency

Partition Tolerance

CA

CP AP

CAP theorem

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

RESOURCES AND PROCESS DISTRIBUTION

17

Scale up Scale out

Add resources in one machine Add resources in multiple nodes and get parallelism

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

DATA PROCESSING STEPS

18

1 2 3 4 5 Data Processing Steps

Storage Processing Acquisition Exploration Elaboration

UniCredit Business Integrated Solutions

DATA ANALYSIS

19

� Goal: extract value from data � Big Problem: understand results and

find better pattern to extract useful information from the large amount of data

� Exploratory data analysis � Combine statistics and probability models

to explore data and estimate values or make predictions

� Machine Learning Techniques

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

DATA ANALYSIS

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REAL TIME

Scheduled analysis to collect, clean and process data in order to produce results from previously gathered data

BATCH

REAL TIME

BATCH

Data processing at acquisition time enabling

fast results' elaboration

1 2 3 4 5 Data Processing Steps

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases Technology

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1

2

3

4

5

MOVING ON

UniCredit Business Integrated Solutions

MACHINE LEARNING

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Knowledge acquisition automatization algorithms’ design and implementation through: � Data-driven models � Continuous learning based on provided input

Goal

Main Machine Learning Areas

Supervised Learning Unsupervised Learning

1 2 3 4 5 Machine Learning

UniCredit Business Integrated Solutions

MACHINE LEARNING: SUPERVISED LEARNING

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01 02 03

Training with data that has a well known class

After the training, the classifier can assign the correct class to new data

Continuously learning with new training

datasets

1 2 3 4 5 Machine Learning

The desired output for each input is known a priori. The goal is to discover a rule to map a general input to the correct output.

Example - Classification

UniCredit Business Integrated Solutions

MACHINE LEARNING: UNSUPERVISED LEARNING

24

The output for each input is not known a priori. The goal is to discover pattern in data and find structures in inputs.

Clustering Discover a structure in the dataset, divide inputs in different groups according to their features.

Predict users’ interests, filter items following user’s engagement.

Collaborative Filtering

Examples

1 2 3 4 5 Machine Learning

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases

• Inter/Intra-Bank Risk Detection • CRM • My Business View • Call Center Support

Technology

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1

2

3

4

5

AGENDA

UniCredit Business Integrated Solutions

Inter/Intra-Bank Risk Detection – CLIENTS’ NEEDS

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Determine and classify UniCredit-related inter/intra-bank payments’ risk

Goal

1 2 3 4 5 Use Cases

UniCredit Business Integrated Solutions

Inter/Intra-Bank Risk Detection – EXAMPLE

01

02

03

A transaction event is recorded

Does the transaction chain involve a high-risk step?

Classify risk and notify Security Department

1 2 3 4 5 Use Cases

27

UniCredit Business Integrated Solutions

Inter/Intra-Bank Risk Detection – DATA PROCESSING

INPUT

CHAIN EXPLORATION

OUTPUT

Identify Country and transaction Bank

Risk Category

Validate history and next step against security rules

1 2 3 4 5 Use Cases

28

UniCredit Business Integrated Solutions

Inter/Intra-Bank Risk Detection – ARCHITECTURE

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Transactions

Branch Notifications

Message Normalizer

Listener

Transaction Processor

Branch Data Rule Engine

Publisher

Fraud Catcher

Clean Transaction Queue

Branch Delay Queue

Fraud Queue

Transaction Queue

E-mail Notification

Web Service

1 2 3 4 5 Use Cases

UniCredit Business Integrated Solutions

Inter/Intra-Bank Risk Detection – APPLICATION LOGIC

Business Rules Logic Business logic is decoupled from application, enabling scalability in rules’ editing and adding.

Application Logic All components’ development and integration logic necessary to keep application running smoothly.

1 2 3 4 5 Use Cases

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UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases

• Inter/Intra-Bank Risk Detection • CRM • My Business View • Call Center Support

Technology

1

2

3

4

5

AGENDA

31

UniCredit Business Integrated Solutions

CRM – CLIENTS’ NEEDS

Catalyze marketing campaigns planning and carriage via a customer behavior analytics platform

Goal

1 2 3 4 5 Use Cases

32

UniCredit Business Integrated Solutions

CRM – EXAMPLE

01

02

03

Mario Rossi buys a new motorcycle

May Mario be interested in “Guida Protetta” assurance?

How can we validate the match and notify Mario?

1 2 3 4 5 Use Cases

33

UniCredit Business Integrated Solutions

CRM – DATA PROCESSING

INPUT

DATA ENRICHMENT

OUTPUT

TRIGGER

Identify transaction type � Credit Card � Bank Transfer � Debit Card � …

Potential product of interest

Enrich Information with customer data and

transaction’s market category

User notification / Product proposal

1 2 3 4 5 Use Cases

34

UniCredit Business Integrated Solutions

CRM – ARCHITECTURE

Data Preparation

Machine Learning

Model Repository

Events

Enrichment

Business Interface

Filtering & Scoring

Training / PMML

Schema Analysis Model

Configuration

Campaign Manager

1 2 3 4 5 Use Cases

35

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases

• Inter/Intra-Bank Risk Detection • CRM • My Business View • Call Center Support

Technology

1

2

3

4

5

AGENDA

36

UniCredit Business Integrated Solutions

My Business View – CLIENTS’ NEEDS

Offer end user a set of reports containing aggregated data for a merchant’s issuer, acquirer and terminal management activities

Goal

1 2 3 4 5 Use Cases

37

UniCredit Business Integrated Solutions

My Business View – EXAMPLE

01

02

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Mario Rossi wants to analyze his bookshop’s trends

How could we support him?

Mario can review his activity’s KPIs through MyBusiness View

1 2 3 4 5 Use Cases

38

UniCredit Business Integrated Solutions

My Business View – DATA PROCESSING

INPUT

RULE ENGINE

OUTPUT

Transactions carried on in the activity

Activity reporting through dashboards

Perform aggregation and compute KPIs

1 2 3 4 5 Use Cases

39

UniCredit Business Integrated Solutions

My Business View – ARCHITECTURE

Web Service

Merchant Data

Card Data Clustering

Cache

1 2 3 4 5 Use Cases

40

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases

• Inter/Intra-Bank Risk Detection • CRM • My Business View • Call Center Support

Technology

1

2

3

4

5

AGENDA

41

UniCredit Business Integrated Solutions

Call Center Support – CLIENTS’ NEEDS

Monitoring customer calls, requests and issues raised through the call center system

Goal

1 2 3 4 5 Use Cases

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UniCredit Business Integrated Solutions

Call Center Support – EXAMPLE

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02

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Mario Rossi calls contact center to raise a complaint: his credit card is not swiping correctly lately

Operator sees that Mario is a multi-year customer and he has raised 3 complaints in the last 2 months

Operator proposes Mario to replace his card with a new “Flexia Classic” card discounting his 1st year fee

1 2 3 4 5 Use Cases

43

UniCredit Business Integrated Solutions

Call Center Support – DATA PROCESSING

INPUT

PROCESSING ENGINE

OUTPUT

Operator inserts caller’s Name into the dashboard

Customer history and potential suggestions

Extract and merges customer data from

heterogeneous sources

1 2 3 4 5 Use Cases

44

UniCredit Business Integrated Solutions

Call Center Support – ARCHITECTURE

Web Service

Indexed Files Customer Data

Service Management Data

1 2 3 4 5 Use Cases

45

UniCredit Business Integrated Solutions

Introduction

Data Processing Steps Machine Learning Use Cases Technology

1

2

3

4

5

MOVING ON

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UniCredit Business Integrated Solutions

TECHNOLOGIES 1 2 3 4 5 Technologies

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UniCredit Business Integrated Solutions

GITHUB OPEN REPOSITORY

https://github.com/UniCreditDnA 1 2 3 4 5 Technologies

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Chiara Bartalotta e-mail [email protected]

mobile +39 340 3743961

www.unicreditgroup.eu/ubis