How Retail Banks Use MongoDB

How Retail Banks use MongoDB

Kunal TanejaBusiness Architect, Financial [email protected]

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• About MongoDB – The Company– The Database (MongoDB)

• Challenges in Financial Services

• Case Study – Single View of Customer

Agenda

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MongoDB

NoSQL database

Document Data Model

Open-Source

General Purpose

{ name: “John Smith”, date: “2013-08-01”, address: “10 3rd St.”, phone: {

home: 1234567890, mobile: 1234568138 }}

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MongoDB Overview

400+ employees 900+ customers

Over $231 million in funding(More than other NoSQL vendors combined)

Offices in NY & Palo Alto and across EMEA, and APAC

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Leading Organizations Rely on MongoDB

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• The Community

• MongoDB in Financial Services

• Case Study – MongoDB as a Tick Store

Agenda

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MongoDB.

NoSQL Document based database.

Designed to build todays applications.

•Fast to build.

•Quick to adapt.

•Easy to scale

•Lessons learned from 40 years of RDBMS.

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Relational Model

PlanID BenFK Plan

100 1 PPO Plus

200 2 Standard

EmpID Name Dept Title Manage Payband

9950 Dunham, Justin

500 1500 6531 C

EmpBenPlanID EmpFK PlanFK

1 9950 100

2 9950 200

BenID Benefit

1 Health

2 Dental

DeptID Department

500 Marketing

TitleID Title

1500 Product Manager

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Document Model

EmpID Name Dept Title Manage Payband Benefits

9950 Dunham, Justin

Marketing Product Manager

6531 C

EmpBenPlanID EmpFK PlanFK

1 9950 100

2 9950 200

Health PPO Plus

Dental Standard

PlanID BenFK Plan

100 Health PPO Plus

200 Dental Standard

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Document Model

EmpID Name Dept Title Manage Payband Benefits

9950 Dunham, Justin


6531 C Health PPO Plus

Dental Standard

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MongoDB - Agility

Dynamic Schemas

V 1.0 V 1.1 V 2.0

EmpID Name Dept Title Manager Payband Benefits

9950 Dunham, Justin


6531 C

EmpID Name Title Payband Bonus

9952 Joe White CEO E 20,000

EmpID Name Dept Title Manager Payband Shares

9531 Nearey, Graham

Marketing Director 9952 D 5000

Health PPO Plus

Dental Standard

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ShellCommand-line shell for interacting directly with database

MongoDB - Usability

DriversDrivers for most popular programming languages and frameworks

> db.collection.insert({product:“MongoDB”, type:“Document Database”})> > db.collection.findOne(){

“_id” : ObjectId(“5106c1c2fc629bfe52792e86”),“product” : “MongoDB”“type” : “Document Database”

}

Java

Python

Perl

Ruby

Haskell

JavaScript

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“No SQL”, But Fully Featured

MongoDB{ customer_id : 1,

first_name : "Mark",

last_name : "Smith",

city : "San Francisco",

accounts : [ {

account_number : 13,

branch_ID : 200,

account_type : "Checking"

},

{ account_number : 14,

branch_ID : 200,

account_type : "Savings"

} ]

}

Rich Queries• Find all Mark’s accounts• Find everybody who opened an account

last month

Geospatial• Find all customers that live within 10

miles of NYC

Text Search• Find all tweets that mention the bank

within the last 2 days

Aggregation• What’s the average value of Mark’s

accounts

Map Reduce• How many customers that have a

checking account also have an IRA

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MongoDB - Scalability

• High Availability

• Auto Sharding

• Enterprise Monitoring

• Grid file storage

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Agenda

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FS/Banking Challenges1. Changing Regulatory Requirements

2012 2013 2014 2015 2016 2017 2018 2019

ICB Ring-fencing

ICB Loss Absorbency

Leverage Ratio - Basel III

NSFR – Basel III

MiFID II

T2S

LCR – Basel III

ICB / Competition

Audit Policy

Cross Border Debt Recovery

Financial Transaction Tax

Market Abuse Directive (MAD II)

PRIP

Accounting Directive Review

AIFM Directive

EU Transparency Directive

EU Reg on Credit Rating Agencies

CRDV

Internal Governance Guidelines

FATCA

PDPD

EMIR

SWAPS Push Out – Dodd Frank

Securities Law Directive (SLD)

Volker Rule – Dodd Frank

Short Selling

Close Out Netting

Crisis Management

Recovery & Resolution

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Source LayerSource Layer BI Abstraction & Reporting LayerBI Abstraction & Reporting Layer

Acquisition LayerAcquisition Layer

Extraction & Staging

Cleansing

Atomic LayerAtomic Layer

MDM

Ad-hoc reports & Analytics

Dashboards & Web Reports

Web Services

Corporate Data WarehouseCorporate Data Warehouse

Data Lineage and Metadata

ETL

Transformation & Access Layer


Transformation & Calculation

Performance & Access

Change Data

!Reject Data

Normalisation & Storage

FS/Banking Challenges1. Changing Regulatory Requirements

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Primary:NYC

Secondary:NYC

Primary:LON

Primary:SYD

Secondary:LON

Secondary:NYC

Secondary:SYD

Secondary:LON

Secondary:SYD

FS/Banking Challenges2. Latency and Global synchronisation of information

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FS/Banking Challenges2. Latency and Global synchronisation of information




Cleansing




Web Services



ETL





Change Data

!Reject Data


HK

London

New York

4pm EST4pm EST

4pm GMT4pm GMT

4pm UTC4pm UTC

Actual Risk 24 hours late

Actual Risk 24 hours late

Wait & sync

Wait & Sync

Wait & Sync

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Web

CallCenter

Customers

Impact•Similar processes and systems duplicated•Changes done in multiple places•Siloed view of customer•Siloed experience by customer•Cross-channel/silo data is previous day

Central Functions•Risk •Compliance•Legal•…

Loans Cards

Deposit Accounts

…

Mobile

Computer

Call

Center

Mob

ile

ATM

Branch

EOD

EOD

EOD

EOD

Branch

Computer

Call

Cent

er

…

FS/Banking Challenges3. Multi-channel and 360 View of Customer

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Cleansing


MDM



Web Services



ETL





Change Data

!Reject Data


FS/Banking Challenges3. Multi-channel and 360 View of Customer

LoansLoans

Credit CardCredit Card

PaymentsPayments

Loans meets CardCard meets Payments

….

Loans meets CardCard meets Payments

….

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Approaches tried in the pastSiloed Data Marts

Source Layer

Source Layer

BI Abstraction & Reporting LayerBI Abstraction & Reporting Layer



Cleansing




Web Services



ETL





Change Data

!Reject Data


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Polymorphic Operational Data Store

Source Layer

Source Layer

BI Abstraction & Reporting LayerBI Abstraction & Reporting Layer



Cleansing




Web Services



ETL





Change Data

!Reject Data


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Where MongoDB is being used

Business Domain Solution Areas to Consider

Customer Engagement Single View of a CustomerCustomer Experience ManagementResponsive Digital BankingGamification of Consumer ApplicationsAgile Next-generation Digital/Social/Mobile Platform

Marketing Multi-channel Customer Activity CaptureReal-time Cross-channel Next Best Offer Location-based Offers

Risk Analysis & Reporting Firm-wide Liquidity Risk AnalysisTransaction Reporting and Analysis

Regulatory Compliance Flexible Cross-silo Reporting: - Basel III, Dodd-Frank, etc.Online Long-term Audit TrailAggregate Know Your Customer (KYC) Repository

Reference Data Management [Global] Reference Data Distribution Hub

Payments Corporate Transaction Reporting

Fraud Detection Anti-Money Laundering

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Agenda

Retail BankingHow MongoDB and Infusion are helping Banks in the Digital era

Andy Ross (Infusion) and Kunal Taneja (MongoDB)

5 August 2014

Overview of InfusionWe help leading companies navigate the digital era

With over 600 employees worldwide and 15 years of success and growth, Infusion is focused on innovation as the way for business to flourish.

Infusion helps leading companies navigate the digital era.

A bit about us

How we help clients

Data & Analytics

Mobility

Digitalized

Customer

Experience

WebDigita

l Strategy

Innovation

Design Agency

Enterprise software development

Managed services

A history of success with the largest customers

Trends in Retail BankingDrivers and themes

Drivers of change

Regulation

Customer v product focus

Return of the branch

New entrants

New technology

Digital themes

Mobility

Deliver faster

and for less

Personalisation

Big data

Customer

experience

The challenge: how does a big company act like a start-up?

The MetLife story

One day we got an email from our friend Gary…

New MetLife CIO with a (30/60/90) planGet a slow moving company to move fastAttract talented technologistsSolve long-standing and difficult business problems

Business context

One day we got an email from our friend Gary…

Asked if we could build an application thatwould produce a 360° view of his new customersusing innovative technologyand have a Facebook style interface?

Business challenge

70different systems

140yrs

customer data

45million

policies

100million

transactions

Our approach: GO FAST!

90days

2week

s

Sold the idea

Delivered the application

Our approach: act like a start-up

Get software built and optimize

The Wall unmasked

User-centric design

Dramatic productivity enhancements

Gone viral Changing how they “do projects”Is something the organization can, and is, rallying around and aligning to

The Wall lives

Additional benefits

Why were we successful?

Strong champion

Modern technology

Enterprise ready

Incubation

Behaved like a start-up

You can do things differently!

You can push

harder than you

think

You need a strong

champion

Sell the idea

Lessons learned

Questions?

Stay tuned after the webinar and take our survey for your chance to win MongoDB swag.

How Retail Banks Use MongoDB

Technology

Transcript of How Retail Banks Use MongoDB