How Retail Banks Use MongoDB
 

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How Retail Banks Use MongoDB.

How Retail Banks Use MongoDB.

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  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • MongoDB provides agility, scalability, and performance without sacrificing the functionality of relational databases, like full index support and rich queriesIndexes: secondary, compound, text search (with MongoDB 2.4), geospatial, and more <br />
  • Lots of new regulations coming in, and most of them deal with Data!. Some of them ask you to keep more data while others ask you to get a holistic view across your data. <br /> Other than regulation, there is an increased focus on “Better Risk Management” How do you ensure that you have good risk practices and controls in place. <br />
  • Each regulation requires at-least 5 changes in your architecture. Time to deliver this is 6 months!! <br />
  • Start applying for credit card, call into call center, have to wait in the queue and then start all over with agent, they ask about a balance transfer (that have never used). It turns out I was walking by a branch and scanned a QR code for a mortgage offer last week, and that would be a relevant offer or question to ask <br /> Browsing personal web site, treasurer for corporate account, cross-sell corporate services <br />
  • Added an operational data layer <br /> Akin to a glorified spreadsheet <br /> Used technologies that are used by CDW and ended up with Data Marts <br />

How Retail Banks Use MongoDB Presentation Transcript

  • 1. How Retail Banks use MongoDB Kunal Taneja Business Architect, Financial Services kunal.taneja@mongodb.com
  • 2. 2 • About MongoDB – The Company – The Database (MongoDB) • Challenges in Financial Services • Case Study – Single View of Customer Agenda
  • 3. 3 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 } }
  • 4. 4 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
  • 5. 5 Leading Organizations Rely on MongoDB
  • 6. 6 • About MongoDB – The Company – The Database (MongoDB) • The Community • MongoDB in Financial Services • Case Study – MongoDB as a Tick Store Agenda
  • 7. 7 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.
  • 8. 8 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
  • 9. 9 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
  • 10. 10 Document Model EmpID Name Dept Title Manage Payband Benefits 9950 Dunham, Justin Marketing Product Manager 6531 C Health PPO Plus Dental Standard
  • 11. 11 MongoDB - Agility Dynamic Schemas V 1.0 V 1.1 V 2.0 EmpID Name Dept Title Manager Payband Benefits 9950 Dunham, Justin Marketing Product Manager 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
  • 12. 12 Shell Command-line shell for interacting directly with database MongoDB - Usability Drivers Drivers 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
  • 13. 13 “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
  • 14. 14 MongoDB - Scalability • High Availability • Auto Sharding • Enterprise Monitoring • Grid file storage
  • 15. 15 • About MongoDB – The Company – The Database (MongoDB) • Challenges in Financial Services • Case Study – Single View of Customer Agenda
  • 16. 16 FS/Banking Challenges 1. 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 GuidelinesFATCA PD EMIR SWAPS Push Out – Dodd Frank Securities Law Directive (SLD) Volker Rule – Dodd Frank Short Selling Close Out Netting Crisis Management Recovery & Resolution
  • 17. 17 Source LayerSource Layer BI Abstraction & Reporting Layer BI 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 & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Normalisation & Storage FS/Banking Challenges 1. Changing Regulatory Requirements
  • 18. 18 Primary:NYC Secondary:NYC Primary:LON Primary:SYD Secondary:LON Secondary:NYC Secondary:SYD Secondary:LON Secondary:SYD FS/Banking Challenges 2. Latency and Global synchronisation of information
  • 19. 19 FS/Banking Challenges 2. Latency and Global synchronisation of information Source LayerSource Layer BI Abstraction & Reporting Layer BI Abstraction & Reporting Layer Acquisition LayerAcquisition Layer Extraction & Staging Cleansing Atomic LayerAtomic Layer Ad-hoc reports & Analytics Dashboards & Web Reports Web Services Corporate Data WarehouseCorporate Data Warehouse Data Lineage and Metadata ETL Transformation & Access Layer Transformation & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Normalisation & Storage 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
  • 20. 20 Web Call Center 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 Com puter Call Center Mobile ATM Branch EO D EO D EO D EO D Branch Com puter Call Center … FS/Banking Challenges 3. Multi-channel and 360 View of Customer
  • 21. 21 Source LayerSource Layer BI Abstraction & Reporting Layer BI 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 & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Normalisation & Storage FS/Banking Challenges 3. Multi-channel and 360 View of Customer LoansLoans Credit CardCredit Card PaymentsPayments Loans meets Card Card meets Payments …. Loans meets Card Card meets Payments ….
  • 22. 22 Approaches tried in the past Siloed Data Marts Source Layer Source Layer BI Abstraction & Reporting Layer BI Abstraction & Reporting Layer Acquisition LayerAcquisition Layer Extraction & Staging Cleansing Atomic LayerAtomic Layer Ad-hoc reports & Analytics Dashboards & Web Reports Web Services Corporate Data WarehouseCorporate Data Warehouse Data Lineage and Metadata ETL Transformation & Access Layer Transformation & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Normalisation & Storage
  • 23. 23 Polymorphic Operational Data Store Source Layer Source Layer BI Abstraction & Reporting Layer BI Abstraction & Reporting Layer Acquisition LayerAcquisition Layer Extraction & Staging Cleansing Atomic LayerAtomic Layer Ad-hoc reports & Analytics Dashboards & Web Reports Web Services Corporate Data WarehouseCorporate Data Warehouse Data Lineage and Metadata ETL Transformation & Access Layer Transformation & Access Layer Transformation & Calculation Performance & Access Change Data ! Reject Data Normalisation & Storage
  • 24. 24 Where MongoDB is being used Business Domain Solution Areas to Consider Customer Engagement Single View of a Customer Customer Experience Management Responsive Digital Banking Gamification of Consumer Applications Agile Next-generation Digital/Social/Mobile Platform Marketing Multi-channel Customer Activity Capture Real-time Cross-channel Next Best Offer Location-based Offers Risk Analysis & Reporting Firm-wide Liquidity Risk Analysis Transaction Reporting and Analysis Regulatory Compliance Flexible Cross-silo Reporting: - Basel III, Dodd-Frank, etc. Online Long-term Audit Trail Aggregate Know Your Customer (KYC) Repository Reference Data Management [Global] Reference Data Distribution Hub Payments Corporate Transaction Reporting Fraud Detection Anti-Money Laundering
  • 25. 25 • About MongoDB – The Company – The Database (MongoDB) • Challenges in Financial Services • Case Study – Single View of Customer Agenda
  • 26. Retail Banking How MongoDB and Infusion are helping Banks in the Digital era Andy Ross (Infusion) and Kunal Taneja (MongoDB) 5 August 2014
  • 27. Overview of InfusionWe help leading companies navigate the digital era
  • 28. 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
  • 29. How we help clients Data & Analytics Mobilit y Digitalized Customer Experience Web Digital Strateg y Innovation Design Agency Enterprise software development Managed services
  • 30. A history of success with the largest customers
  • 31. Trends in Retail Banking Drivers and themes
  • 32. Drivers of change Regulation Customer v product focus Return of the branch New entrants New technology
  • 33. Digital themes Mobility Deliver faster and for less Personalis ation Big data Customer experience The challenge: how does a big company act like a start-up?
  • 34. The MetLife story
  • 35. One day we got an email from our friend Gary… New MetLife CIO with a (30/60/90) plan Get a slow moving company to move fast Attract talented technologists Solve long-standing and difficult business problems Business context
  • 36. One day we got an email from our friend Gary… Asked if we could build an application that would produce a 360° view of his new customers using innovative technology and have a Facebook style interface? Business challenge 70 different systems 140yrs customer data 45million policies 100million transactions
  • 37. Our approach: GO FAST! 9 0 days 2 weeks Sold the idea Delivered the application
  • 38. Our approach: act like a start-up Get software built and optimize
  • 39. The Wall unmasked User-centric design
  • 40. Dramatic productivity enhancements
  • 41. Gone viral Changing how they “do projects” Is something the organization can, and is, rallying around and aligning to The Wall lives Additional benefits
  • 42. Why were we successful? Strong champion Modern technology Enterprise ready Incubation Behaved like a start- up
  • 43. You can do things differently! You can push harder than you think You need a strong champion Sell the idea Lessons learned
  • 44. Questions? Stay tuned after the webinar and take our survey for your chance to win MongoDB swag.