Webinar: Achieving Customer Centricity and High Margins in Financial Services with MongoDB
 

Webinar: Achieving Customer Centricity and High Margins in Financial Services with MongoDB

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It is imperative that Financial Services firms align the organization around providing maximum value to customers across all channels and products with the agility to capitalize on new opportunities. ...

It is imperative that Financial Services firms align the organization around providing maximum value to customers across all channels and products with the agility to capitalize on new opportunities. They must do this at the same time as cutting costs, improving operational efficiency, and complying with current and future regulations. This effort is commonly referred to as Industrialization, or streamlining people, process, and technology for maximum customer value, service, and efficiency.

MongoDB can help you in this initiative by allowing you to centralize data management no matter how it is structured across channels and products and make it easy to aggregate data from multiple systems, while lowering TCO and delivering applications faster. MetLife publicly announced that they used MongoDB to enable a single view of the customer in 3 months across 70+ existing systems. We will explore case studies demonstrating these capabilities to help you industrialize your firm.

Key takeaways:

Unique capabilities, brought to you by MongoDB
Concrete use cases that help industrialization
Implementation case studies, to pave the way

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  • 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 askBrowsing personal web site, treasurer for corporate account, cross-sell corporate services
  • 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
  • Start applying for credit card, call into call center, have to wait in the queue and then start all over with agent, ask about a balance transfer (that have never used), was also browsing mortgages last week, and that would be a relevant offer or question to ask
  • 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
  • Point out what other NoSQL databases have (not rich querying and strong consistency)
  • Single view of a customer
  • Can store all accounts in one tableHave performance capacity and easy scaling to to do real-time, not just batch
  • Good for regulatory reporting, e.g. KYC
  • Dynamic schema again importantAuto-sharding allow infinite capacity on commodity hardware
  • Compared to distributed cache - $ and fixed schema
  • Then 10x better performance50% less dev time

Webinar: Achieving Customer Centricity and High Margins in Financial Services with MongoDB Webinar: Achieving Customer Centricity and High Margins in Financial Services with MongoDB Presentation Transcript

  • Achieving Customer Centricity and High Margins in Financial Services with MongoDB Matt Kalan Business Architect, Financial Services matt.kalan@mongodb.com @matthewkalan
  • FS Industry transformation Drivers of change Requirements • Lost revenue (fees, prop trading) • New products and new markets • New competitors • Increase wallet share • Better risk management • Cost savings • Emerging markets opportunities • Agility to respond to competitors & regulators • Regulatory change and uncertainty • Cross-channel and global integration • Proliferation of channels • • Globally distributed operations Intraday decision support • Operational efficiencies • Faster market movements 2
  • Challenges with Current Structure Customers Web Impact • Similar processes and systems duplicated Central Functions • Risk • Compliance • Legal • … • Changes done in multiple places • Siloed view of customer • Siloed experience by customer • Cross-channel/silo data is previous day 3 Cards … … Deposit Accounts Call Center Loans View slide
  • Current sample user scenario • Start to apply for credit card on web • Have a question; look up customer service number • Call number, wait in queue, it’s the wrong call center, re-route to correct one • Wait in another queue, get to agent • Agent answers question and starts all over in applying for credit card • Ask me about a balance transfer for the 100th time 4 View slide
  • Industrialization: customer centricity while optimizing people, process, & tech Customers Solution: Reuse processes and technology services across product Mobile Real-time cross-channel & product decision support Loans Cards Right action at the right time in right channel Benefits • Increased wallet share • Operational efficiencies • … Higher customer satisfaction • Shared services • Single View of Customer • Cross-sell • Onboarding • Customer Service • Fraud detection • Intraday Risk • Compliance Faster product rollout … Computer 5 ATM
  • Future user scenario • Start to apply for credit card on web/mobile • Chat is available but I have time so call agent • Automated line knows my history • Offers to route me directly to agent • Agent continues where I left off • Ask me about a mortgage for which I scanned a QR code today walking by a branch 6
  • Obstacles for Enabling Industrialization Today 1. Aggregation of disparate data is difficult 2. Legacy systems often not real-time enabled 3. Master data can be hard to change and distribute 4. Operational applications are siloed 5. Application development is not agile 7
  • Difficult because RDBMSs not supporting modern requirements Data Types & OOP Agile Development • Unstructured data • Iterative • Semi-structured data • Short development cycles • Polymorphic data • New workloads Volume of Data New Architectures • Petabytes of data • Horizontal scaling • Trillions of records • Commodity servers • Millions of queries per second 8 • Cloud computing
  • What if the database enabled agility more than limited it? • Dynamic and variable schemas • Richly-structured data • Easy horizontal scaling • Low TCO • Plus still maintaining old capabilities – Rich querying – Strongly consistently data 9
  • Richly structured and dynamic schema Relational Document Data Structure { } 10 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 : ”IRA”, beneficiaries: […] }]
  • MongoDB capabilities 1. Dynamic Document Schema Application 2. Native language drivers db.customer.insert({…}) db.customer.find({ name: ”John Smith”}) { name: “John Smith”, date: “2013-08-01”), address: “10 3rd St.”, phone: [ { home: 1234567890}, { mobile: 1234568138} ] } Driver Mongos 3. High availability Shard 2 Shard N Primary Primary Primary Secondary Secondary Secondary - Replica sets Shard 1 Secondary … 5. Horizontal scalability 11 - Sharding Secondary Secondary 4. High performance - Data locality - Indexes - RAM
  • Current Challenges and Case Studies of Solutions
  • Challenge: Aggregation of disparate data is difficult Batch Datamar t Batch Datamar t Customer Accounts Loans Loans Loans … Deposits Deposits Deposits 13 Batch Datamar t Batch Data Warehouse Reporting Cards Cards Cards Issues • Yesterday’s data • Details lost • Inflexible schema • Slow performance Impact • What happened today? • Worse customer satisfaction • Missed opportunities • Lost revenue
  • Solution: Using dynamic schema and easy scaling Operational Data Hub Customer Accounts CSR Application Real-time or Batch Customer Portal Loans Loans Loans … … Deposits Deposits Deposits 14 Operational Reporting Data Warehouse Strategic Reporting Cards Cards Cards Benefits • Real-time • Complete details • Agile • Higher customer retention • Increase wallet share • Proactive exception handling
  • Single View Case Study: Tier 1 Global Insurance Provider Global 360 degree view of customers’ policy portfolio and interactions Problem Why MongoDB Results • 70 systems and 20 screens to view customer policies • Dynamic schema: can combine 70 systems easily • Many CSR calls taken just to reroute customer • Performance: can handle • Unified customer view all data in one DB available to all channels • Poor customer experience • Replication: local reads and high availability • Shorter and less calls rerouted • Source systems are hard to change • Sharding: can add data easily by scaling out • Increased customer satisfaction 15 • Delivered in 3 months with $3M – previous attempts failed over 2 yrs
  • Challenge: Legacy systems often not real-time enabled or too slow Data source 1 Batch copy Application 1 Often not ready to expose as enterprise services • Mainframe • Core systems • Data Warehouses • Not scalable system Application 2 Data source 2 … Slow request/response 16 Application 3 … Data source N Batch copying of data many times or requests are too slow Application X Changing source data affects X systems Impact • Slow time to market • Resource intensive • Hard to change interfaces and modernize system
  • Solution: Virtualize legacy systems with a persistent caching service Mainfram e Batch Batch copy API Batch copy Application 1 Application 2 EDW … … Pub/sub … Core system Application 3 Application X 17 Benefits • Faster time to market • More agile in changing sources • Can modernize data sources behind virtualization • Infinite scale with low TCO
  • Case Study: Global Custodial Bank Virtualize Enterprise Data Sources Create a central data hub for accessing data across the enterprise Problem • Found numerous pointto-point copies of data • Change in one system impacts multiple groups • Response time on EDW was too slow • Wanted one central data hub for most often accessed data 18 Why MongoDB Results • Dynamic schema: can • Data accessible by batch normalize data as needed or REST layer in one place and prioritized • Customer portal response • Performance: can handle times shrunk by 90% all data in one logical DB • Shorter development times • Sharding: can add data with more accessible hub easily by scaling out • Could modernize data sources without changing apps
  • Challenge: Master data can be hard to change and distribute Batch Batch Batch Golden Copy Common issues • Hard to change schema of master data • Data copied everywhere and gets out of sync Batch Batch Batch Batch Batch Impact • Process breaks from out of sync data • Business doesn’t have data it needs • Many copies creates 19 more management
  • Solution: Persistent dynamic cache replicated globally Real-time Real-time Real-time Real-time Real-time Solution: • Load into primary with any schema • Replicate to and read from secondaries Real-time Real-time Real-time Benefits • Easy & fast change at speed of business • Easy scale out for one stop shop for data • Low TCO 20
  • Case Study: Global bank Reference Data Distribution Distribute reference data globally in real-time for fast local accessing and querying Problem • Delays up to 36 hours in distributing data by batch • Charged multiple times globally for same data • Incurring regulatory penalties from missing SLAs • Had to manage 20 distributed systems with same data 21 Why MongoDB Results • Dynamic schema: easy to • Will save about load initially & over time $40,000,000 in costs and penalties over 5 years • Auto-replication: data distributed in real-time, • Only charged once for data read locally • Data in sync globally and • Both cache and database: read locally cache always up-to-date • Capacity to move to one • Simple data modeling & global shared data service analysis: easy changes and understanding
  • Reporting Reporting Loan Transactions Loan Systems Deposit Transactions 22 … Card Systems … Cards Transactions Reporting Challenge: Siloed operational applications Deposit Systems Impact • Views are siloed • Duplicate management and data access layer • Need another layer to aggregate
  • Solution: Unified data services Loan Systems … … … … Common persistence framework Reporting Card Systems Deposit Systems 23 Benefit • Each application can still save its own data • Data is already aggregated for crosssilo reporting • One cluster and data access layer to manage
  • Case Study: Global Broker Dealer Trade Mart for all OTC Trades Distribute reference data globally in real-time for fast local accessing and querying Problem • Each application had its own persistence and audit trail • Wanted one unified framework and persistence for all trades and products • Needed to handle many variable structures across all securities 24 Why MongoDB Results • Dynamic schema: can • Fast time-to-market using save trade for all products the persistence framework in one data service • Store any structure of • Easy scaling: can easily products/trades without keep trades as long as changing a schema required with high • One consolidated trade performance store for auditing and reporting
  • Challenge 5: Application development not agile enough Requirement changes Change 25
  • Solution: Match dynamic data model to the application 26
  • 70%+ Lower TCO $1,680K Dev. and Admin Commercial RDBMS $517K Compute – Scale-Up Servers Dev. and Admin Storage – SAN Compute – Commodity HW Storage – Local Storage 27
  • Summary • FS firms are targeting industrialization to return revenue and margins to historical levels • IT needs to help this endeavor by enabling agility & customer centricity across all products & channels • We discussed 5 concrete examples to begin industrializing to drive business value today • Many of your competitors have already delivered on the value of MongoDB so you can too! 28
  • MongoDB Products and Services Subscriptions MongoDB Enterprise, Monitoring, Support, Commercial License Consulting Expert Resources for All Phases of MongoDB Implementations Training Online and In-Person for Developers and Administrators MongoDB Monitoring Service Free, Cloud-Based Service for Monitoring and Alerts MongoDB Backup Service Cloud-based service for backing up and restoring MongoDB 29
  • For More Information Resource MongoDB Downloads mongodb.com/download Free Online Training education.mongodb.com Webinars and Events mongodb.com/events White Papers mongodb.com/white-papers Case Studies mongodb.com/customers Presentations mongodb.com/presentations Documentation docs.mongodb.org Additional Info 30 Location info@mongodb.com