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FSI Roundtable - AWS FSI Personalized Baking

AWS FSI Personalized Baking - FSI Roundtable - Tatiana Orofino

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© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Tatiana Orofino
Financial Services Business Development
Make banking more personal with
AWS
Why should banks invest in personalization?
®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved.
70% of banks say customer centricity is a priority, but only 14% of customers are
satisfied with the amount of personalization they are experiencing
Customer satisfaction is notably higher (49%) among customers who are offered
personalized digital experiences versus those who are not (39%)
50% of customers would buy a product from their primary bank if it made a personalized
offer
For every $100 billion in assets a bank has, it can achieve as much as $300 million in
revenue growth by personalizing its customer interactions.
Customer expectations are changing
®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved.
Customers are willing to share personal data with their bank
Get timely, personalized offers and rewards
Receive instant account balances or fraud alerts
Ask questions and contact customer support
Check the status of a payment
Secure, access, and monitor their accounts
But in exchange they want to be able to easily…
®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved.
AWS can help you use customer data to…
Understand customer
goals and anticipate
customer needs to make
contextual offers
Improve customer
experiences with
personalized messages
to build loyalty and
reduce churn
Capture and identify
signals to deliver
customized insights
that lead to customer
intimacy
Why should you build Personalization on AWS?
Ad

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FSI Roundtable - AWS FSI Personalized Baking

  • 1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Tatiana Orofino Financial Services Business Development Make banking more personal with AWS
  • 2. Why should banks invest in personalization?
  • 3. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. 70% of banks say customer centricity is a priority, but only 14% of customers are satisfied with the amount of personalization they are experiencing Customer satisfaction is notably higher (49%) among customers who are offered personalized digital experiences versus those who are not (39%) 50% of customers would buy a product from their primary bank if it made a personalized offer For every $100 billion in assets a bank has, it can achieve as much as $300 million in revenue growth by personalizing its customer interactions. Customer expectations are changing
  • 4. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. Customers are willing to share personal data with their bank Get timely, personalized offers and rewards Receive instant account balances or fraud alerts Ask questions and contact customer support Check the status of a payment Secure, access, and monitor their accounts But in exchange they want to be able to easily…
  • 5. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. AWS can help you use customer data to… Understand customer goals and anticipate customer needs to make contextual offers Improve customer experiences with personalized messages to build loyalty and reduce churn Capture and identify signals to deliver customized insights that lead to customer intimacy
  • 6. Why should you build Personalization on AWS?
  • 7. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. What does successful customer engagement in banking look like? Customer interaction data is part of a continuous feedback loop, delivering proactive tailored interactions, generating signals, and supporting business loyalty and growth. Customer engages with bank Message customer and capture data Identify signals Factor in time, location, context CUSTOMER INSIGHTS PERSONALIZED ENGAGEMENTS Segment customer based on profile Profile customer Optimize content for customer Re-message customer
  • 8. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. A data lake is the foundation for personalization Structured data ERP CRM LOB applications Semistructured data Mobile Social Sensors POS terminals Unstructured data Phone calls Images Videos Email Streaming Amazon Kinesis Amazon S3 data lake Cloud-scale centralized architecture that enables enterprise data science Amazon S3 Batch load AWS GlueAmazon EMR Amazon MSK Personalization Storing data in an Amazon S3 data lake enables customers to directly search or query their data, leverage predictive or prescriptive analytics, and apply AI/ML to extract additional data insights. Amazon SageMaker Amazon EMR Amazon Personalize AmazonRedshift AWS Deep Learning AMIsAmazon Pinpoint
  • 9. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. Amazon Personalize is a machine learning service that makes it easy to create individualized recommendations for customers • Create high-quality recommendations that respond to specific needs • Add real-time personalization to applications • Tailor on-site search, product sorting, recommendations, and offers • Deliver personalization within days, not months • All data analyzed by Amazon Personalize is kept private and secure Amazon Personalize makes it easy to create individualized recommendations Amazon Personalize
  • 10. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. Amazon Personalize generates real-time recommendations Store customer data in an Amazon S3 data lake Amazon S3 Stream use activity from your application using the Amazon Personalize API Amazon Personalize API Amazon Personalize • Automatically process and examine the data • Identify what is meaningful • Select the right algorithms • Train and optimize a customized personalization model Load data Inspect data Identify features Select algorithms Select hyper-parameters Optimize models Build feature store Host models Create real-time caches Train models Customized Personalization API ProvidesAmazon Personalize with an activity stream to generate real-time recommendations or request recommendations in bulk
  • 11. Banks are using AWS to enhance the customer experience
  • 12. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. Emirates NBD is using AWS to build personal customer experiences Emirates NBD wanted to engage more with its customers. The bank AWS machine learning services including Amazon Personalize and Amazon SageMaker to build a personalized retail customer banking experience. Working with AWS enabled the bank to build a personal finance manager that uses an automated, self-learning system to make personal customer recommendations. Amazon Personalize Amazon SageMaker Our vision is to be the Middle East’s most innovative financial services organization and to achieve this we have chosen to work with the world’s most innovative technology company, Amazon Web Services. – Suvo Sarkar, Senior ExecutiveVP and Group Head, Retail Banking & Wealth Management, Emirates NBD “ ”
  • 13. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. A multinational European bank uses AWS to tailor notifications A leading global bank wanted to better connect its customers to financial opportunities. The bank is using Amazon Kinesis to stream millions of transaction records in real-time, and using AWS Lambda to apply business logic to that data. Working with AWS enabled the bank to provide customers with balance alerts, overdraw alerts, and single-click travel insurance options tailored to their preferences. A European G-SIB AmazonKinesis AWS Lambda
  • 14. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. National Australia Bank is using AWS to build more personal experiences National Australia Bank (NAB) built a data lake on AWS, called Data Hub, to power Discovery Cloud – a laboratory for its data scientists. NAB is adopting a cloud-first strategy using AWS compute, storage, database and analytics capabilities to build new services to deliver better financial outcomes for its customers. NAB operates a cloud-enablement training program to teach employees about AWS technologies and enable them to innovate digital experiences for its customers. NAB is also looking to Amazon Connect to provide exceptional customer service. Data is key for us.We need it to be accurate, consolidated, traceable, and accessible. NAB Data Hub is a 100% cloud native data lake built on AWS. - Yuri Misnik, CIO, NAB “ ”
  • 15. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. Capital One uses Amazon Connect to improve customer outcomes We picked AWS because they are the leader in the market. We picked them because our developers demanded it.We picked them because of the features and capabilities that support large enterprises. – Rob Alexander, CIO, Capital One “ ” Capital One wanted a faster, more personalized, and cost- effective way to answer customer calls. The bank replaced its call center with Amazon Connect, including direct banking and fraud operations. Amazon Connect allows Capital One to capture customer intent and provide a seamless, personalized customer experience and improve customer outcomes and business agility. AmazonConnect
  • 16. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. NuData is using anAWS data lake for identity authentication Mastercard purchased NuData to improve its fraud prevention techniques by using passive biometrics to authenticate account holders’ identities. NuData uses an Amazon S3 data lake to store customer data that is collected and analyzed in real time using Amazon Kinesis, Amazon Redshift, Amazon EMR, and Amazon Athena. By using a data lake on AWS, NuData is able to collect and analyze hundreds of data points, which are then used to authenticate users and protect customers from fraud. Amazon S3 Amazon Kinesis Without the tools and techniques we have available on AWS, these would be much harder problems to solve. – Robert Capps,Vice President of Business Development, NuData “ ”
  • 17. ®2019 Amazon Web Services Inc. or its Affiliates. All rights reserved. FINRA uses anAWS data lake to oversee over 3,000 securities firms FINRA needed a platform that could ingest, process, and store 36 billion market events on an average day and dynamically scale up to handle 100 billion events on a peak day. FINRA built a data lake on AWS using Amazon S3 and EMR to store and analyze data from 3,700 broker dealers and 12 exchanges. FINRA’s flexible platform can adapt to changing market dynamics while providing analysts with the tools needed to query the data set. Amazon S3 Amazon EMR We got some huge pleasant surprises out of [going all in on AWS] that we weren’t expecting at all. First of those is amazing performance improvements. On average, 400 times improvement to interactive queries.The investigative capacity to our surveillance team has expanded dramatically. – Steve Randich, CIO, FINRA “ ”
  • 18. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Worldwide | N. America | LATAM | UK/IR | EMEA | APAC | Japan | China

Editor's Notes

  1. 1, 2, Customer expectations are evolving: (Capgemini World Retail Banking Report 2018 – https://worldretailbankingreport.com/wp-content/uploads/sites/3/2018/09/World_Retail_Banking_Report_2018.pdf) Customer expectations is the most disruptive force in banking today (70.8%) Barely half of customers say their experience across different bank channels was positive (51.1 percent in branch, 46.9 percent on mobile and 51.7 percent on internet banking), Personalization is key: Satisfaction was notably higher among those customers who had been offered personalized digital experiences proactively (49.1%) vs. those who had not (39.5%) 3. 50%: Customers that would have bought a product from their primary bank if it had made a personalized offer http://www.bain.com/publications/articles/evolving-the-customer-experience-in-banking.aspx 4. For every $100 billion in assets a bank has, it can achieve as much as $300 million in revenue growth by personalizing its customer interactions. One bank has used personalization to lift branch sales productivity by more than 30%. Another institution has seen a 20% increase in revenues over three years. - BCG, What does personalization in banking really mean? https://www.bcg.com/publications/2019/what-does-personalization-banking-really-mean.aspx
  2. Globally, 67% of customers will grant banks access to more personal data, but 63% want more tailored advice, and the same number demand priority services such as expedited loan approvals, or a monetary benefit such as more competitive pricing. https://www.accenture.com/_acnmedia/accenture/next-gen-3/dandm-global-research-study/accenture-banking-global-distribution-marketing-consumer-study.pdfla=en Customer expectations are evolving: (Capgemini World Retail Banking Report 2018 – https://worldretailbankingreport.com/wp-content/uploads/sites/3/2018/09/World_Retail_Banking_Report_2018.pdf) Customer expectations is the most disruptive force in banking today (70.8%) Barely half of customers say their experience across different bank channels was positive (51.1 percent in branch, 46.9 percent on mobile and 51.7 percent on internet banking), Personalization is key: Satisfaction was notably higher among those customers who had been offered personalized digital experiences proactively (49.1%) vs. those who had not (39.5%) Positive experiences drive customer loyalty Consumers want to easily: … check a claim or transaction status … secure, access, and monitor their accounts 3. … receive instant account balances or fraud alerts 4. … get timely, personalized offers and rewards 5. … ask questions and contact customer support Traditional channels Many firms have ended up with ‘islands of engagement’ – traditional channels to engage with users that are siloed from one another and that deliver a standardized, impersonal message. Traditional channels often do not deliver timely messaging, and the messages are not contextual/personalized to the user
  3. Capture interaction data Create targeted products, service, and offers Provide personalized experiences with timely, tailored messages Reach customers via their preferred channel Capture usage data from multiple devices (Internet of Things) Fraud detection
  4. Identifying customers’ needs and providing appropriate personalization is necessary for banks to remain competitive. Without innovation, user engagement channels lead to missed opportunities. Firms face a negative cycle: Not keeping up with generational shifts and evolving consumer demands Not capturing and analyzing customer interaction data Not building user segments, identifying buying signals, flagging consumer sentiments Not shifting users to lower-cost channels So how can companies get to the ideal user engagement state? By building a positive feedback loop where customer insights are gathered, and are used to inform and send more personalized messages. As users become accustomed to more personalized, timely engagements from firms in other industries, they seek out the same experiences in Financial Services. Firms that don’t adopt digital channels, and that don’t capture data to develop robust customer profiles, risk missing out on selling opportunities, and risk losing customers to competitors.
  5. Amazon S3 data lakes enable banks to capture and analyze customer interaction data and build more personalized customer relationships A data lake is a centralized repository that allows structured and unstructured data to be stored at any scale. By capturing the various customer touchpoints and interaction data, financial institutions can use this data to create more customized products and services, provide more personalized messaging, and reach customer via their preferred communication channel. A data lake is a bet against the future – you don’t know what analysis you might want to do, so why not just keep everything to give the best chance you can satisfy any requirement that comes along? A data lake is different from a data warehouse because it stores relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media. The structure of the data or schema is not defined when data is captured. This means you can store all of your data without careful design or the need to know what questions you might need answers for in the future. Different types of analytics on your data like SQL queries, big data analytics, full text search, real-time analytics, and machine learning can be used to uncover insights. Many companies leverage a data lake to complement, rather than replace, existing Data Warehouses. A data lake can be used as a source for both structured and unstructured data, which can be easily converted into a well-defined schema for ingestion into a Data Warehouse, or analyzed ad hoc to quickly explore unknown datasets and discover new insights. With this in mind, consider the following best practices when building a data lake solution: Configure your data lake to be flexible and scalable so that you can collect and store all types of data as your company grows. Include design components that support data encryption, search, analysis and querying. Implement granular access-control policies and data security mechanisms to protect all data stored in the data lake. Provide mechanisms that enable users to quickly and easily search and retrieve relevant data, and perform new types of data analysis. Leverage managed services for multiple methods of data ingestion and analysis. For example, use Amazon Kinesis, AWS Snowball, or AWS Direct Connect to transfer large amounts of data. Then use powerful services such as Amazon EMR, AWS Data Pipeline, and Amazon Elasticsearch Service to process that data for meaningful analysis. The AWS data lake ecosystem illustrates the ability to ingest any type of data, structured, semi-structured, and unstructured data, into S3 using multiple methods. Customers can bring data into S3 using batch upload processes or real-time streaming applications, depending on how the data is collected and how quickly it needs to be processed and analyzed. Structured data: Data that are highly normalized with common schema and stored in relational databases, powering transactional line-of-business applications Semistructured data: Data that contain identifiers without conforming to a predefined schema Unstructured data: Data that do not conform to a data model and are typically stored as individual files Batch load: Extracts data from various data sources at periodic intervals and moves them to the data lake Streaming: Ingests data that are generated from multiple sources such as log files, telemetry, mobile applications, and social networks Once the data is in S3, firms can apply traditional SQL analytics by leveraging Redshift or Athena, or use Quicksight for business intelligence applications. Additionally, firms also have the option to apply more customized data transformations using EMR or leverage machine learning to extract additional insights from the data.
  6. Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications. Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the machine-learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity. Amazon Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications, using machine learning technology perfected from years of use on Amazon.com. With Amazon Personalize, you provide an activity stream from your application – clicks, page views, signups, purchases, and so forth – as well as an inventory of the items you want to recommend, such as articles, products, videos, or music. You can also choose to provide Amazon Personalize with additional demographic information from your users such as age, or geographic location. Amazon Personalize will process and examine the data, identify what is meaningful, select the right algorithms, and train and optimize a personalization model that is customized for your data. All data analyzed by Amazon Personalize is kept private and secure, and only used for your customized recommendations. You can start serving personalized recommendations via a simple API call. You pay only for what you use, and there are no minimum fees and no upfront commitments. Amazon Personalize is like having your own Amazon.com machine learning personalization team at your disposal, 24 hours a day.
  7. Personalized recommendations Personalized search Personalized notifications
  8. Innovate faster: Having faster access to data enables the discovery of new business opportunities and creation of new products. Realize cost efficiencies: Storing data in the cloud allows firms to retain data at a much lower cost than on-prem.
  9. https://www.businesswire.com/news/home/20190528005712/en/Emirates-NBD-Building-Artificial-Intelligence-enabled-Bank-Future
  10. The bank chose to use Amazon Kinesis to manage its data streams and Amazon Lambda to analyze the data like takes data from its on-premises mainframe, manage the streams with Kinesis, and uses Lambda to use analytics and machine learning to process the data What customers want to hear from banks: You want to know what your balance is You want to know when your balance is running low. Get an alert to manage money. Get travelers insurance when you land in a foreign country with a single click.
  11. https://aws.amazon.com/summits/sydney/on-demand/keynote/ (1:04:30) Patrick Wright Australia’s largest business bank Our customers want great, connected, in the moment connected experiences. NAB is leveraging the AWS cloud and adopting microservices to embrace data create personalized services. NAB Discovery Cloud: A service for its data analysts and data scientists intended to replace aging data warehouses. It uses AWS’s S3 storage service, Amazon Elastic MapReduce and the cloud provider’s data-warehouse-as-a-service offering, Redshift.  It will be the foundational element that will feed our machine learning ecosystem, it will be the foundational element that will feed our analytics ecosystem, it will be the foundational element that feeds our marketing ecosystem.
  12. Capital One had three requirements in mind when it reviewed its contact center options: Aspirations to have a common cloud environment Operational efficiencies Enhanced experiences/innovative customer engagement Capital One explains why they switched to Amazon Connect at re:Invent 2017: https://youtu.be/rSzDFJGw5vg?t=18m32s Use case: All-in Geo: NAMER (US) Case study: https://aws.amazon.com/solutions/case-studies/innovators/capital-one/ Other resources: https://partners.wsj.com/aws/capital-one-rethinking-fraud-protection-machine-learning/
  13. https://partners.wsj.com/aws/how-mastercards-nudata-keeps-identities-protected-with-aws/
  14. The Financial Industry Regulatory Authority (FINRA) is a private sector regulator responsible for analyzing 99% of the equities and 65% of the option activity in the US. In order to look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust set of big data tools in the AWS Cloud to support these activities. One particular application requires low-latency retrieval of items from a data set that contains trillions of records. FINRA analysts use this application to investigate particular sets of related trade activity. FINRA uses Apache HBase on Amazon EMR, and Amazon S3 as a data lake resulting in cost savings of over 60% over their on-premises solution, and drastically reducing the time needed for recovery or upgrades. VIDEO: https://youtu.be/rHUQQzYoRtE