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Big Data: Banking Industry Use Case
- 1. Group Names Sruthi Naveen Ramandeep Kaur Bagri Big Data In Banking Industry
- 2. Case Study Problems Highlights ● Maximize the number of active credit card customers ● Isolate the cards that would likely never be activated to reduce wasted marketing spend. ● Get 360 degree view of the customers ● Reduce loss of customers to their competitors
- 3. Key Trends - Credit Card ● As per the latest trend, there is a 21% decline in card applications. ● Direct-mail response rates to credit card offers have been declining. ● 44% of credit-card applications are submitted over mobile devices (including smartphones and tablets). ● The percentage of consumers receiving a credit card offer rose from 54% to 67% over nine months last year, suggesting that card marketers are making efforts to either reach new demographics or under-served segments. ● Consumers’ traffic increased in usage of third-party aggregator websites like Credit Karma, The Points Guy ● The new Ultra FICO score, due to pilot early in 2019, allows consumers to add checking, savings and other information to potentially boost their existing FICO score. People who previously would have been locked out of having a credit card because of a lower FICO score might now be a target audience
- 4. Key Trends
- 5. Key Trends-Big Data ● According to Microsoft, over 60% of banks in North America say that Big Data would offer them an advantage, but only 37% have experience with live big data technology. ● Big Data and Business Analytics Revenue Worldwide across Industries-2018
- 6. Approach ● Requirement Analysis- Platform selection- Technical Architecture- Solution Design and Development- Testing-Deployment ● Social Media Analytics to understand /gain insight to customer sentiments and customer behavior leading to personalized offerings, identify new segments ● Click Stream Analytics on Credit card page ● Customer transaction analysis- Customer spending pattern, investment choices, etc. This with their social data can help to understand the needs, desires and preferences of the customer and to come up with a detailed customer profile
- 7. Proposed Solution ● Recommendation Engine-Right products and services to the right customers at the right time. ● Targeted ads based on the individual needs of the customer/for a segment ● Efficient Chatbots reducing the call center traffic and enhanced customer experience ● Fraud Detection ● Personalization-Customized Products/Offerings/Rewards ● Innovative reward strategies to attract the customers. ● Educate customers –Intuitive short videos
- 8. Proposed Solution ArchitectureDataSource • Customer account data • Transaction Data • Click stream Logs/ Social Media DataAccess • Sqoop • Kafka • Flume DataStorage&Processing • Storm(Real Time Processing) • Spark ML • HDFS(Stor age)-(Batch processing) Dashboard(Re al Time) SiLK (Search Analytics) Tableau BI Tools (Analytics graphs) Couch Base Apache Solr (Search Platform ) Pig Hive ClientAccess Analytics Decide/Solution Fraud Alerts ChatBot Recommen dation Engine 360 degree Customer Profile
- 9. Recommended Tech stacks Data Layer - HDFS Data Ingestion Layer- Flume, Sqoop, Kafka Data Processing Layer – Spark ML, Storm, Pig, Hive, Solr, Couchbase Operations and Scheduling layer – Oozie, Zookeeper Data Presentation Layer (Analysis) –BI Tools, Tableau, SiLK
- 10. Deployment strategy ● Org restructure- --Independent vertical silos to more horizontal structure ● Identify the stakeholders ● Line of Business-Credit Card LOB ● Compliance Team(Data Privacy Rules in the countries) ● Analytics and Engineering Team ● Educate the team ● Choose the deployment Model-IaaS/BDaaS(Big Data Stack ).Providers include Amazon EMR, Microsoft Azure,Google Cloud Platform ● Pilot a project ● Follow an Agile Methodology for Project Implementation ● Analyze the results, work on the learnings and feedback and execute more projects.
- 11. Solution Design Mock Up AI Chatbot Personalized Offerings-Real Time AI Chatbot – Initiate with each visitor on website and leading them to 3-minute video for their education, learning , understanding and motivating to take step for applying to credit card. Collecting Information from Customer profile and highlighting personalized offers. Example: Summer (Customer, 23 age)-collecting her social engagements from Twitter/FB analytics and giving her following offers with rewards and gifts; Wedding personal loan, home loan, kids education loan, Retirement gift
- 12. Solution design mock up Visualization:Public Sentiment on Social media-world wide
- 13. Social Mentions for each Dimensions
- 14. Proposed Solution Benefits ● Better Recommended products and services to customers ● More personalization/targeted rewards programs/Pushing real time offers- Happy customers Expand the customer base/identify new consumer segments by social listening . i.e. Customer Acquisition ● Customer Retention ● Enhanced customer experience ● Increase cross selling opportunities ● Improved risk management by identification of potential defaulters ● Helps in identifying unique value proposition(variation in Annual fees and reward structures) to keep the customers using the card and engaged ● Helps in Understanding public sentiment towards the brand ● Using Predictive analytics- knowing not only what consumers want now , but what they are likely to want in the future and make offers available for them.
- 15. Proposed Solution Benefits ● Campaign success/failure- Faster feedbacks Based on sentiments from customer and hence quicker corrections/changes possible without much harm to the brand ● Helps prevent fraud: Based on 360 degree customer profile and customer behavior pattern helps in preventing/reducing losses due to fraud ● Reduced call center costs based on the intelligent chatbot ● According to Oracle, providing customers with what they need can bring in 18% increase in annual revenue. ● McKinsey finds using data to make better decisions can save marketing spend by 15 to 20%. Banks on an avg spend 8% of their overall budget in marketing. This can be reduced and generate additional revenue by targeted marketing strategies
- 16. Summary ● Banking industry can definitely benefit from Big data. They are a storehouse of data and the information that can be gained from it is tremendous. Big Data Analytics helps to get a 360 degree view of customer. This in turn aids in building a better relationship and engagement with the customer leading to a win situation for both sides. ● Banks have started realizing the value of Big Data Analytics and in the coming years, will definitely see the wave of change- Personalization, Intelligent Chatbots, Automatic Fraud Detection, Robotic Process Automation, all helps in increasing revenue, reducing costs , reduce cycle time and enhanced customer satisfaction.
- 17. THANK YOU !