Pi cube banking on predictive analytics151
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Predictive analytics can help banks in several key areas: 1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition. 2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk. 3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
Pi cube banking on predictive analytics151
- 2. www.pi-cube.com Banking trends: 2016 Customer experience (Sales + service) Effective use of analytics Decisions driven/supported by data Digital channels expand and thrive Market Consolidation (M&A) 2
- 3. www.pi-cube.comData & Analytics Bank Data Information Knowledge Wisdom Business Intelligence Predictive Analytics Prescriptive Analytics 3
- 4. www.pi-cube.com 4 Business Intelligence Predictive Analytics Orientation Rearview Future Types of questions What happened? When, who, how many? What will happen? What will happen if we change this one thing? What next? Methods Reporting (KPI’s, metrics) Automated monitoring/alerting Dashboards & Scorecards Ad-hoc queries Predictive modeling Statistical analysis Data/text/multimedia mining Simulation/optimization Data types Structured Structured/unstructured Knowledge creation Manual Automated Business value Reactive Proactive
- 5. www.pi-cube.com SMART Banking with Predictive Analytics Driven by YOUR data Surfacing quantified insights Enabling YOUR informed decisions Bring in the future with Predictive Analytics POWER-UP your lending business 5
- 7. www.pi-cube.com SMART Banking (web/mobile enabled) COMPLIANCE RISK REVENUES • Identify customer personas • Find new customers • Estimate customer lifetime value • Maximize customer wallet share • Reduce customer attrition • Manage credit risk • Optimize lending policy • Re-structure loans proactively • Assess default-risk • Manage cash reserves • Predict delinquency rate • Enhance loan underwriting • Improve loan applicant selection Proactive customer awareness Proactive risk-aversion Proactive regulatory compliance DATA 7
- 8. www.pi-cube.com 8 Know your customer’s needs. Ahead of time. • Customize your marketing to the individual customer • Make an offer at the point of need • Increase sales • Create custom offers for your best customers • Increase customer loyalty • Reduce customer attrition
- 9. www.pi-cube.com 9 Protect your loan assets. Proactively • Manage your loan portfolio proactively • Identify future problem loans • Re-structure potential delinquent loans • Make better loans to your best customers
- 10. www.pi-cube.com Predictive Analytics Life Cycle Predictive Analytics RoadMap (PARM) Business Understanding Identify use case objectives Data Exploration & Prep Collect, review, select & cleanse sample data Model build & validate manipulate data & draw conclusions Implement & Deploy Integrated web application & dashboard Identify points of contact Review & prioritize business objectives Map business objectives to use cases Joint SOW (Develop, review, signoff ) Use case 1 Use case 2 Use case 3 Use case … 10 Predictive Analytics Roadmap Identify problem (use case) Collect sample data Build & present Predictive Model(s) Pilot Phase (proof-of-concept) Maintain Model optimization Predictive Analytics Framework (architecture, process, connectors)