Pi cube banking on predictive analytics151

Mar 15, 20162 likes471 views

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.

Predictive Analytics for
banking
www.pi-cube.com

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

www.pi-cube.comData & Analytics
Bank Data
Information
Knowledge
Wisdom
Business
Intelligence
Predictive
Analytics
Prescriptive
Analytics
3

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

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

www.pi-cube.com
SMART banking
Data-driven
Risk-averse
Regulation-
compliant
Customer-aware
6

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

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

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)

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The document discusses analytics with big data, describing how businesses are using analytics to gain insights from large datasets. It provides examples of common business questions and the types of analytics that can help answer them, such as forecasting, recommendations, and predictive modeling. The document also introduces Robust Designs, a software company that specializes in business intelligence solutions using their CUBOT product.

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Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.

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1) Advanced analytics uses predictive, proactive, and forecasting capabilities to gain insights from large amounts of structured and unstructured data from various sources. 2) By 2014, 30% of analytic applications will use advanced analytic techniques and the global market for analytics software is expected to reach $34 billion. 3) Enablers of advanced analytics include in-memory databases, data mining, real-time data warehouses, and analytics-as-a-service to process large volumes of data and provide faster results.

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This document provides an introduction to predictive analytics. It defines analytics and predictive analytics, comparing their purposes and differences. Analytics uses past data to understand trends while predictive analytics anticipates the future. Business intelligence involves using data to support decision making and aims to provide historical, current and predictive views of business. As technologies advanced, business intelligence evolved from being organized under IT to potentially being aligned under strategy management. Effective communication between business and analytics professionals is important for organizations to benefit from predictive analytics. The business case for predictive analytics includes enabling strategic planning, competitive analysis, and improving business processes to work smarter.

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This document outlines an agenda for an IBM SPSS Data Mining Workshop. It includes introductions, an overview of predictive analytics and data mining, exercises demonstrating the use of IBM SPSS Modeler software, and a discussion of data mining methodology and applications. The objectives are to introduce predictive analytics and data mining, demonstrate the ease of use of IBM SPSS Modeler, and provide hands-on experience applying data mining techniques and models.

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This document discusses big data and analytics and its value proposition. It outlines the 4 V's of big data - volume, variety, velocity and veracity. It then discusses how big data and analytics can be leveraged across different industries to drive key business imperatives like acquisition, personalization, profitability and retention. It also outlines 5 key analytical use cases and 5 key operational use cases for big data and analytics. Finally, it emphasizes the need to be proactive about privacy, security, governance and building a culture that infuses analytics.

What we do; predictive and prescriptive analyticsWeibull AS

Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision. Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it. Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.

Predictive Analysis PowerPoint Presentation SlidesSlideTeam

Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.

Why is Data Science still not a mainstream in corporations - Sasa RadovanovicInstitute of Contemporary Sciences

The main task of this talk is to see how Data Science can influence big companies to generate new revenue and more profit. Subjects that will be addressed in this talk are: • Understanding a value it brings to corporations on long-term (direct revenue generation not only cost reduction); • Data Science is important part of digital transformation. Are corporations ready? • Management dedication on investment; • Lack of Data Science managers acting as a link between Data Scientists and Business managers. Provide motivation/interesting tasks for Data Scientists while validating investments in business environment; • Lack of skillful Data scientists; • Compensation of Data Scientists among other Employees (obviously a different scales needs to be applied); • Examples of Applied Data Science as revenue generators in Telenor Serbia;

Role of business analytics in the banking industryVaisakh Nambiar

Banking analytics can help banks improve customer segmentation, acquisition, and retention. It also enhances risk management, customer understanding, and fraud prevention. Examples show how analytics helped a bank reduce customer churn by 15% through targeted campaigns, increase bank revenues by 8% by correcting unnecessary discounts, and increase products per customer three times over through microsegmentation. In conclusion, analytics provides banks marketing advantages and helps optimize risk, compliance, and decision-making.

Predictive analytics and new CRM systems Valgen, Inc.

1. CRM systems help organizations overcome technology hurdles, improve collaboration, and elevate customer relationships. Predictive analytics can make CRM systems even more valuable. 2. When implementing a CRM system, organizations should consider a multi-stage deployment where basic functions are delivered first before adding predictive analytics to identify opportunities and capitalize on customer interactions. 3. To maximize value, organizations should retain focus on business objectives, implement analytics-based decision processes that integrate data, provide action recommendations, and give performance feedback.

Marketing analytics for the Banking IndustrySashindar Rajasekaran

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1030 track2 kompRising Media, Inc.

SmarterHQ is a leading multi-channel behavioral marketing platform that uses machine learning models to personalize customer interactions for large B2C brands in real-time. It builds models using data from various digital and retail sources and entities to make product recommendations, predict future customer behavior, and optimize marketing campaigns across channels like website, mobile, email, and third-party. A key client sees over 50 million transactions daily, worth $850 million in sales, and SmarterHQ helps target the most valuable repeat customers.

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Pi cube banking on predictive analytics151

1. Predictive Analytics for banking www.pi-cube.com

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

6. www.pi-cube.com SMART banking Data-driven Risk-averse Regulation- compliant Customer-aware 6

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)

11. www.pi-cube.com 11

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