This document discusses how predictive analytics can drive success in fintech and banking. It summarizes how Bizfi, an online lending marketplace, has deployed predictive analytics to optimize its sales process, marketing efforts, and lending process. Key lessons discussed include using predictive analytics to qualify the best prospects early, intelligently route customers to the best employee, and identify customer lifecycles. Future opportunities discussed include leveraging unstructured data and creating a centralized data platform.

1. © 2016 Beyond the Arc, Inc.
How Predictive Analytics Can Drive
Success in Fintech and Banking
2017
Steven Ramirez
CEO
Beyond the Arc

2. What we’ll cover today
• Is Predictive Analytics relevant in fintech and
banking?
• How has Bizfi deployed predictive analytics?
• What are the lessons learned and key takeaways?
• Where do you go next?
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3. About Bizfi
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Bizfi is a fully automated online marketplace designed to give
multiple financing options to applicants. What sets us apart is
our proprietary platform technology which allows business
owners to get actual results for multiple product types and the
ability to go directly to contract. Every step of the way, business
owners are guided by an experienced funding concierge.

4. Should financial
services be investing
in predictive
analytics?
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5. Select the correct analytical approach to find
meaning in your data
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Predict
Propensity
Estimated value
Forecasting
Classify
Profile customers
by interests
Segment
Identify highly
similar prospects
Associate
Identify behaviors
commonly
occurring together
Sequence
Track common
order of events
SolutionsProblems

6. Predictive analytics at Bizfi
• Optimizing the sales process
◦ Lead scoring (likely to convert)
◦ Value of leads from various sources
• Increasing the cost-effectiveness of marketing
◦ Marketing analytics
• Making the lending process more efficient
◦ Qualify the best prospects, at the earliest stage
◦ Use intelligent routing to match prospects with the employee
most likely to close the transaction
• Take underwriting to the next level
◦ Credit side/lending/balance sheet
• Identify customer lifecycle
◦ New deals vs renewals
• Collections and portfolio
◦ Reduce risk of default, collect more from past due accounts
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7. Why Customer Journey Analytics?
The Setting
• Customers have always had experiences
• Businesses have always had operations and analytics
What’s New
• Detailed personal data is more widely available
• Businesses can create Experience platforms
that tie together the whole journey
• Each customer’s individual pathway can now be
addressed
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8. Big Data Makes It Personal
• As the data gets bigger,
it contains a higher
volume of more specific
customer interactions
◦ General advertising
◦ Segmented marketing
◦ Personalized contact
◦ Individually targeted and
custom messaged
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9. A Long Journey… Taken One Step At A
Time
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• Create demand
• Acquire customers
• Onboarding of Customers
• Routine touchpoints
• Sales
• Cross-Sell
• Customer Lifetime Value
• Customer Service
• Customer Satisfaction
• Customer Loyalty
• KPI Dashboard
• Employee satisfaction
• Operational costs
• Attrition prevention
• Win-Back
Customer
point of view
Business
point of view

10. Analytics Make Each Touchpoint Better
• Prioritize – Rank likelihood, rank value
• Describe – Make segments
• Evaluate – Test everything for success in the field
• Monitor – Watch for changes over time
• Choose the best, get rid of the rest – Make decisions
based on the analytics
• Iterate – gather more data and improve over time
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11. Considerations for startups
• Managing operations is critical, and measuring the
efficiency of each phase
◦ Where it matters most: underwriting
• Grow fast, but manage risk
◦ Customer acquisition vs cost vs fraud
• Drive down Cost of Capital
• Portfolio management (collections and repayment)
◦ Business process for collections,
◦ Identify segments, create messaging for each payment
“persona”
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12. Future opportunities
• Unstructured data
• Customer profiling
• Entity analytics
• Creating a data platform to capture disparate
sources
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13. 13
A little about Beyond the Arc
Highlights and credentials
• Recognized by Forrester Research for our
work in analytics and CX transformation
• Featured speaker on analytics best
practices, Predictive Analytics World ‘16
• Guest Lecturer, Haas School of Business,
UC Berkeley 2016
• Featured speaker, BAI Retail Delivery
Annual Conference 2012-2016
• Faculty Fellow, Pacific Coast Banking
School, August 2012
• Featured speaker on social media data
mining, American Bankers Association
2013
Recent press
• Financial Brand, 2014,2015,2016,2017
• Fortune, June 2014
• Fox Business, January 2014
• Loyalty Magazine, Summer 2012
• Destination CRM, May 2012
• American Banker/Bank Technology
News, Jan 2012
• BAI – Retail Banking Strategies, Jan
2012
• ABA Banking Journal, Jan 2012
AnalyticsStrategy Insights Action

14. Disclaimer and copyright
©2017, Beyond the Arc, Inc. All rights reserved
This document provides our commentary and analysis. The information contained
herein is of a general nature and is not intended to address the circumstances of any
particular individual or entity.
Although we endeavor to provide accurate and timely information, there can be no
guarantee that such information is accurate as of the date it is received or that it will
continue to be accurate in the future. No one should act on such information without
appropriate professional advice after a thorough examination of the particular
situation. No warranty expressed or implied.
The information provided in this presentation is not intended nor should be used as a
substitute for legal advice or other expert opinions and services in specific situations.
This material may not be distributed.
Other company, product, and service names may be trademarks or service marks of
others.
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15. Thank you
Steven J. Ramirez, CEO
Beyond the Arc, Inc.
Office 1.877.676.3743
Email sramirez@beyondthearc.com
Digital beyondthearc.com
@beyondthearc
Facebook.com/beyondthearc
Slideshare.net/beyondthearc
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