Pinar Donmez - Kabbage at the Chief Analytics Officer Forum, West Coast
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Transcript of Pinar Donmez - Kabbage at the Chief Analytics Officer Forum, West Coast
The Impact of Big Data and non-traditional data sources in online
lending Chief Analytics Officer Forum West Coast
May 26, 2016 San Francisco, CA
By Pinar Donmez
Chief Data Scientist at Kabbage, Inc.
Agenda
Purpose and Objectives
Turning Big Data into Big Value
Value of Big Data in Understanding Customers
Kabbage Data Centric Approach to Lending
Use of Big Data Analytics to Manage Future Customers
Q & A
Goal:
i ) Deliver a message explaining the value and importance of big data technologies with supportive examples
ii) Discuss how big data is leveraged at Kabbage
Agenda
Purpose and Objectives
Turning Big Data into Big Value
Value of Big Data in Understanding Customers
Kabbage Data Centric Approach to Lending
Use of Big Data Analytics to Manage Future Customers
Q & A
Turning big data into actionable insights will be the key innovative drive for the foreseeable future
Big data spending will grow to $50billion by 2019, with 23% annual growth rate
Turning big data into actionable insights will be the key innovative drive for the foreseeable future
Big data spending will grow to $50billion by 2019, with 23% annual growth rate
Exponential data growth
• 90% of the world’s data was created in past 2 years
• By 2020
• 10 times more mobile data
• 19 time more unstructured data
• 50 times more product data
Turning big data into actionable insights will be the key innovative drive for the foreseeable future
Big data spending will grow to $50billion by 2019, with 23% annual growth rate
Exponential data growth
• 90% of the world’s data was created in past 2 years
• By 2020
• 10 times more mobile data
• 19 time more unstructured data
• 50 times more product data
Ability to answer ‘What’, ‘Why’, ‘When’, ‘How’ questions
In 2013, only 13% of companies had extensive predictive analytics. By 2016, 70% of most profitable companies will manage their business by real-time predictive analytics capabilities.
Turning big data into actionable insights will be the key innovative drive for the foreseeable future
Big data spending will grow to $50billion by 2019, with 23% annual growth rate
Exponential data growth
• 90% of the world’s data was created in past 2 years
• By 2020
• 10 times more mobile data
• 19 time more unstructured data
• 50 times more product data
Ability to answer ‘What’, ‘Why’, ‘When’, ‘How’ questions
Analytics of things
• IoT will produce more data than ever
• Tremendous opportunities to understand, connect, serve and learn from all possible interactions
Big Data turn into actions even when least expected
In Japan, dairy farmers used wearable and big data technologies for optimizing successful insemination
Artificial insemination success rates today are 70% with a pregnancy rate of 40%
If you successfully detect when a cow is in the heat, then pregnancy rates rises up to ~70%
Question: How can you detect when estrus begins?
Big Data turn into actions even when least expected
In Japan, dairy farmers used wearable and big data technologies for optimizing successful insemination
Artificial insemination success rates today are 70% with a pregnancy rate of 40%
If you successfully detect when a cow is in the heat, then pregnancy rates rises up to ~70%
Question: How can you detect when estrus begins?
Researchers found cows start walking around furiously when they go into heat. They monitor the steps a cow is taking. When the level spikes, the system knows estrus has begun.
Fujitsu solved this problem by Gyujo: sexiest system in the cloud
A set of smart, connected devices send alerts to the farmer when estrus begins
Agenda
Purpose and Objectives
Turning Big Data into Big Value
Value of Big Data in Understanding Customers
Kabbage Data Centric Approach to Lending
Use of Big Data Analytics to Manage Future Customers
Q & A
Netflix understands what its subscribers enjoy watching through big data analysis
Netflix captures
what they watch
when they watch
where they watch
what they search
what rating they give
They also track
plot, genre, character development on movies/shows
color, tone, scenery, other visuals, volume levels
geo-location
where they pause, fast-forward, rewind
The use of big data at Netflix results in hit shows with original content that hooks us all, bringing millions more new subscriptions and revenue sources to the company.
Netflix knew subscribers watch movies starring Kevin Spacey and directed by David Fincher More importantly, they knew the same people loved the 1990 BBC original “House of Cards”
Netflix knew subscribers love dark comedies, plots revolving around prison/crime, and a likeable female lead
Texas Hospital predicts the 30-day readmission risk of heart failure patients
Analytics software has helped the hospital to cut its 30-day readmission rate for nearly half, from 23% to 12%
Agenda
Purpose and Objectives
Turning Big Data into Big Value
Value of Big Data in Understanding Customers
Kabbage Data Centric Approach to Lending
Use of Big Data Analytics to Manage Future Customers
Q & A
Traditional lending is becoming obsolete
Time-consuming
Hard to work with banks
Sub-prime population
Take weeks to get access to cash
Kabbage difference lies in the utilization of in a variety of atypical data sources
Credit history
Detailed sales transactions Payment processing data
Checking account activity
Social data
3 K’s of Kabbage Lending Kapacity: Does the business have enough capacity to handle the loan?
▪ Sales growth ▪ Transaction volume ▪ Account Balance ▪ Revenue vs
expenditures
▪ Past delinquencies ▪ Public records ▪ Bankruptcies ▪ Reviews ▪ Likes ▪ Ratings
Kharacter: Does the business show reliable characteristics?
▪ Store age ▪ Repeat sales ▪ Buyer retention ▪ Reputation
Konsistency: Does the business consistently perform well?
Main Concerns Focus Kabbage’s situation
▪ 12 node Hadoop Cluster ▪ Xlarge instances ▪ Nightly importers ▪ Daily jobs analyzing/scoring customers
Size ETL pipeline
Hadoop
▪ Deeply nested information hierarchies ▪ Dozens of file formats ▪ Inconsistencies and gaps in the data
Challenges Data Types
Scale Complexity
Acceleration & Growth
Kabbage Data Architecture
▪ ~5M new data files every day ▪ Terabytes of raw transactional data ▪ Very complex, rich, (un)structured data sources
Data Mining
& Machine
Learning
Risk & Underwriting
How can we identify and manage risk?
Examples
- Credit risk modeling
- Fraud Detection
- Business sustainability forecast
- Seller-Buyer relationship
Collections Can we have a more optimal Collections strategy?
- Marketing analytics
- Pre-approval models
- Response models
- Collections model
- Payment reversal prediction
- Collection optimization
We solve problems that will ultimately help us better understand SMBs and serve a fingerprint product to them.
Problems we tackle at Kabbage
Marketing How can we spend wisely?
Social data is a particularly interesting area for us to use in risk and underwriting
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5
6
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2
1
Straightforward social clues Ratings
Followers
Likes
Reviews Comments Posts
Sentiment analysis
Sophisticated text mining
Content extraction
Collective Knowledge
Ensemble different pieces of information
Search for a compelling story
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▪ Intuitive yet valuable correlations
– More reviews are correlated with more sustained businesses
– More reviews + high ratings correlate well with credit worthiness
– More likes, more followers, etc. show better growing trajectory
▪ Informative insights from text
– Text mining: “…wrote $20K in bad checks…”
– Sentiment: “…stay away, total scam…”
– Sentiment: “…poor customer service, terrible communication…”
Examples of Key Findings
Agenda
Purpose and Objectives
Turning Big Data into Big Value
Value of Big Data in Understanding Customers
Kabbage Data Centric Approach to Lending
Use of Big Data Analytics to Manage Future Customers
Q & A
Financial institutions use big data to understand customer relationships
Banks monitor customers’ journey through
websites, call centers, branches, …
use the data to predict churn or purchase of a financial service
Connected Sales channels
Various sales channels communicate with each other so that a customer who starts an application online but does not complete it, could get a follow-up offer in the mail, or an email to set up an appointment
Targeted offers
Cash-back deals based on where customer has made payments in the past
Four elements are important for developing advanced analytical capabilities: People, Data, Intent, Tools
Lack of relevant data technologies is the biggest reason why organizations cannot leverage big data analytics. Data storage and format problems follow.
Where financial orgs should invest $$ in big data for big gains? Create customized, consistent customer experience Social media plays a big role in this goal “…Jon and Cheryl’s FB page reveals they just had a baby. Company sends them life insurance offers…”
A unified, single data view of the customer Combine all data into one place, make it accessible to a large audience “…Mortgage dept. just closed a deal with Mr. Smith. Insurance department is unaware, lost cross-sell opportunities…”
Data insight flows to the right people at the right time The information flows in real-time “…Jane is currently in the branch and looking for a bank product. She gets a valuable offer from the teller right there and then…”
Enhance customer relationships through shared values “…Oliver only purchases organic produce at the grocery store. His credit card company sends him a letter showing their commitment to a sustainable environment…”