22. 4 CRITICAL FACTORS
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To having ML be effective for you
1.Use ‘Segments of One’ for personalization and high precision
2.Integrate cross channel data for better decisions
3.‘Whitebox’ decision making for compliance
4.Real-time decision making for great customer experiences
25. @2016 Feedzai
BLACK BOX TO CLEAR BOX
25
Typically Machine Learning is ‘Black Box’ – cannot see why decisions were made.
By changing this, we have fundamentally shifted
the possibilities for Machine Learning.
3/23/16
MURKY
DECISIONS
CLEAR
ANSWERS
2
In the Industrial Revolution, it was about manual labor.
In this new era, it’s about brain power winning over muscle power.
AI is powering the cognitive revolution, and making it possible for companies large and small to compete. It’s leveling the playing field.
There will be winners and losers.
A winner is nuTonomy. They have the worlds first self-driving taxis, before Uber.
Insurify, a startup out of MIT, announced the launch of Evia (Expert Virtual Insurance Agent), an artificially intelligent virtual insurance agent that aims to find you better car insurance using a photo of your license plate.
However, Insurify simplifies the way it gets you that quote by asking you to snap a photo of your license plate and text it to EVIA. The robo-agent then scours millions of records to verify personal information and driving history and then delivers policy quotes and recommendations back to you via text message.
“Silicon Viking” startup — EASI’R — has launched its bid to disrupt the old, antiquated car sales industry with an intelligent algorithm that predicts when customers will buy new vehicles, even before the customers know it themselves.
EASI’R’s new algorithm works within its CRM from the first second it is switched on, thanks to over 20 million customer interactions collected over a ten-year period.
The solution analyzes patterns in customer behavior, then clusters those customers by demographic data, online search behavior, and transactional data, even drawing on sales interaction records held in the CRM. Using that information, and its understanding of millions of customer interactions, it predicts the most promising next steps for each customer.
And it does this throughout the buying cycle, guiding salespeople to take the next steps and helping them send the right information at the right time.
For example, EASI’R can predict when a customer is going to buy a new car, a trigger every car salesperson wants to understand.
If a customer has rejected an offer, for example, the algorithm predicts when the ideal time would be to contact them again, and with which counteroffer, to increase the likelihood of closing an alternate deal.
With its knowledge of car-buying customer patterns, the algorithm can tell the automotive dealers when to send content, what specifically should be sent, and which content delivery channel would be most effective at that particular time.
There are lots of apps that help you research prices for buying or selling a car, and they’ll even hook you up with a dealership when you’re ready to buy.
But Beepi lets you buy and sell used cars in the US without using a dealership — or a test drive — at all.
So how do you buy a car with an app but without testing it? “The same way you buy anything online,” Resnik said. You find a car you like at a price that works for your budget and buy it. A Beepi representative goes to the seller, gives her the money and delivers the car to you. Then you get what Beepi calls a 10-day test drive. If the car doesn’t work out for you, you get your money back.
Part of expanding the market is offering a streamlined financing process. Beepi has partnered with Chase and credit unions to offer traditional financing and, as of this morning, Ally Financial to offer leasing. You can even pay for the entire car with a credit card or Bitcoin.
With Beepi, “there’s no break between online research and online buying,” said Resnik. “You start the process online and finish online. That gives us the advantage.”
Data enables machine learning to make our lives easier
Self-driving cars
Spam filters
Drone delivery
Space and the unknown
ALL INSIDE A COMPUTER THE SIZE OF A HOUSE BRICK
$2 Billion per day in volume
400 dimensions
3000 transactions per second
99.999% availability
Machine learning is to cognitive labor
What 3D printers are to human labor
The world has evolved from humans just building a machine to get their work done faster
Its about building a machine that builds machines that do 100-1000 times as much work as before
E.g. Big Data allows us to offer a better customer experience by…..
talk about how we solve a bank’s “thin-file” account applicants who are credit-worthy and credit-history-poor. They are however “data-rich” so we tap into alternate data such as college attended and education major to make informed decisions using better indicators of default loss than traditional car/home payment history.
E.g. Big Data allows us to offer a better customer experience by…..
talk about how we solve a bank’s “thin-file” account applicants who are credit-worthy and credit-history-poor. They are however “data-rich” so we tap into alternate data such as college attended and education major to make informed decisions using better indicators of default loss than traditional car/home payment history.
Data is being created across multiple channels. In 2016, 1/3 of online transactions are predicted to happen on mobile.
Lines between online and offline are blurring.
Information silos limit the opportunity to identify fraud that crosses channel borders or offer richer experience to customers that matter.
Financial companies have realized that they are not utilizing customer data as they effectively as Google or Facebook do. For example, if a valued customers has a credit card, or a mortgage loan that could benefit from refinancing. A simple adoption of machine learning is to make them a relevant offer.
When the customer accesses the bank’s online channel, calls a call center, or visits a branch, that information is available to the online app, or the sales associate to present the offer.
Data sources
Xx
Lexis Nexus true cost of fraud 2015
Aberdeen study
Fulfillment delays leads to customer dissatisfaction
Digital downloads with instant delivery models are exploding – digital music, game downloaded, instant money transfer are changing the speed of commerce
In high-volume environments, that data arrives at incredible rates, yet still needs to be analyzed and stored.
Which is the way big data gets big - through a constant stream of incoming data.
Making decisions during customer engagement window when it matters
Point solutions -> Unified platform supporting all channels and business lines
Member credit unions buying solutions for unmet needs -> CO-OP offers customized solutions per credit union or segment
Static risk models -> A machine that iterates over hundreds of models for maximum effectiveness
Old technology, e.g. Neural Networks -> State-of-the-art technology, e.g. Random Forests, Deep Learning, Unsupervised Learning
No models for new types of business/channel fraud, e.g. Kiosk fraud, Account Takeovers -> Be able to generate robust models for new fraud types in weeks