如何利用数以百万计的手机活动日志来实时了解我们的客户!

移动应用程序和我们的设计一样好,客户如何使用它。但是他们是怎么使用的呢?我们有超过3500万个设备运行我们的移动银行平台,我们需要了解他们每一个。客户享受他们的经验,他们是丢失了,还是他们是3000英里以外的欺诈性黑客?
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1.How to Use Millions of Mobile Activity Logs to Understand Our Customers, in Real Time! Aaron Colcord, FIS Global Kevin Mellott, FIS Global #DevSAIS18

2. Who is FIS Global? • One of the largest global FinTech companies • Customers are banks and credit unions • We’re FIS Digital Finance, Mobile Data and Analytics • Ecosystem of products and services built around core banking #DevSAIS18 2

3.The Evolution of Banking #DevSAIS18 3

4.Digital Banking • Check our Account Balances • Deposit a Check • Pay Someone/Bill • Get Money out of the ATM • We should be able to replace our actual wallets with the digital wallet. • Let’s talk about Fraud as an aspect of understanding #DevSAIS18 4

5.Fraud Detection Datapoints – Frequency of Transactions – Location of the Transactions – Pattern – Items purchased – Total Spend – Transaction Amount #DevSAIS18 5

6.Fraud Detection has evolved too • The Algorithms that fraud uses have only grown.(This is Anomaly Detection) • The Accuracy of when something is fraud and not fraud has only grown more accurate • Biggest Drawback happens on the backend. – After/During Transaction – Emphasis has been catching faster and notifying faster – Being Predictive about what transactions mean – Grabbing more Detail about the Transaction to make the prediction #DevSAIS18 6

7.This could be better • We are emphasizing more work faster to close our fraud. • When Fraud is detected, usually our access is cut and that is good to stop more bad stuff from occurring. But if there is actually no fraud, that isn’t good. #DevSAIS18 7

8.With Mobile, is this the best we can do? We can prove it is you by BioMetrics We can reasonably assume you have your phone on you We can track what security features you have enabled Prove how you are and where you are Enrich the Experience, Add Behavior and Feedback Loop #DevSAIS18 8

9.Scenarios ONE TWO THREE Behavioral Is this your Weird Pattern primary Transactional device? Pattern #DevSAIS18 9

10.Traditional Fraud Detection • Supervised learning activity • Trained using ALL transactions • Anomaly detection (simplified) #DevSAIS18 10

11.Challenges • Building a training dataset • User behavior is unique • Evolve what we already know #DevSAIS18 11

12.Scenario One ONE TWO THREE Behavioral Is this your Weird Pattern primary Transactional device? Pattern #DevSAIS18 12

13.Behavioral Patterns • Is it normal for this person? • Personalized anomaly detection • Indirect correlation to transaction • Unsupervised analysis #DevSAIS18 13

14.Physical Behavior • When using our mobile app: – Wifi Network – Longitude & Latitude – Device Info (make/model/os) – App Info (installId/version) • Metadata associated with every action #DevSAIS18 14

15.User Behavior • Authentication Details – Biometric (touch, face) – 2FA (SMS, etc) – User/Pass • Payment Method • Device Used – Have we seen it before? When? #DevSAIS18 15

16.Time-based Behavior • Repeating patterns – Pay landlord same amount each month • Time/distance since last entry – Multiple concurrent sessions – Different locations, short time period • User tendencies – Ex: Usual to purchase at 4am local time? #DevSAIS18 16

17.Scenarios ONE TWO THREE Behavioral Is this your Weird Pattern primary Transactional device? Pattern #DevSAIS18 17

18.Device Overload • We live in a world of devices – Voice assistants, watches, phones, etc. – Will keep growing with IOT • People often use the same one for purchasing #DevSAIS18 18

19.Managing Devices • Register upon first time use – Includes step-up authentication method – Provides a way to "activate" privileges – Establishes a baseline #DevSAIS18 19

20.Scenarios ONE TWO THREE Behavioral Is this your Weird Pattern primary Transactional device? Pattern #DevSAIS18 20

21.Weird Transactions • Comparing a specific transaction – "Typical" for the merchant – "Typical" for the customer • Buying 20 laptops online – Normal for a business owner – Sketchy for an individual's account – Establish customer profiles (clustering technique) #DevSAIS18 21

22.How Does it Work? #DevSAIS18 22

23.Architecture #DevSAIS18 23

24.ML Pipeline #DevSAIS18 24

25.Model Storage • Re-builds in background • Databricks Jobs API #DevSAIS18 25

26.Model Usage • Loaded within Structured Streaming • Transformation produces result • Available to other apps #DevSAIS18 26

27.Message Broker • AWS Kinesis • Many will work • Kafka, Azure, etc. #DevSAIS18 27

28.Communicating Fraud • Results write to S3 • Event notification triggers SQS • Queue connects to mobile app • Requires step-up authentication to verify #DevSAIS18 28

29.Feedback Loop • Fraud department activities • Feeds into our training data • Model regenerates via Databricks Job • Always using latest model #DevSAIS18 29