SolidQ Data Science Services Fraud Detection

Transcription

1 SolidQ Data Science Services Fraud Detection Agenda Introduction The Continuous Learning Cycle The Structure of the POC The Benefits 1

2 Initial Situation Attempts to fraud happen every day! Credit card frauds Suspicious bank transfers Loan application frauds Card skimming... SolidQ s Approach Establishing a continuous learning cycle Our tool: Microsoft SQL Server Suite Includes all the software needed for a fraud detection infrastructure If SQL Server (Standard edition or higher) already in use, no further licenses need to be purchased Our service is called Mentoring Work and consulting together with knowledge transfer After project is finished, the customer is able to improve the fraud detection system himself 2

3 Continuous Learning Cycle What does the fraud detection system basically? Checks each transaction Assigning a weight in terms of probability (0-1) if the transaction is fraudulent or not Continuous Learning Cycle Two main techniques Supervised or directed models o Need flags, which mark spotted past frauds o Learn patterns and rules which lead to these flags o Predict frauds in new transaction based on what they learned Unsupervised or undirected models o Analyze data without prior knowledge o Find transactions which do not resemble other transactions (i.e. outliers) o Control the efficiency of directed models 3

4 Continuous Learning Cycle Flag reported frauds Create directed models Predict on new data Refine models Check the predictive models Cluster part of new data Measure over time Check the clustering models The Structure of the POC SolidQ suggests to start with a proof-of-concept (POC) Takes 5-10 working days The team consists of: 1 SolidQ Data Mining Mentor (at least) 1 IT expert (either from customer [recommended] or from SolidQ) (at least) 1 subject matter expert (SME) analytical person, who understands the data The more people are involved the better two teams could work simultaneously 4

5 The Structure of the POC Proof-of-concept Training 1-2 days (optional, but strongly recommended) Data preparation and data overview 2-3 days Preparing and evaluating data mining models 2 days Initial preparation of the continuous learning cycle 1 day Presentation of results 1 day Training We strongly advise to start with a training To get a common background To understand how the algorithms work To understand how to interpret the results To get familiar with the SQL Server suite 5

6 Data Preparation Data selection Building computed variables Sampling Handling of missing values and outliers Categorization Data Overview Checking the distribution of variables Finding dependencies between variables Measuring the amount of information in of variables (entropy) 6

7 Building and Evaluating Data Mining Models Microsoft Neural Networks Work best with ~50% special cases in the total sample data set Microsoft Naïve Bayes Work well with ~10% or more special cases in the total sample data set Microsoft Decision Trees Work well with ~1% or more special cases in the total sample data set Benefits (1) The customer learns patterns of frauds The customer already learns how to do the whole cycle with their own people and call SolidQ only in case of additional complex problems Analytical people can do much more in-depth analyses IT people can do the data extraction and preparation much more efficiently All of them learn how to use creativity for further improvements of the process and procedures 7

8 Benefits (2) Improved data quality Employees could get more satisfaction with their job, as they would contribute to the central knowledge about fraud patterns proactively Because more minds would be involved in the enterprise, the company could expect more and better patterns An Example (1) Credit card issuer manages to detect abuse after an average of 8 fraudulent transactions per card Loss per card: EUR ~3,000 Experience shows: ~0.7% of transactions are fraudulent SolidQ realistically manages to achieve a lift of 50 times the number of frauds found by selecting the rows to check with a directed model comparing to randomly selecting. (we already achieved a lift of 100 times in the past) 8

9 An Example (2) SolidQ can find frauds online after only 2 fraudulent transactions with a probabilty of 70%. More than 4 frauds per card can be prevented (6 * 0.7 = 4.2) Saving per card: EUR ~1,500 Assumption: only 10 cards are stolen / abused per day. Total saving per day: EUR ~15,000 How We Can Help You Expert SolidQ Services Data with Science End-to-End Services Offerings Fraud Detection Market Basket Analysis Churn Detection Text Mining Forecasting Data Understanding 9

10 Why SolidQ Experience You Can Rely On We Stand Behind Our Work Service Delivery Model Built-On Excellence Unparalleled Expertise Our team is made up of more than 80 Microsoft MVPs and numerous PhDs, authors, speakers and recognized industry experts Our team of Microsoft experts will be here for you when you need us Our team ensures that our customers get the highest quality work at the lowest possible cost We avoid the rework, we do it right the first time With over 30 books published, hundreds of speaking appearances and numerous articles written, we re the experts the experts go to when they need help Think Big. Move Fast. 10

11 Questions? Thank you! 11