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Fraud Detection Class Slides
1.
Graphs in Fraud Detection Max De Marzi Field Engineer, Neo4j @maxdemarzi
2.
About Me • Max De Marzi - Neo4j Field Engineer • My Blog: http://maxdemarzi.com •
Find me on Twitter: @maxdemarzi • Email me: maxdemarzi@gmail.com • GitHub: http://github.com/maxdemarzi
3.
Overview Types of Fraud • Credit Card Fraud • First-Party Fraud •
Synthetic Identities and Fraud Rings • Insurance Fraud Types of Analysis • Traditional Analysis • Graph-Based Analysis Fraud Detection and Prevention Common Questions
4.
…but before we get into that … • What isn’t Fraud?
5.
I don’t know, but I know who does • Alex Beutel, CMU • Leman Akoglu, Stony Brook •
Christos Faloutsos, CMU • Graph-Based User Behavior Modeling: From Prediction to Fraud Detection • http://www.cs.cmu.edu/~abeutel/kdd2015_tutorial/
6.
User Behavior Challenges • How can we understand normal user behavior?
7.
User Behavior Challenges • How can we understand normal user behavior? • How can we find suspicious behavior?
8.
User Behavior Challenges • How can we understand normal user behavior? • How can we find suspicious behavior? •
How can we distinguish the two?
9.
Users
10.
Does your little girl like Rambo?
11.
Personalization
12.
Understanding our Users • What do we know about them?
13.
Demographics: Age
14.
Demographics: Gender
15.
Understanding our Users MATCH (u:User)-[r:RATED]->(m:Movie) RETURN u.gender, u.age, COUNT (DISTINCT u) AS user_cnt, COUNT (DISTINCT m) AS mov_cnt, COUNT(r) AS rtg_cnt
16.
Understanding our Users
17.
Understanding our Users MATCH (me:User {id:1}) -[r1:RATED]-> (m:Movie) <-[r2:RATED]- (similar_users:User) WHERE ABS(r1.stars-r2.stars) <= 1 RETURN similar_users.gender, similar_users.age, COUNT(DISTINCT similar_users) AS user_cnt, COUNT(r2) AS rtg_cnt
18.
Understanding our Users
19.
Little Girls like Movies other Little Girls Like
20.
Little Girls like Movies other Little Girls Like
21.
What do Little Girls Like? MATCH (u:User)-[r:RATED]->(m:Movie) WHERE u.age = 1 AND u.gender = "F" AND r.stars > 3 RETURN m.title, COUNT(r) AS cnt ORDER BY cnt DESC LIMIT 10
22.
What do Little Girls Like?
23.
What do Men 25-34 Like? MATCH (u:User)-[r:RATED]->(m:Movie) WHERE u.age = 25 AND u.gender = "M" AND r.stars > 3 RETURN m.title, COUNT(r) AS cnt ORDER BY cnt DESC LIMIT 10
24.
What do Men 25-34 Like?
25.
Modeling “Normal” Behavior • Predict Edges (Similar Users)
26.
Modeling “Normal” Behavior • Predict Edges (Movies I should Watch)
27.
Recommendation Engine with Neo4j Recommendation
28.
Content Based Recommendations • Step 1: Collect Item Characteristics • Step 2: Find similar Items •
Step 3: Recommend Similar Items • Example: Similar Movie Genres
29.
There is more to life than Romantic Zombie-coms
30.
Collaborative Filtering Recommendations • Step 1: Collect User Behavior • Step 2: Find similar Users •
Step 3: Recommend Behavior taken by similar users • Example: People with similar musical tastes
31.
You are so original!
32.
Using Relationships for Recommendations Content-based filtering Recommend items based on what users have liked in the past Collaborative filtering Predict what users like based on the similarity of their behaviors, activities and preferences to others Movie Person Person RATED SIMILARITY rating: 7 value: .92
33.
Hybrid Recommendations • Combine the two for better results • Like Peanut Butter and Jelly
34.
Hello World Recommendation
35.
Hello World Recommendation X
36.
Movie Data Model
37.
Cypher Query: Movie Recommendation MATCH (watched:Movie {title:"Toy Story”}) <-[r1:RATED]- () -[r2:RATED]-> (unseen:Movie) WHERE r1.rating > 7 AND r2.rating > 7 AND watched.genres = unseen.genres AND NOT( (:Person {username:”maxdemarzi"}) -[:RATED]-> (unseen) ) RETURN unseen.title, COUNT(*) ORDER BY COUNT(*) DESC LIMIT 25 What are the Top 25 Movies • that I haven't seen • with the same genres as Toy Story •
given high ratings • by people who liked Toy Story
38.
Let’s try k-nearest neighbors (k-NN) Cosine Similarity
39.
Cypher Query: Ratings of Two Users MATCH (p1:Person {name:'Michael Sherman’}) -[r1:RATED]-> (m:Movie), (p2:Person {name:'Michael Hunger’}) -[r2:RATED]-> (m:Movie) RETURN m.name AS Movie, r1.rating AS `M. Sherman's Rating`, r2.rating AS `M. Hunger's Rating` What are the Movies these 2 users have both rated
40.
Cypher Query: Ratings of Two Users Calculating Cosine Similarity
41.
Cypher Query: Cosine Similarity MATCH (p1:Person) -[x:RATED]-> (m:Movie) <-[y:RATED]- (p2:Person) WITH SUM(x.rating * y.rating) AS xyDotProduct, SQRT(REDUCE(xDot = 0.0, a IN COLLECT(x.rating) | xDot + a^2)) AS xLength, SQRT(REDUCE(yDot = 0.0, b IN COLLECT(y.rating) | yDot + b^2)) AS yLength, p1, p2 MERGE (p1)-[s:SIMILARITY]-(p2) SET s.similarity = xyDotProduct / (xLength * yLength) Calculate it for all Person nodes with at least one Movie between them
42.
Movie Data Model (v2)
43.
Cypher Query: Your nearest neighbors MATCH (p1:Person {name:'Grace Andrews’}) -[s:SIMILARITY]- (p2:Person) WITH p2, s.score AS sim RETURN p2.name AS Neighbor, sim AS Similarity ORDER BY sim DESC LIMIT 5 Who are the • top 5 Persons and their similarity score • ordered by similarity in descending order •
for Grace Andrews
44.
Your nearest neighbors
45.
Cypher Query: k-NN Recommendation MATCH (m:Movie) <-[r:RATED]- (b:Person) -[s:SIMILARITY]- (p:Person {name:'Zoltan Varju'}) WHERE NOT( (p) -[:RATED]-> (m) ) WITH m, s.similarity AS similarity, r.rating AS rating ORDER BY m.name, similarity DESC WITH m.name AS movie, COLLECT(rating)[0..3] AS ratings WITH movie, REDUCE(s = 0, i IN ratings | s + i)*1.0 / LENGTH(ratings) AS recommendation ORDER BY recommendation DESC RETURN movie, recommendation LIMIT 25 What are the Top 25 Movies • that Zoltan Varju has not seen • using the average rating •
by my top 3 neighbors
46.
Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes (Age, Gender, etc) Age: 35 Age: ?
47.
Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes •
Predict Edge Attributes (Rating)
48.
What Rating should I give 101 Dalmatians? MATCH (me:User {id:1})-[r1:RATED]->(m:Movie) <-[r2:RATED]-(:User)-[r3:RATED]-> (m2:Movie {title:”101 Dalmatians”}) WHERE ABS(r1.stars-r2.stars) <=1 RETURN AVG(r3.stars)
49.
Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes •
Predict Edge Attributes • Clustering and Community Detection
50.
Predict a Star Rating purely on Demographics MATCH (u:User)-[r:RATED]->(m:Movie {title:”Toy Story”}) WHERE u.age = 1 AND u.gender = "F" RETURN AVG(r.stars)
51.
Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes •
Predict Edge Attributes • Clustering and Community Detection • Fraud Detection
52.
Two Sides of the Same Coin Recommendations • Add the relationship that does not exist Fraud Detection • Find the relationships that should not exist
53.
Modeling User Behavior • Modeling normal users and detecting anomalies are two sides of understanding user behavior
54.
Modeling User Behavior • Modeling normal users and detecting anomalies are two sides of understanding user behavior • Rough Model of normal vs outlier
55.
Modeling User Behavior • Modeling normal users and detecting anomalies are two sides of understanding user behavior. • Fine grained models can find more subtle outliers
56.
Modeling User Behavior • Modeling normal users and detecting anomalies are two sides of understanding user behavior • Complex models can capture normal and abnormal patterns
57.
Modeling User Behavior • Modeling normal users and detecting anomalies are two sides of understanding user behavior • Known fraudulent patterns can be searched for directly
58.
Credit Card Fraud
59.
Cross Reference
60.
Find the Nodes ArrayList<Node> nodes = new ArrayList<Node>(); nodes.add(db.findNode(Labels.CC, “number”, card)); nodes.add(db.findNode(Labels.Phone, “number”, phone)); nodes.add(db.findNode(Labels.Email, “address”, address)); nodes.add(db.findNode(Labels.IP, “address”, ip));
61.
Add the Crosses for(Node node : nodes){ HashMap<String, AtomicInteger> crosses = new HashMap<String, AtomicInteger>(); crosses.put("CCs", new AtomicInteger(0)); crosses.put("Phones", new AtomicInteger(0)); crosses.put("Emails", new AtomicInteger(0)); crosses.put("IPs", new AtomicInteger(0)); for ( Relationship relationship : node.getRelationships(RELATED, Direction.BOTH) ){ Node thing = relationship.getOtherNode(node); String type = thing.getLabels().iterator().next().name() + "s"; crosses.get(type).getAndIncrement(); } results.add(crosses); }
62.
Examine Results [{"ips":4,"emails":7,"ccs":0,"phones":4}, -- cc returned 4 ips, 7 emails, and 3 phones. {"ips":1,"emails":1,"ccs":1,"phones":0}, -- phone returned just 1 item for each cross reference check. {"ips":2,"emails":0,"ccs":4,"phones":3}, -- email returned 2 ips, 4 credit cards and 3 phones. {"ips":0,"emails":1,"ccs":3,"phones":2}] -- ip returned 3 credit cards and 2 phones.
63.
What is a subgraph? KDD 2015 2
64.
Subgraphs
65.
What is a subgraph? KDD 2015 3 •
A Subset of nodes and the edges between them
66.
What are some useful subgraphs? Largest dense subgraph (Greatest
average degree)
67.
What are some useful subgraphs? E Ego-network: the subgraph among a
node and its neighbors
68.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern
69.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern MATCH (a)--(b)--(c)--(a) RETURN *
70.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern
71.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern
72.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern
73.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern
74.
What are some useful subgraphs? Graph queries: find subgraphs
of particular pattern MATCH (a)—(b)—(c)— (d)—(a)—(c), (d)—(b) RETURN *
75.
Graphs as Matrices
76.
Clustering gives Clarity Link
77.
Ego-net Patterns
78.
Ego-net Patterns Ni: number of
neighbors of ego i Ei: number of edges in egonet i Wi: total weight of egonet i λw,i: principal eigenvalue of the weighted adjacency matrix of egonet i
79.
Power Law Density slope=2 slope=1 slope=1.35
80.
Power Law Weight
81.
Power Law Eigenvalue
82.
Find Groups within Ego-Nets
83.
Find Groups within Ego-Nets Link
84.
First-Party Fraud
85.
First-Party Fraud • Fraudster’s aim: apply for lines of credit, act normally, extend credit, then…run off with it • Fabricate a network of synthetic IDs, aggregate smaller lines of credit into substantial value •
Often a hidden problem since only banks are hit • Whereas third-party fraud involves customers whose identities are stolen • More on that later…
86.
So what? • $10’s billions lost by banks every year • 25% of the total consumer credit write-offs in the USA •
Around 20% of unsecured bad debt in E.U. and N.A. is misclassified • In reality it is first-party fraud
87.
Fraud Ring
88.
Then the fraud happens… • Revolving doors strategy • Money moves from account to account to provide legitimate transaction history •
Banks duly increase credit lines • Observed responsible credit behavior • Fraudsters max out all lines of credit and then bust out
89.
… and the Bank loses • Collections process ensues • Real addresses are visited •
Fraudsters deny all knowledge of synthetic IDs • Bank writes off debt • Two fraudsters can easily rack up $80k • Well organized crime rings can rack up many times that
90.
Discrete Analysis Fails to predict…
91.
…and Makes it Hard to React • When the bust out starts to happen, how do you know what to cancel? • And how do you do it faster then the fraudster to limit your losses? •
A graph, that’s how!
92.
Probably Non-Fraudulent Cohabiters
93.
Probable Cohabiters Query MATCH (p1:Person)-[:HOLDS|LIVES_AT*]->() <-[:HOLDS|LIVES_AT*]-(p2:Person) WHERE p1
<> p2 RETURN DISTINCT p1
94.
Dodgy-Looking Chain
95.
Risky People MATCH (p1:Person)-[:HOLDS|LIVES_AT]->() <-[:HOLDS|LIVES_AT]-(p2:Person) -[:HOLDS|LIVES_AT]->() <-[:HOLDS|LIVES_AT]-(p3:Person) WHERE p1
<> p2 AND p2 <> p3 AND p3 <> p1 WITH collect (p1.name) + collect(p2.name) + collect(p3.name) AS names UNWIND names AS fraudster RETURN DISTINCT fraudster
96.
Pretty quick… Number of people:
[5163] Number of fraudsters: [40] Time taken: [100] ms
97.
Localize the focus MATCH (p1:Person {name:'Sol'})-[:HOLDS|LIVES_AT]-()… Number
of fraudsters: [5] Time taken: [13] ms
98.
Stop a bust-out in
ms.
99.
Quickly Revoke Cards in Bust-Out MATCH (p1:Person)-[:HOLDS|LIVES_AT]->() <-[:HOLDS|LIVES_AT]-(p2:Person) -[:HOLDS|LIVES_AT]->() <-[:HOLDS|LIVES_AT]-(p3:Person) WHERE p1
<> p2 AND p2 <> p3 AND p3 <> p1 WITH collect (p1) + collect(p2)+ collect(p3) AS names UNWIND names AS fraudster MATCH (fraudster)-[o:OWNS]->(card:CreditCard) DELETE o, card
100.
Auto Fraud
101.
Whiplash http://georgia-clinic.com/blog/wp-content/uploads/2013/10/whiplash.jpg
102.
Whiplash for Cash http://georgia-clinic.com/blog/wp-content/uploads/2013/10/whiplash.jpg http://cdn2.holytaco.com/wp-content/uploads/2012/06/lottery-winner.jpg
103.
Whiplash for Cash
Example Accidents Cars Doctor Attorney People Drives Is Passenger Drivers Passengers Witnesses
104.
Risk • $80,000,000,000 annually on auto insurance fraud and growing • Even small % reductions are worthwhile! •
British policyholders pay ~£100 per year to cover fraud • US drivers pay $200-$300 per year according to US National Insurance Crime Bureau
105.
Regular Drivers
106.
Regular Drivers Query MATCH (p:Person)-[:DRIVES]->(c:Car) WHERE NOT
(p)<-[:BRIEFED]-(:Lawyer) AND NOT (p)<-[:EXAMINED]-(:Doctor) AND NOT (p)-[:WITNESSED]->(:Car) AND NOT (p)-[:PASSENGER_IN]->(:Car) RETURN p,c LIMIT 100
107.
Genuine Claimants
108.
Genuine Claimants Query MATCH (p:Person)-[:DRIVES]->(:Car), (p)<-[:BRIEFED]-(:Lawyer), (p)<-[:EXAMINED]-(:Doctor) OPTIONAL MATCH
(p)-[w:WITNESSED]->(:Car), (p)-[pi:PASSENGER_IN]->(:Car) RETURN p, count(w) AS noWitnessed, count(pi) as noPassengerIn
109.
Fraudsters
110.
Fraudsters MATCH (p:Person)-[:DRIVES]->(:Car), (p)<-[:BRIEFED]-(:Lawyer), (p)<-[:EXAMINED]-(:Doctor), (p)-[w:WITNESSED]->(:Car), (p)-[pi:PASSENGER_IN]->(:Car) WITH p,
count(w) AS noWitnessed, count(pi) as noPassengerIn WHERE noWitnessed > 1 OR noPassengerIn > 1 RETURN p
111.
Auto-fraud Graph • Once you have the fraudsters, finding their support team is easy. • (fraudster)<-[:EXAMINED]-(d:Doctor) •
(fraudster)<-[:BRIEFED]-(l:Lawyer) • And it’s also easy to find their passengers • (fraudster)-[:DRIVES]->(:Car)<-[:PASSENGER_IN]-(p) • And easy to find other places where they’ve maybe committed fraud • (fraudster)-[:WITNESSED]->(:Car) • (fraudster)-[:PASSENGER_IN]->(:Car) • And you can see this in milliseconds!
112.
It’s all about the
patterns
113.
Phony Persona
114.
Online Payments Fraud (First-Party) • Stealing credentials is commonplace • Phishing, malware, simple naïve users •
Buying stolen credit card numbers is easy • How should one protect against seemingly fine credentials? • And valid credit card numbers?
115.
We are all little stars • Username and passwords • Two-factor auth •
IP addresses, cookies • Credit card, paypal account • Some gaming sites already do some of this • Arts and Crafts platform Etsy already embraced the idea of graph of identity
116.
An Individual Identity Subgraph 128.240.229.18 fred@rbs.co.uk 1234LOL
117.
We are all made of stars…
118.
Other Specific Considerations Specific Weighted Identity Query MATCH (u:User {username:'Jim',
password: 'secret'}) OPTIONAL MATCH (u) -[cookie:PROVIDED]->(:Cookie {id:'1234'}) OPTIONAL MATCH (u)-[address:FROM]->(:IP {network:'128.240.0.0'}) RETURN SUM(cookie.weighting) + SUM(address.weighting) AS score Bare Minimum Other Specific Considerations Final Decision
119.
General Weighted Identity Query MATCH (u:User {username:'Jim',
password: 'secret'}) OPTIONAL MATCH (u)-[rel]->() WHERE has(rel.weighting) RETURN SUM(rel.weighting) AS score Bare Minimum All Available Weightings Final Decision
120.
An Individual Login History fred@rbs.co.uk 1234LOL
121.
From 1st to 3rd Party • The 1st party identity graph can easily be extended to 3rd party fraud • Like in the bank fraud ring, fraudsters can mix-n-match claims •
Start with a few phished accounts and expand from there!
122.
Shared Connections 128.240.229.18 fred@rbs.co.uk 1234LOL nick@bearings.com Ca$hMon£y
123.
Graphing Shared Connections Hmm….
124.
Scan for Potential Fraudsters MATCH (u1:User)--(x)--(u2:User) WHERE u1
<> u2 AND NOT (x:IP) RETURN x Network in common is OK
125.
Stop specific fraudster network, quickly MATCH path =
(u1:User {username: 'Jim'})-[*]-(x)-[*]-(u2:User) WHERE u1<>u2 AND NOT (x:IP) AND NOT (x:User) RETURN path
126.
How do these fit with traditional fraud prevention? http://www.gartner.com/newsroom/id/1695014 Gartner’s Layered Fraud Prevention Approach
127.
Demo Time
128.
Bank Fraud http://gist.neo4j.org/?dfdfbddfdc63f4858f80
129.
Credit Card Fraud Detection http://gist.neo4j.org/?3ad4cb2e3187ab21416b
130.
Whiplash for Cash http://gist.neo4j.org/?6bae1e799484267e3c60
131.
132.
133.
Ask for help if you get stuck • Online training - http://neo4j.com/graphacademy/ • Videos - http://vimeo.com/neo4j/videos •
Use cases - http://www.neotechnology.com/industries-and-use-cases/ • Meetups • Books to get your started • http://www.graphdatabases.com • http://neo4j.com/book-learning-neo4j/
134.
Deep Neural Networks for Bank Fraud https://www.youtube.com/watch?v=TAer-PeIypI Fraud Detection starts about half-way (after intro)
135.
Thanks for listening @maxdemarzi
Download now