This document proposes a conceptual design for a fraud detection system for an insurance agency. It is a multi-step process that begins by [1] bootstrapping the system using existing fraud experts to label cases and build an initial knowledge base, then [2] uses machine learning to analyze labeled cases and build predictive fraud models, and [3] continuously improves the quality and coverage of labeled training data through active learning. The goal is to create an adaptive system that can detect evolving fraud patterns over the long term with incremental benefits at each step.
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Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
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3. Fraud Detection System- What is It?
• Fraud Detection System improves the productivity of claims (analyst) department
to detect fraud
– Higher detection with lower human effort
• Some challenges for an insurance agency
– Fraud cases are not labeled and often unknown i.e. not self revealing
– Patterns of fraud change frequently. Old fraud patterns might not continue.
– Cases occur with relative rarity
– Fewer cases of fraud across small data set (base rate and sample size problems)
• Given the challenges
– Off-the-shelf products might not work effectively. They might yield a one time
performance gain and level off afterwards
– Need a hand crafted solution that matures over time to fit a specific insurance agency’s
business lines
• Long Term view of Fraud Detection System
– Instead of one-time quick performance gain, Deep Blue proposes a long term view for
fraud detection in which we continuously label the new cases with the help of Analysts
and improve the coverage of fraud cases.
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4. Our Methodology For Chartis Context
• Bootstrap the knowledge base of fraud detection
– Work with existing fraud analyst team/experts to construct criterion for fraud cases
– Anomaly detection by deep analysis of available data and features generates a large
number of hypothesis to locate potential fraud cases
• This is done by detecting anomalies across various hierarchies (providers, claimants,
geographies, etc.) and across features within hierarchies
– This leads to a simple system which flags cases for labeling
• Deploy Machine Learning to analyze labeled cases and construct robust fraud
prediction models
– Adapt the algorithms to changing patterns in the fraud by periodic rebuilding
– Continuously force the fraud prediction models to explore other features (attributes) as
potential lead indicators of fraud. Expand types of fraud that are uncovered.
• Make continuous effort to improve the quality of fraud detection case data
– In bootstrapped system, cases flagged for review may not have a prediction (i.e.
fraud/not and “case of interest” or not) due to lack of labeled data
– Active learning uses subsequently labeled cases to enable prediction of (1) Fraud; and
(2) Cases of Interest
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5. Fraud Detection System (FDS) – Boot Strapping
Fraud Analysts Labeled Cases
(Human) Labeled Case database
Anomaly Expert System
Detection - Boot Strapping
- Distribution Analysis Knowledge base
- Feature Analysis
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6. FDS – Expansion and Adaption
Expert System
Decommissioned Labeled Cases
Fraud Analysts
(Human) Labeled Case database
Cases to Fraud And/Or
Evaluate Case Of Interest
Anomaly Fraud Detection Engine
Detection Active Learner -ML Predictive Models
- Distribution Analysis Ongoing - Rankings & Voting
- Feature Analysis - Adapting Models Case Stream
Monitoring
Not Classifiable
Not Fraud and
Not Case Of Interest
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7. Key Strengths Of Proposed Design
• Incremental design which produces incremental benefits at each step
• Extremely adaptable to changing patterns of fraud
• Modular in design and highly reusable across business lines
– A minor customization is necessary to adapt to specific business lines
• Low risk investment approach
– To improve data collection and knowledge repository around fraud detection
– To develop analytical infrastructure that creates fraud detection capabilities inside
Chartis
• Ability to apply the best of breed techniques and latest research advancements in
fraud detection
– Packaged products often lag cutting edge modeling advancements by a few years
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8. Sample Application - Auto Insurance Claim Fraud
Labeled Claims
1) Bumper, injury, NY, … F, COI*
Fraud Analysts Labeled Cases
2) Side collision, dent, OH,..NF, NCOI
(Human) 3) flooding, radiator, AZ,.. NF, COI
Cases to Fraud And/Or
Evaluate Case Of Interest
Input for
Fraud Detection Engine Model Building
Anomaly Active Learner - Some Techniques: Logistic
Detection Regression, Neural Networks,
-Active clustering Decision Trees , Random Forest
- Distribution Analysis Ongoing
- Feature Analysis - Fuzzy claims - If trees are used, a potential rule: Case Stream
Monitoring If ( zip = 10063 && type = bumper
&& time_of_incident < 6 AM ) =>
COI
Input Data:
(1) Accident Characteristics Not Classifiable
(2) Claimant Characteristics
(3) Insured Characteristics Not Fraud and
(4) Injury Characteristics Not Case Of Interest
(5) Treatment
* F- Fraud, NF- No Fraud, COI- Case of Interest, NCOI- No Case of Interest
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