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Fraud Detection in Insurance
with Machine Learning
for WARTA (TALANX Group)
Artur Suchwalko, Ph.D., QuantUp, CEO
artur@quantup.pl
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Introduction
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What do you think about…
…when you think about fraud detection with ML?
• Deep Learning, xgboost, Autoencoder, severe class imbalance?
• How to apply the model to real decision taking, sample
representativeness, having historical frauds identified and
marked, potential features, goal function?
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Case Study: Warta (high level)
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About Warta
• 2nd biggest insurer in Poland
• Full offering: Life and non-life insurances
• Member of Talanx Group
• Award winning innovator, e.g.
• InsurTech Congress award for implementing anti-fraud
solution (http://media.warta.pl/pr/356330/warta-doceniona-
za-wdrozenie-platformy-datawalk)
• First comprehensive mobile app for claim handling process.
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Project scope
Warta’s reasons to start:
• Looking for comprehensive anti-fraud solution for non-life insurances
• Convinced to improve anti-fraud KPIs
• Readiness to replace existing technologies: IBM, Statistica.
Chosen solution:
• DataWalk – data gathering, data linking, expert scoring and fraud investigations.
• QuantUp – Machine Learning algorithms to improve suspicious claim selection.
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Integration with DataWalk
DataWalk is a Big Data software platform for connecting numerous
large data sets, both external and internal, into a single repository
for fast visual analysis.
DataWalk can be used for:
• Fast data modelling
• Fraud hypothesis prototyping
• Fraud scoring
• Fraud investigations
Analyze your data
10x faster
Increasing the
effectiveness of
anti-fraud rules up
to 80%
Pre-configured rules
and scores
30-90 days return on investment
Demo movie: https://youtu.be/h45mheDH4uU 7

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Integration with DataWalk
• DataWalk enables easy to use, graphical
interface (Universe Viewer) to interpret and
link data, as well as to create and maintain
ABTs.
• Well prepared and easy to update data model
is a fundamental issue in ABT creation and
predictive model credibility.
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Case Study: Warta (auto insurance & ML only)
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Goal & results
• Goal:
• Improving of detection of probable frauds for further investigation
• Probable / doubtful / suspicious claim: suspected to be a fraud but not proven to be one
• Finding and proving are two different things
• Result: improvement of order of 30% (comparing to past simple models)
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Important business questions
• How to choose claims for investigation to:
• detect highest number of fraud attempts?
• detect highest amount of fraudulent claims?
• detect highest amount with limited resources and time for detection?
• be able to prove highest number / amount of fraud attempts?
• This requirement is translated into a suitable goal function for a model
• and should affect the optimization criterion.
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Claim case 1: Rules / human
• Description: A driver hit the rear side of a victim's car. The car was pushed to the crossroads
area and there was a collision with a third car (Mercedes). The police was called.
• Rules:
• airbags inflated
• similar age of both drivers
• difference of cars' age >=11 years
• historical loss coefficient >=5
• Result: Not refused to pay because of fraud attempt: the description was consistent with
the damages
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Claim case 2: Model
• Description: I (victim) was driving a left lane. The second driver (a culprit) was driving a right
lane (the same direction). He wanted to change the lane, haven't seen my car and hit my
car. Its rear left side damaged my car's right front side.
• Analysis: no clear evidence
• only one year of cars' age difference
• no age information for the second driver
• insurance policy was not new
• no claim history for drivers and cars
• Result: Refused to pay because of fraud attempt: no correlation between description and
damages – not possible to be a real claim (verified)
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How to build a model?
• Preparation of the predictors (can be complex because of aggregation of data from many
sources) in a form of ABT
• Having the target variable in the historical data
• Build a predictive model
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Important
• Checking if modeling is possible (the process of claim handling influences the historical
data): 0% vs. 100% checked
• Definition of new predictors
• Detection of false predictors
• Data enhancement: historical aggregates, textual, external
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Inside
• Historical information about all collission parties
• Extraction of information from text notes
• Avoiding false predictors
• Boosted trees
• with a non-standard goal function
• and careful hyperparameter optimization
• Reduction of number of predictors to make the model more simple and robust
• Handling new values, e.g. car model
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Pure analytics vs. business
ROC for less and more complex models
These results don’t reflect the real values and are used for illustrative purposes17

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Numbers, amounts & preprocessing
ROC for simple and complex models
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Numbers, amounts & preprocessing
ROC for amounts for simple and complex models
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Non-standard goal functions
• Ranking model: checking claims basing on potential profit
• Simple classification models base on counts
• Replication of results
ROC curve
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Non-standard goal functions
Profit accordingly to the model ordering 21

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Non-standard goal functions
• Claim amount turned out to be a strong predictor
• The amount could decide about verification: high claims first
• Even independently of predictors / model!
Amount acordingly to the model ordering
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False predictors
Ranking (VIP-alike): iterative removing of the best feature and rebuilding of the model:
1. Active features: all
2. Build a model using active features
3. Calculate AUC and a features ranking
4. Deactivate the best feature accordingly to the rating
5. Go to 2 until all features are inactive.
6. Plot and conclude
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False predictors
False predictors (red) can be anywhere! 24

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Summary
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Project results
First quarter of using the full-scope solution
• Detection Rate Improvement in 1st quarter: +60%
• True Positives > 80%
• ROI = less than 2 months (!)
• Predictive models responsible for 30-40% of the
final fiscal results.
https://m.bankier.pl/wiadomosc/Polowanie-na-
dawcow-polis-czyli-na-nas-7599390.html
BENEFICIARIES
OF DATAWALK & R IMPLEMENTATION
Vice President Claims
• Extremely positive project ROI.
• Reduction of technology providers
• Results accomplished 6x faster and ~20x cheaper
than similar project at key competitor.
• Warta strengthens position of market innovator
in claim handling area.
Head of Anti-Fraud Department
• Impressively improved business results.
• Higher satisfaction and trust in analytics among
team members.
• Knowledge accommodation and knowledge
sharing within the team.
Head Analysts
• Full control over analytical environment.
• Access to all data without engaging IT.
• Expert scoring, machine learning and
investigations in one place.
• Possibility to test new fraud schemas.
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Summary
• Predictive models alone gave a fraction of the total ROI
• The business goal is not always just directly maximizing losses, income etc.
• It’s pretty common for DS/ML projects to get additional profit as a side effect
• ROI for such projects should be measurable and high (but not neccessarily fast) for carefully
chosen business cases
• Predictive models can be significantly improved not spending much (hyperparameters
tuning, goal function, methods etc.)
• There are pitfalls to avoid!
• Usually you don’t need fancy hardware / software (PCs + R!)
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What’s important in fraud detection with ML?
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Business & Analytics
• Find a good business case (volume big enough)
• State the business goal and carefully translate it into analytics: use the right goal function
• Correct process of model building
• Controlled implementation
• Measuring model effectiveness comparing to no model / previous situation – using right
KPIs (not always simple, not always possible)
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Process & Data
• Check if modeling is possible with supervised models (fraud flags stored; correct, and
representative sample; good data coverage)
• Data preparation is the most important factor
• Use many data sources
• Data enhancement: aggregates from historical data, textual, external
• Cost of data preparation!
• Detection of false predictors: if not detected then the model is degraded in production (it is
arduous for wide data)
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Data sources
• ”Plain” data: basic
• Complete data related to the loss, claim, and parties involved
• Flags of historical frauds
• ”Plain” data: enhanced
• Using ZIP codes and additional statistics, e.g. fraction of forest area, unemployment rate
• Weather data
• Analysis of connections (SNA)
• Tekst (words from a list, n-grams, others)
• Analysis of neighbourhood using maps
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What influences model quality?
• Solving the right business problem
• Sample representativeness
• Goal function in line with the business goal
• Right model complexity and the correct model building process
• Costs of misclassifications, e.g. false alarm rate
• Black box predictions explanations  proving fraud attempts  improving of actionability
• It’s pretty hard to get everything in a single model
• Validation of the model and carefully testing its implementation
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Methods
• Commitees / ensembles of trees / boosted trees – good results, possible to use different
goal functions, variable importance, handling NA’s  use this!
• Deep Neural Networks – for data complex enough but still having the same structure
• Manual feature extraction not neccessary
• Any (almost) goal function
• Recurrent Neural Networks – working directly on events not on aggregates from ABT
• Using black box model’s prediction explanations (LIME and its friends) – to improve
actionability
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How to improve a model?
• Average model
• vs. human / rules: +10-30%
• Good model
• vs. average model: +10-50% (depending on measurement)
• predictive power driven by data
• Incorrect model
• vs. human / rules: +0% (or losses)
• works in a computer only
Assuming that the goal function and actionability remain unchanged
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About me
• Commercial experience in DS / ML: > 20 years, ~ 100 projects, ~ 3,000 hours of workshops
• Translating a business problem into an analytics problem + choosing adequate means to
solve the latter
• Founder & owner of QuantUp DS / ML firm
• Contact me if you need:
• During the conference
• After the conference: artur@quantup.pl
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