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Enhancing Fraud Detection with Re-Ranking Subgroups and Local Patterns

This overview presents a novel approach to fraud detection utilizing re-ranking of subgroups identified through local patterns. It discusses the challenges in recognizing fraud in healthcare, the development of a relevant algorithm, and an application case called iWebCare. The methodology emphasizes the need for user input to define subgroup interestingness, leading to better identification of novel fraud patterns. By employing statistical techniques and machine learning, we aim to create a system that autonomously discovers significant fraud indicators while catering to practical application requirements.

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Enhancing Fraud Detection with Re-Ranking Subgroups and Local Patterns

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Presentation Transcript

  1. Re-ranking Subgroups for Fraud Detection Stefan Rüping Fraunhofer IAIS Dagstuhl Seminar „Parallel Universes and Local Patterns“

  2. Overview • Fraud Detection • A prototypical application case of local patterns? • Subgroup Discovery • A prototypical algorithm for local patterns • Re-ranking Subgroups • Making it work in practice… • Summary

  3. Application Case – Project iWebCare • Developement of a e-government Web Service Plattform for Fraud Detection in Healthcare • Challenges: • Identification of novel fraud patterns • Monitoring of fraud patterns • Autonomous Mining, i.e. user works without the assistance of a data miner

  4. Fraud Detection • Assumption: there exist typical ways of committing fraud, which make up small, but significant fraud patterns • Problem setting • In theory, supervised learning problem: map cases to fraud label • In practice, fraud labels impossible to collect • Alternative approaches • Analyze proxy label • Money spent, prescriptions issued, … • Find interesting patterns in the data • Interestingness is subjective to domain expert  prototypical application case for local patterns

  5. Subgroup Analysis • Task • Given examples (xi,yi)i=1…n X{0,1}, and kN • Find the k subgroups of X with highest statistical deviation in the probability of y • Subgroup S described by propositional formula • x(1) = A & x(2) = B  P(y=1) = 0.9 • Quality measure: q(S) = ga |p-p0| where • g = #{ i | xi  S } / n, and • p = #{ i | xi  S, yi = 1 } / #{ i | xi  S } • Algorithm: Explora (Kloesgen, 1996) • Full depth-First-Search with effective pruning • Several heuristic / randomized algorithms • Extension to numeric y possible

  6. Subgroup Analysis II • Easily extensible to weighted examples: Given examples (xi,yi)i=1…n X{0,1}, and weights wi  0 • Let q(S) = ga |p-p0| where • g = iSwi , and • p = iS yi wi / g • Obviously identical to standard case when wi = 1/n

  7. Problem Setting • Starting point • Fraud label does not exist • Domain expert can name an attribute whose distribution is somehow related to fraud • Subgroups which induced by this attribute are not necessarily to most interesting ones • Expert can hardly define what is interesting to him • Expert can easily give pairwise comparisons of subgroups: more-interesting-than • Assumption: the interestingness of a subgroup to the expert is defined by • The form of the subgroup, i.e. the parameter a • The attributes used to define the subgroup • The examples covered by the subgroup

  8. Re-Ranking of Subgroups • Approach: Given • A list of subgroups (Si)i=1…n • Expert’s comparisons of subgroups from this list, i.e. set P of pairs (i,j) meaning that subgroup i is more interesting than subgroup j • Find a subgroup quality measure q’ which better correlates to the experts assessment of interestingness • Select the most interesting and the k most irrelevant subgroups from P • Represent subgroup S as (g, |p-p0|, attD, attS) where • p, g defined as usual • attD binary vector of size dim(X) with attD(i) = 1  attribute i used in definition of subgroup S • attS vector of size of intersection of S with most interesting / irrelevant subgroups

  9. Algorithm • Reminder: S represented by (g, |p-p0|, attD, attS) • Assume q’(S) = ga |p-p0| j exp(attD(j)w(j)) i exp(attS(i)w(d+j)) • Usual quality function plus additional factor for attributes and covered examples • Such that log q’(S) = (a, 1, wd, ws) * (log g, log |p-p0|, attD, attS)  Can use modified version of ranking SVM to find (a, wd, ws) that maximize correlation of q’ with interestingness information given by user

  10. Ranking SVM • Standard Variant: • Subgroup-ranking Variant:

  11. Iterative Algorithm • Find subgroups w.r.t. proxy attribute • Ask user for interestingness information, encoded as pairs P • Use ranking SVM to find (a, wd, ws) to maximize correlation of q’ with P • Re-start subgroup search with new a and weights wS • Preliminary results: significant increase in correlation between ranked subgroups and interestingness (measured in ranking w.r.t. unknown, true label)

  12. Summary • Fraud detection is a typical application case for local pattern detection • Statistical validity of patterns only takes you so far… • Optimization of degree of interest directly targets local patterns to user requirements

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