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Cape Town Office: Silvermine House,Steenberg Office Park, Tokai, 7945Tel: +27 (0) 21 702 4111
Johannesburg Office: Office 2, First floor, building 16, The Woodlands, Woodmead, Sandton 2148Tel: +27 (0) 11 656 0915
Lisbon Office: Rua das Chagas 20 R/C E 1200-107Tel: +35 (0) 12 1340 5190
PREDICTIVE INSURANCE SOLUTIONS
www.olsps.comINSURANCE CLAIM SEGMENTATION
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OVERVIEW
• Segments all incoming short term insurance claims
• Ranks claims on likelihood of fraud (0-1)• Low risk = Fast track• High risk = Investigation
Low Risk ClaimsFast Track Channel
Medium Risk ClaimsNormal Assessment Channel
High Risk ClaimsSIU Channel
0 1Risk Score
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• Direct Claims to the correct assessment channel at First Notification of Loss (FNOL). Basic channels are:• Fast Track (lowest cost processing channel)• Normal Assessment Channel (medium cost processing channel)• Forensic Assessment/SIU (high cost processing channel)
• The Goal is Two-Fold:
• Increase the proportion of claims that are fast tracked, thereby reducing processing costs
• Improve profitability of forensic unit
• Spin-off is increased Client Satisfaction
OVERVIEW
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SIMPLIFIED LOOK AT THE MECHANICS OF THE SOLUTION
CLAIM VARIABLES SEGMENTATION OUTPUT• Driver’s profile• Type of car• Time of accident• …• …• …
Business Rules
Predictive Models+
Low Risk
Medium Risk
High Risk
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CALCULATION SEQUENCE
Claim Reg Score Request
P.A Models (2)
Risk Rules
P.A Model Score
Rule Score
Risk Matrix
Risk Score(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0,9, 1.0)
Value Risk
Trade-OffInitial
SegmentOverride
RulesSegment
Recommendation
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CONVERSION OF TWO DIMENSIONS OF RISK TO ONE
Highly Decrease
dDecrease
d None Increased Highly Increased
None 0.1 Low Risk
0.2 Low Risk
0.4 Low Risk
0.5 Medium
Risk
0.6 Medium
Risk
Low 0.3 Low Risk
0.3 Low Risk
0.4 Low Risk
0.5 Medium
Risk
0.7 Medium
Risk
Medium0.5
Medium Risk
0.5 Medium
Risk
0.6 Medium
Risk
0.7 Medium
Risk0.8 High
Risk
High0.7
Medium Risk
0.7 Medium
Risk0.8 High
Risk0.9 High
Risk1.0 Send
to SIU
PREDICTIVE ANALYTICS SCORE
JUDG
EMEN
T RU
LES
SCOR
E
0 1
Min
Max
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VOLUME SIMULATIONS – SEGMENT THRESHOLDS
Predictive Models Risk Score
Busin
ess R
ules
Risk
Sco
re
% CLAIM IN EACH RISK LEVEL
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VALUE PROPOSITION: SIU & FAST TRACK
0 5000 10000 15000 20000 25000 30000 35000 40000 450000.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
% FRAUD
ILLUSTRATIVE FRAUD EXISTING
ILLUSTRATIVE FRAUD FUTURE
CLAIMS RANKED BY FRAUD SCORE DESCEND-ING
ACTU
AL F
RAUD
% :
AVER
AGE
FRAU
D %
X 1
00
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BENEFITS OF THE SOLUTION
SIU CLAIMS
ORDINARY CLAIMS
FT/EXPRESS CLAIMS
Increased Repudiations
Reduced Handling Costs
Increased Customer Satisfaction
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INSURANCE CLAIM SEGMENTATION USE-CASE
www.olsps.comSUPPLIER RANKING
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PROBLEM DESCRIPTION
• Large insurers often use in excess of 1 000 MBRs to facilitate repair
• Costs and capabilities of MBRs vary greatly
• Detailed information available on claims & costs
We need to develop a way to leverage the information that we have in order to unlock the cost-saving potential
POTENTIAL FOR COST SAVING
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MARK UP BASED APPROACH
• Mark up % on each component of repair is obtained from each MBR
• Score is calculated for each mark up %
• Overall discount offered by MBR is also considered
• All components are combined into an overall score
EMPIRICAL APPROACH USING PREDICTIVE ANALYTICS• Each repair done by each MBR
is considered over a time period
• Repairs are ‘normalised’ so that different repairs can be compared to each other
• Repair cost per MBR is averaged over all its repairs
• Average repair cost per MBR is used to obtain relative cost effectiveness
TWO METHODS TO TACKLE PROBLEM
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MBR 1• Labour cost = R500/hr• Fairly allocates labour
to jobs• Repairs parts where
possible
MBR 2• Labour cost = R500/hr• Over-allocates labour
to jobs• Replaces all parts with
new
POSSIBLE SHORTCOMING OF MARK UP APPROACH
MARK UP BASED SYSTEM SEE THESE AS EQUIVALENT INTRODUCING ROOM FOR EXPLOITATION
PREDICTIVE APPROACH USES ACTUAL COSTS CHARGED – IMPOSSIBLE TO EXPLOIT
VS
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RANKING GRAPH
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RANKING GRAPH
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SUPPLIER RANKING USE-CASE
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NOTES