So the table is a SAS dataset? And the updates are are updates/inserts that do not over-write the entire dataset? If so, then the audit trail facility may be useful, e.g. http://www.lexjansen.com/nesug/nesug03/ph/ph006.pdf ... View more

Make sure you have given sufficient permissions on the LASR server library and LASR server.   The admin manual (v7.3 - chapter 3 Security) lists the required metadata authorizations - you might want to review it to make sure everything is setup correctly.      ... View more

Here was a response from SAS employee John Stultz:   In case you have not seen this, here is an old but good paper by SAS’ Terry Woodfield (Predictive Modeling in the Insurance Industry Using SAS Software-- http://www2.sas.com/proceedings/sugi26/p013-26.pdf ) that might help give you some ideas on how to create derived fields and use them as model inputs within an Enterprise Miner process flow.    You might also find more current information/examples in Global Forum papers by searching the Online Proceedings: http://supportprod.unx.sas.com/events/sasglobalforum/previous/online.html   And as always, you can usually find a bunch of stuff by searching the internet.  For example, here is a list of some derived/binary variables that might be relevant: (https://www.researchgate.net/publication/227540405_Detection_of_Automobile_Insurance_Fraud_With_Discrete_Choice_Models_and_Misclassified_Claims)       Characteristics of the Insured/Claimant/Policy: AGE: Age of insured driver when the accident occurred LICENSE: Number of years since the insured obtained first driver’s license RECORDS: Number of previous claims of the insured COVERAGE: Third-party liability equals 1; extended coverage equals 0 DEDUCTIBLE: Existence of a deductible equals 1; otherwise equals 0 ACCESSORI: Coverage for accessories equals 1; otherwise equals 0   Characteristics of the Vehicle: VEHUSE: Vehicle for private use equals 1; other uses equal 0 VEHAGE: Age of the vehicle   Characteristics of the Accident: FAULT: Insured accepts the blame for the accident equals 1; other-wise equals 0 NONURBAN: Accident occurred in a nonurban area equals 1; otherwise equals 0 NIGHT: Accident occurred at night equals 1; otherwise equals 0 WEEKEND: Accident occurred during a weekend equals 1; otherwise equals 0 WITNESS: Existence of witnesses equals 1; otherwise equals 0 POLICE: Existence of police report equals 1; otherwise equals 0 ZONE1: Zone with high level of accidents equals 1; otherwise equals 0 ZONE3: Zone with low level of accidents equals 1; otherwise equals 0 REPORT: Existence of a suspicious textual report equals 1; otherwise equals 0. This variable indicates that the claimant reported unusual circumstances for the accident. NAMES: Same family name for insured and the other vehicle driver equals 1; otherwise equals 0. PROXIM: Accident occurred between the policy issue date and the policy effective starting date equals 1; otherwise equals 0. DELAY: Claim not reported to the company within the established period equals 1; otherwise equals 0   ... View more

Hello Onur,   I've added one suspect vehicle_id and prepared some code. This may give you a start...   data incidents; input INCIDENT_ID 1-7 VEHICLE_ORDER_NO 9 VEHICLE_ID $ 11-27; datalines; 4704002 1 JHMGD18707S205511 4704002 2 VF1LAOF0520635569 4704002 3 VF37ANFZWWP005871 4704053 1 VF32BKFWA9D002173 4704053 2 WF0HXXWPDH9C49160 4704053 2 VF1LAOF0520635569 run; proc sql; create table suspects as select vehicle_id, count(distinct incident_id) as nr_incid from incidents group by 1; create table incidents2 as select incidents.*, suspects.nr_incid from incidents, suspects where incidents.vehicle_id = suspects.vehicle_id; quit; proc print data=incidents2; run;     Hope this helps, Cheers, Eric ... View more

Actually I just want to find the second, third or fourth.. incident if min two of these parties involve again. ... View more

I just want to choose the customers who's min 2 different policy_ids and the date between ex policy end date and new policy date are greater than 1 month ... View more