Download as PDF, PPTX
Uploaded byAuditWare Systems Ltd.
Quick Wins and Shortcuts to Get the Most out of IDEA
This document provides techniques for summarizing and investigating personal loan application data from March to September 2005 using IDEA. Some key findings include identifying colleagues with high approval rates or sales volumes, customers who took out multiple loans, and loans to older customers or for high values that may require further investigation. The document also demonstrates how to convert proprietary date fields to IDEA dates and calculate time periods between events to further analyze the data.
More Related Content
Similar to Quick Wins and Shortcuts to Get the Most out of IDEA
Quick Wins and Shortcuts to Get the Most out of IDEA
- 1. Some quick winsand short cuts - a typical IDEA investigation Andrew Morton Risk MI Services HBOS 1
- 2. Some quick winsand short cuts - a typical IDEA investigation • 20% of the functionality gets you 80% of the way there • Plus, there are some little tricks we have found useful • These techniques should yield some tangible outcomes, and give you the confidence to explore more of the functionality 2
- 3. Personal Loan applicationsMarch – September 2005 ROLLNO STATE DALLOC DREPDEC BRANCH PERSNO ARRADV POSTCODE MTHSINARR APPLISCORE LOANAMOUNT SURNAME CUSTDOB Account number Loan status 4=application, 8=open, 12=declined, 16=repaid Date account allocated (coded) Date account repaid or declined (coded) Allocating branch code Personnel number of allocating colleague Amount of arrears or advances Customer‟s post code Months in arrears Application credit score Amount of loan (negative) Customer‟s surname Customer‟s date of birth (coded) 3
- 4. The initial investigation •Summarise account states • Summarise colleague performance and decline rates • Identify extremes of colleague performance • Identify multiple loans to the same customer • Identify accounts for further investigation • Convert coded dates to IDEA dates • Summarise sales volumes per month • Calculate age of customer at date of application • Stratify sales by customer age bands • Identify accounts for further investigation 4
- 5. Using a virtualnumeric field to obtain a subtotal when summarising @if(STATE = 12 , 1 , 0) Condition Action if condition is true Action if condition is false If your file has a „Yes/No‟ field you want to subtotal - @if(<fieldname> = “Y” , 1 , 0) 5
- 6. Multiple sales tothe same customer by the same colleague Key field summarisation – PERSNO POSTCODE SURNAME CUSTDOB and optionally to ID sales on the same day – DALLOC Summarise LOANAMOUNT Criterion STATE = 8 6
- 7. The initial investigation •Summarise account states • Summarise colleague performance and decline rates • Identify extremes of colleague performance • Identify multiple loans to the same customer • Identify accounts for further investigation • Convert coded dates to IDEA dates • Summarise sales volumes per month • Calculate age of customer at date of application • Stratify sales by customer age bands • Identify accounts for further investigation 7
- 8. Date conversion • HBOSdates are in the range –99999 to 99999, where –99999 is 01/01/1850 • 01/11/2005 is –43082, and 99999 will be 30/07/2397 • 01/01/1900 was -81739 • @daystod() converts the number of days since 01/01/1900 to an IDEA date • Therefore, @daystod(DALLOC + 81739) will convert the HBOS allocation date to an IDEA date value 8
- 9. SAS dates • Numberof days since 01/01/1960 • 01/11/2005 is 16741 • Days elapsed from 01/01/1900 to 01/01/1960 = 21914 • @daystod(SASDATE + 21914) will convert SAS to IDEA format • You may encounter SAS date/time format (number of seconds since 01/01/1960) • 01/11/2005 is 1893369600 • Use @daystod((SASDATE / 86400) + 21914) 9
- 10. Using dates • Elapsedtime between two events, e.g. – Service quality (account application to account opening) – Product persistence (Accounts closed within three months of opening) • @age() returns the number of days between two dates (most recent first) • Customer age at account opening could be calculated as @age(@daystod(DALLOC),@daystod(CUSTDOB)) /365.25 • Can avoid need to convert into date format and back, using (DALLOC – CUSTDOB) /365.25 • To return age at last birthday, use @int((DALLOC – CUSTDOB) /365.25) 10
- 11. Techniques and Findings •Virtual subtotal field in summaries • Virtual percentage field in summaries • Summarise by customer using unique identifiers • Identify exceptions by occurrence and value • Convert proprietary date fields to IDEA dates • Calculate elapsed time using proprietary date formats • Use virtual month field to summarise by product • Identify high-activity colleagues for further investigation • Identify colleagues with low decline rates for further investigation • Customers with multiple loans by same colleague in short time period • Quarter-end activity peaks • High-value loans to older customers for investigation • Multiple loans to older customers for investigation 11
- 12.