Error Tolerant Matching Acoustic Errors Mr Haseez Abbafi 99 Woodheyes Rd London NW10 9DE Mr Hafeez Abbasi 99 Woodheyes Rd London NW10 9DE Semantic and

Error Tolerant Matching

• Acoustic ErrorsMr Haseez Abbafi

99 Woodheyes Rd London NW10 9DEMr Hafeez Abbasi

99 Woodheyes Rd London NW10 9DE

• Semantic and Transposition ErrorsMrs Deolinad Abao

1 Station Rd Barnet Hertfordshire EN5 1NP Miss Deolinda Adao

Basil Court 1 Station Rd Hertfordshire EN5 1NG

RCP Consulting LimitedSuite B, 15th Floor, Casey Building,38, Lok Ku Road, Sheung Wan,Hong KongTel: 2815 9911Fax : 2815 0611E : [email protected]

Real World Applications

• Data quality assessments• Single view of customer• Customer de-duplication• Data migration• Clean, match and merge• Vendor screening• Forensic accounting• Fuzzy search/retrieval• Address cleaning• Anti money laundering• Denied persons


Real Time Fuzzy Search

• ObjectiveIdentification and validation of university students making loan

applications with third party lenders

• ChallengeComplex and sensitive search criteria to identify as many

applicants as possible without performance deterioration. Rules are client configurable

Encompassing periods of extreme volume processing volatility

• DeploymentWeb Service front end plus scalable cluster

• ResultGuaranteed 7 sec response day in day out. 24 x 7.


Corporate Fraud• Objective

Detect potential fraudulent vendors

• ChallengeCompanies that have a large number of vendors are at risk of

fraud. The impact of any one specific fraud is unpredictable

• DeploymentThe vendor lists were compared firstly to the employee list and

secondly to a mail box drop/pornography shop list

• ResultResults suggests that 1.5% of all vendors are fraudulent. On

one account that led to the successful investigation of 250 targeted vendors


Anti Terrorist Application

• ObjectiveDetect individuals on the OFAC list

• ChallengeUS companies may not deal with individuals, companies

and vessels on the OFAC list nor with specific countries

• DeploymentRules were configured to allow maximum visibility of all

possible matches

• ResultIn one account 15 entities were uncovered.The US Patriot act enforces a $10m fine per entity


Denied Persons List

• ObjectiveDetect individuals on the US Denied Persons list

• ChallengeUS companies funded by government may not deal with

individuals, companies that have defrauded the government. The denied persons list also includes barred nurses and doctors

• DeploymentRules were configured to allow maximum visibility of all

possible matches

• ResultIn every account examined entities were uncovered


Duplicate Invoices

• ObjectiveDetect duplicate invoice payments

• ChallengeCompanies with large purchase order ledgers are vulnerable to

paying an invoice more than once.

• DeploymentRules were configured to allow maximum visibility of all possible

matches.

• ResultIn one account GBP 1.8m was discovered to be multiple paid.

One vendor had triple billed their client for an entire quarter. One invoice was paid seven times. Their accountants had double billed them as well…


Documents

Error Tolerant Matching Acoustic Errors Mr Haseez Abbafi 99 Woodheyes Rd London NW10 9DE Mr Hafeez Abbasi 99 Woodheyes Rd London NW10 9DE Semantic and