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Customer Information Data Management Systems | Pitney Bowes

Entity resolution for sanctions screening starts with clean, consolidated, de-duplicated data

By Jim Burnick, Managing Director Financial Services, Pitney Bowes

Learn how banks can be proactive with regulators and improve customer experience along the way.

A new executive brief from Pitney Bowes  discusses how entity resolution technologies can help alleviate the investigative burden of sanctions screening and watch-list filtering. However, before you start consolidating entities for a single view of the customer, you might want to think about the quality of data being ingested into your system. Many financial institutions (FIs) find it difficult to get a holistic, party-level customer view across the enterprise and then connect it with Know Your Customer (KYC) data. This includes sanctions/screening data and list data FIs have generated internally.

Today's FIs buy a plethora of screening lists and negative-media data from a range of providers. One name may appear on multiple lists. If a customer uses different name variations (Jim Bob Jones vs. James Robert Jones, for example) the same entity may appear multiple times on each list. For example, you may see the same customer on a politically exposed persons list and on an Office of Foreign Assets Control list under the same name, or find the same person with different names on the same list. This situation can dramatically increase the number of hits that require investigation.

FIs may have similar data-quality issues in their own repositories. James Robert Jones may have several accounts in different, siloed lines of business. For example, he may have a 15-year-old credit card account, and the address on file is two moves old. A clerk may have accidentally transposed the final two digits of the telephone number he used when opening his savings account. This error remains part of that record. His savings account application may list his ZIP code as 10034, his brokerage account as 10040.

Incomplete and inaccurate customer information must be resolved – and duplicate information consolidated – before FIs can begin that type of entity resolution that leads to a single view of the customer.

Calling upon nearly a century's worth of experience in data structuring and linkage, Pitney Bowes now offers software solutions that aid banks in verifying and consolidating customer information, both in their own databases and in the screening lists and negative-media lists they purchase. Capabilities such as sentiment analysis and artificial intelligence coupled with list management, intelligent mapping between party and list, and configurable weighting of filtering/matching rules can reduce alerts and improve investigative effectiveness.

These solutions first comb the FI's disparate systems and data silos to consolidate information on a single customer, fixing data quality issues along the way.

Consider the scenario of James Robert Jones vs. Jim Bob Jones. Once the software solutions consolidate enough information on these entities to determine they are a single party (finding name variations share a social security number and two instances of the same address); the software not only consolidates these names into a single entity and appends that entity with a unique identification number, but retrofits existing customer records with more holistic and accurate data. If every other account belonging to James Robert Jones lists his phone number as ending with the digits 86, and the record attached to his saving account lists it as ending in 68, the software can automatically fix that error. Location intelligence capabilities can resolve the question about the customer's ZIP code, and standardize it to the correct 10034 in all client records. And if recent account openings indicate that Jones lives at 45 Main Street, it can update the 15-year-old address associated with his credit card account.

Pitney Bowes can also act as a data aggregator for screening lists purchased by the FI. Software solutions can de-duplicate entries appearing on these lists. Comparing consolidated FI customer entities against de-duplicated lists saves significant investigative time.

These solutions can further help refine information found on negative media lists. Suppose James Robert Jones has been charged with shoplifting. After reading one article on the subject, bank investigative units may determine that Jones was acquitted of the crime, or that the crime was so minor it would unlikely affect his relationship with the bank. It can then write a computational rule telling its screening programs to ignore all other negative media reports of the shoplifting incident or add this incident to an internal “white list." Conversely, if after reading an article saying that Jones was charged with criminal racketeering, the FI may choose to spend significant investigative time researching this charge and its potential effect on the bank.

Correcting, completing and de-duplicating customer data is a vital first step in developing the type of single view of the customer that can improve Know Your Customer and Customer Due Diligence (CDD) processes, reducing operational risk. This can make banks more productive and proactive with regulators, and improve customer experience along the way.

For more information on Entity Resolution and streamlining sanctions screening, view our Executive Summary now.

Visit us online: pitneybowes.com/us/industry/financial-services.html#AML

 

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