IBS Journal March 2016


KYC entity resolution shattered by legal fragmentation


Crushed between the threat of fines for fail- ing to prevent money laundering and legal paralysis of data, the banking industry is hard pressed to deliver on both sets of ob- ligations. By 30th June 2017, European busi- nesses will be expected to comply with the European Union’s fourth Anti-Money Laundering Directive, for transactions of €10,000 or more, with penalties of up to €5 million or 10% of annual turnover. Howev- er, the levels of due diligence around cus- tomer identification that will be required in Europe cannot paper over the cracks that exist in the global fight against financial crime.


Anti-money laundering regulation and


data protection laws can clash, with banks caught in the middle. As international busi- nesses, financial institutions have to com- ply with multiple regulatory jurisdictions which are occasionally very different. Yet rules that require them to verify the identi- ty of specific entities and maintain custom- er data integrity are often at odds with one another making them difficult to manage. Customer data presents a challenge


wherever it is used. The omnichannel bank, incorporating everything from mobile, internet, and even Twitter interactions cre- ates a very fragmented view of the custom- er. Providing a consistent service across multiple digital channels requires the knit- ting together of these disparate sorts of cli- ent information from multiple sources. When complying with regulations


that carry a heavy penalty for non-compli- ance, such as anti-money laundering (AML), know-your-customer (KYC) or the Foreign Account Tax Compliance Act (FATCA), the same challenge exists, with the additional element of trying to catch potential crim- inals.


“Data fragmentation may be caused by


people trying to escape detection, a single individual pretending to be multiple peo- ple, or multiple people acting as a syndi- cate or cartel,” says Alasdair Anderson, exec- utive vice president at Nordea. “You have to pull together fragmented pieces of data to build up a single picture of an individual or organisation - entity resolution. Entity resolution - the ability to be able


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to extract a single view of the customer or institution from multiple data sources is complicated by bad actors little or incor- rect data. “In these scenarios the absence of data can be as telling as data itself,” notes Anderson. Institutions who are working across


multiple, different geographies and have grown via acquisition over time have local systems deployed in different jurisdictions. For example, Goldman Sachs has approxi- mately 77,000 relational databases fulfilling a range of different functions, according to sources close to the firm. For banks that have developed such stores in isolation the data being collected will be variable across different systems and processes, making a unified view of data harder to achieve. Tony Wicks, head of sanctions testing


initiatives at interbank payment coopera- tive Swift, says: “The ability of these insti- tutions to identify a specific individual or a legal business entity across their systems does become problematic because they will not have a unique reference that ties everything back together.”


The technical challenge At a technical level moving data


around between separate systems can be expensive and clunky, while the remov- al of siloed technology for enterprise-wide platforms using a rip-and-replace model is both costly and risky. However new digital technologies are enabling a more flexible approach to storage and analysis than was possible using purely relational models. Chad Hetherington, general manag-


er for Enterprise Risk Case Management at AML specialist NICE Actimize, says: “With Big Data technology you can now look across data that is held in multiple siloes and that at least alleviates the technical challenge somewhat.” Platforms like Apache Hadoop, an


open source version of Google’s Big Data storage and retrieval software, are able to support the querying of both structured and unstructured data. This helps to over- come the need to process data from mul- tiple sources which was more challenging for relational databases. The kind of technology being


© IBS Intelligence 2016 www.ibsintelligence.com


employed is often on a par with that used by the security services when they are look for patterns across everything from a transport network to people’s use of social media. “The answer is not just sophisticated algorithms,” says Anderson. “A data-driven strategy that increases the number of data points and data sources to increase trian- gulation will produce optimum results.” In some cases that will require that


more data be gathered through reengi- neered KYC processes, or internal data is supplemented by external sources, from social and traditional media to payment instructions running across the Swift net- work. “A good algorithm will be able to pick


out a series of actors in the correspondent banking system,” he adds. “The more data you can get, the better the resolution on those entities will be and the better the pic- ture you build up of people and/or organi- sations that are interacting with the bank- ing system.”


Legal barriers Countries such as Singapore, Malay-


sia or Chile have particular confidentiality requirements, which inevitably change the way that a bank has to handle compliance and its IT systems. In some cases there is no restriction of importing data into a region, but there are restrictions on exporting it. “In a global bank you need to ask; are


you legally able to build up that picture using those data points?” asks Anderson. “There are significant jurisdictional chal- lenges moving data across borders. Even when data transfer is allowed, the algo- rithm results may only be accessible to an individual legally resident in the originating jurisdiction.” The move to Big Data technologies


provides the ability to create data lakes with less static structures around the way that data is accessed. “Instead of mov- ing data around the organisation you can move the application to the data,” says Het- herington. Yet blunt tools are often used to avoid


being non-compliant with FATCA or AML rules. Banks are frequently chastised in con- sumer news for dropping customers due to


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