Global banking and big data_ the challenge of anti-money-laundering compliance – cloudera vision

Although fraud and abuse are often cited as main drivers for the adoption of Hadoop in highly regulated industries, there has been relatively little focus on big data to prevent money laundering within commercial verticals. Money laundering layering definition The former White House Deputy Chief Technology Officer, Daniel Weitzner, recently told The Wall Street Journal, “[Companies have] taken it on themselves to spot fraudulent transactions. Money laundering identification requirements [They] have invested billions in incredibly sophisticated Big Data techniques…


But the understanding is the government—[and not banks]—will do the analysis to spot money laundering.”

However, a series of high-profile decisions by the U.S. Money laundering countries Department of Justice against BNP Paribas, JP Morgan Chase, Barclays, and other large, global banks resulting in multi-billion-dollar fines has brought anti-money-laundering (AML) to the top of the financial services industry’s priority list. Sentence for money laundering While the first wave of investment in big data tools and technology has heretofore been targeted at the identification and prevention of nefarious activities that lead to direct costs for banks, payment processors, and their customers, spending in the near term may likely be related to compliance with three key pieces of AML regulation:

Unlike other forms of fraud that are identified with machine learning algorithms that detect anomalies and outliers, money laundering schemes are designed to closely mimic typical banking behaviors and are, therefore, characteristically less anomalous. What is considered money laundering The thresholds mandated by reporting policies like BSA and utilized by first- and second-generation AML systems are well known, so criminals have little difficulty modeling the source of their above-board trade and transaction behaviors to be largely imperceptible, even to specialized software.

As a result, these systems must be enriched with much larger and more diverse data sets to isolate signals of possible money laundering. News on money laundering When a signal is detected, human judgment must be applied—a case is opened, which kicks off an inquiry to verify the crime and the extent of the damage. What constitutes money laundering Without big data, the AML indicators are often not sufficiently distinct to be caught by computational models and leave most of the work to a time-consuming and expensive investigation. Federal anti money laundering regulations In fact, respondents to KPMG’s 2014 Global Anti-Money Laundering Survey reported they are “increasingly unhappy with their current automated monitoring efforts, [and are] looking for software that can reduce the burden on the compliance department.”

Apache Hadoop is the ideal platform for AML because it augments all of the core functions of a specialized system to better handle big data: data collection, data preparation, automated evaluation, model building, and investigation. Uk anti money laundering The modern AML architecture is fully integrated with an enterprise data hub, with Hadoop initially staging massive complex data for legacy solutions to provide runtimes for the predictive models and perform the actual fraud detection. Money laundering cases in canada Beyond the introductory use case of more expansive and affordable storage, Hadoop’s natural fit for backtesting against long-term descriptive data is gaining popularity for more advanced AML workloads, as is the use of other components in the Hadoop stack for exploration, discovery, investigation, and forensics.

Data Collection. Money laundering mauritius Bank data tends to be segregated into silos, and modeling is usually limited to a few weeks or months. Money laundering structuring In contrast, the cost of storing data on Hadoop is typically orders of magnitude lower than every other alternative, meaning data spanning decades can easily and affordably be retained and queried in one place.

Data Preparation. Money laundering pdf Hadoop excels at data enrichment, transformation, and vectorization prior to being scored for fraud. Money laundering phases It enables heuristic matching algorithm required to prepare certain types of data and integrates with familiar ETL tools while Hadoop handles the heavy data collection, transformation, and preparation.

Fraud Scoring. Money laundering uk Access to a variety of predictive models improves the accuracy of fraud models. Money laundering terms Hadoop’s support for multiple frameworks can bring multiple computational techniques to bear on the AML problem, including static rules engines, state machines, graph algorithms, natural language processing, and machine learning.

Model Development. What is laundering money Criminal methods evolve to evade detection, requiring predictive models to be improved over time. Anti money laundering in india While some models are relatively static, others use techniques like linear regression and clustering, which require training from a historical data set. Anti money laundering certification free Interactive query tools like Cloudera Search and Impala facilitate the discovery of new patterns and associations while the availability of more data and processing power in Hadoop allow models to incorporate more parameters, train on longer historical perspective, and iterate more rapidly when backtesting new variations.

Investigation. Laundering money through casinos Improving model accuracy to eliminate false positives, thereby reducing the time- and resource-intensive caseload for the human element of investigation, is a major way Hadoop decreases the cost of AML. Anti money laundering techniques As part of an enterprise data hub, ad hoc interactive query reduces the burden of investigation by providing fast answers to arbitrary questions over large data sets.

As part of an enterprise data hub, Hadoop’s flexibility, scalability, and affordability are extending existing investments in dedicated fraud-detection solutions by increasing the volume, age, and variety of data that can be examined while speeding up data transformation for faster time to insight. Examples of anti money laundering Once such massive data is consolidated, Hadoop can increasingly take on more advanced AML workloads such as entity matching while Cloudera Search and Impala remove the complexity of model development, process automation, and case investigation.

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Although fraud and abuse are often cited as main drivers for the adoption of Hadoop in highly regulated industries, there has been relatively little focus on big data to prevent money laundering within commercial verticals. Money laundering placement The former White House Deputy Chief Technology Officer, Daniel Weitzner, recently told The Wall Street Journal, “[Companies have] taken it on themselves to spot fraudulent transactions. Fraud and money laundering [They] have invested billions in incredibly sophisticated Big Data techniques… But the understanding is the government—[and not banks]—will do the analysis to spot money laundering.”

However, a series of high-profile decisions by the U.S. Anti money laundering examples Department of Justice against BNP Paribas, JP Morgan Chase, Barclays, and other large, global banks resulting in multi-billion-dollar fines has brought anti-money-laundering (AML) to the top of the financial services industry’s priority list. Money laundering uae While the first wave of investment in big data tools and technology has heretofore been targeted at the identification and prevention of nefarious activities that lead to direct costs for banks, payment processors, and their customers, spending in the near term may likely be related to compliance with three key pieces of AML regulation:

Unlike other forms of fraud that are identified with machine learning algorithms that detect anomalies and outliers, money laundering schemes are designed to closely mimic typical banking behaviors and are, therefore, characteristically less anomalous. Money laundering acca The thresholds mandated by reporting policies like BSA and utilized by first- and second-generation AML systems are well known, so criminals have little difficulty modeling the source of their above-board trade and transaction behaviors to be largely imperceptible, even to specialized software.

As a result, these systems must be enriched with much larger and more diverse data sets to isolate signals of possible money laundering. Money laundering control act When a signal is detected, human judgment must be applied—a case is opened, which kicks off an inquiry to verify the crime and the extent of the damage. Uk anti money laundering legislation Without big data, the AML indicators are often not sufficiently distinct to be caught by computational models and leave most of the work to a time-consuming and expensive investigation. What does money laundering accomplish In fact, respondents to KPMG’s 2014 Global Anti-Money Laundering Survey reported they are “increasingly unhappy with their current automated monitoring efforts, [and are] looking for software that can reduce the burden on the compliance department.”

Apache Hadoop is the ideal platform for AML because it augments all of the core functions of a specialized system to better handle big data: data collection, data preparation, automated evaluation, model building, and investigation. Money laundering laws uk The modern AML architecture is fully integrated with an enterprise data hub, with Hadoop initially staging massive complex data for legacy solutions to provide runtimes for the predictive models and perform the actual fraud detection. Money laundering documents Beyond the introductory use case of more expansive and affordable storage, Hadoop’s natural fit for backtesting against long-term descriptive data is gaining popularity for more advanced AML workloads, as is the use of other components in the Hadoop stack for exploration, discovery, investigation, and forensics.

Data Collection. Certification in anti money laundering Bank data tends to be segregated into silos, and modeling is usually limited to a few weeks or months. Money laundering examples uk In contrast, the cost of storing data on Hadoop is typically orders of magnitude lower than every other alternative, meaning data spanning decades can easily and affordably be retained and queried in one place.

Data Preparation. Anti money laundering organizations Hadoop excels at data enrichment, transformation, and vectorization prior to being scored for fraud. What is trade based money laundering It enables heuristic matching algorithm required to prepare certain types of data and integrates with familiar ETL tools while Hadoop handles the heavy data collection, transformation, and preparation.

Fraud Scoring. Anti money laundering legislation uk Access to a variety of predictive models improves the accuracy of fraud models. Best money laundering schemes Hadoop’s support for multiple frameworks can bring multiple computational techniques to bear on the AML problem, including static rules engines, state machines, graph algorithms, natural language processing, and machine learning.

Model Development. Anti money laundering association Criminal methods evolve to evade detection, requiring predictive models to be improved over time. Money laundering red flags While some models are relatively static, others use techniques like linear regression and clustering, which require training from a historical data set. What is money laundering yahoo Interactive query tools like Cloudera Search and Impala facilitate the discovery of new patterns and associations while the availability of more data and processing power in Hadoop allow models to incorporate more parameters, train on longer historical perspective, and iterate more rapidly when backtesting new variations.

Investigation. Meaning of anti money laundering Improving model accuracy to eliminate false positives, thereby reducing the time- and resource-intensive caseload for the human element of investigation, is a major way Hadoop decreases the cost of AML. Money laundering how to As part of an enterprise data hub, ad hoc interactive query reduces the burden of investigation by providing fast answers to arbitrary questions over large data sets.

As part of an enterprise data hub, Hadoop’s flexibility, scalability, and affordability are extending existing investments in dedicated fraud-detection solutions by increasing the volume, age, and variety of data that can be examined while speeding up data transformation for faster time to insight. Money laundering regulations 2003 Once such massive data is consolidated, Hadoop can increasingly take on more advanced AML workloads such as entity matching while Cloudera Search and Impala remove the complexity of model development, process automation, and case investigation.

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