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Next Generation Anti-Money Laundering & Compliance Powered by Business Rules, Artificial Intelligence & Machine Learning

Firms large and small need to navigate a set of increasingly complex compliance rules and regulations as regulatory bodies continue to change the established financial regulatory frameworks. With tighter regulation comes the need to seek out more advanced and cost effective compliance solutions.

AML and Compliance Challenges

Despite the introduction of tougher legislation over recent years, money-laundering and financial scandals continue to dominate global news. As the financial services sector falls under increasing scrutiny, banks are mandated to implement full measures to prevent financial crimes. Regulators increasingly require greater oversight from institutions including closer monitoring for anti-money laundering (AML) compliance. Failure to put in place a comprehensive compliance program means risking large fines, depreciated share value, costly legal battles, and reputational damage.

This whitepaper details these shortfalls, and demonstrates how Brighterion’s unique suite of advanced artificial intelligence and machine learning technologies are the ideal AML solution.

Legacy AML Approaches are Ineffective

Traditional AML tools are based on business rules and models trained using historical data. However, these approaches suffer from several important limitations when attempting to identify money-laundering activities.  Most institutions are facing these common issues:

  • Too many false positives
  • Single solutions and corporate silos that increase the risk of violations going undetected
  • High IT costs
  • Current analytic operations that are narrowly focused, only able to view limited data, which increases the risk of undetected suspicious activity or high levels of false positives.
  • Difficulty acting nimbly to counter changing threats and advances in technology and data
  • Personal and corporate names are often represented differently in different systems used by different business units and regions or
  • Many patterns of transactions associated with money laundering differ little from legitimate transactions. Most illegitimate uses of wire transfers mirror standard business practices. They are recognizable only because of their association with criminal
  • Money launderers change their modes of operation frequently. If one method is discovered and used to arrest and convict money launderers, activity will switch to alternative methods. Business rules are not adaptive.

A Comprehensive AML Approach to Ensure Constant Compliance

Standard business rule management systems allow companies to define, deploy, and monitor rules to meet new regulatory challenges. These rules represent the experience of the institution in fighting money laundering, and must be maintained in an ever-changing regulatory landscape. To address these modern challenges, Brighterion integrates customers’ existing Rules as the core of their platform, enhancing these rules with Fuzzy Logic and artificial intelligence technologies including supervised and unsupervised learning. These technologies are being used by customers all over the world to address their institutional risk and compliance challenges.

Brighterion’s Business Rule Management System (BRMS) system draws on decades of experience in AML and is designed with a variety of regulatory frameworks in mind. Brighterion BRMS allows non-IT staff to write, modify, and deploy rules sets through drop-down menus without the involvement of IT staff. It also incorporates fuzzy logic, reason codes, a role-based management system, reporting tools, and has full compatibility with Brighterion’s iMonitor system which analyzes rule performance and suggests effective new rules. These technologies have been designed into the Brighterion system in order to address the wide variety of AML scenarios Brighterion has encountered over years of helping customers deter money laundering in the ever-changing financial regulatory system.

 

 

Supervised Learning Models

Business rules written, tested, and deployed by experts in the field create a first-pass detection method for known types of malicious behavior. One of the ways that Brighterion enhances customers’ Rules systems is through Supervised Learning, producing well-defined classification models from the patterns in a large set of past labelled behaviors. Brighterion’s Supervised Learning models incorporate its years of experience delivering AML solutions by adding automatic data cleaning and enrichment tools. These tools remove the need for teams of data scientists to prepare datasets for use, providing customers with enhanced data regardless of their dataset’s format or volume.

Unsupervised Learning Models

As historical data related to money laundering is scarce and unreliable, it is vital to utilize unsupervised learning technologies which have the ability to gain insight from the data without any prior knowledge of what to look for. Unsupervised learning is learning from unlabeled data, where particularly informative privileged variables or labels do not exist. As a result, the greatest challenge is often to differentiate between what is relevant and what is irrelevant in any particular dataset.

Unsupervised learning also encompasses dimensionality reduction, feature selection, and a number of latent variable models. While first-pass solutions often use business rules, the combination of these painstakingly tested and verified rules with the power of unsupervised learning technology empowers these initial solutions with far greater accuracy.

Brighterion’s unsupervised learning tool, iLearn, utilizes temporal clustering, link analysis, associative learning and other techniques to allow customers to track transaction volatility, entity interactions, behavioral changes, etc. The power of unsupervised learning for detecting money laundering shines when data from a multitude of sources can also be ingested by the system. Having a system flexible enough to accept multiple data points across a variety of sources is essential in tracing the full behavior of the individuals and the money/assets laundered.

iComply: Brighterion’s Compliance Solution

iComply automatically monitors your entire transaction environment and involves compliance personnel only when significant patterns have been found. It offers:

  • Suspicious behavior monitoring: Customer’s Rules are used as the core of the solution, and enhanced with Supervised and Unsupervised learning models to detect new types of suspicious behavior while incorporating feedback from investigators.
  • Powerful Multi-Layer analysis: detect any abnormal behavior. For example, in the context of wire transfer, multi-layer analysis is required. First is the transaction layer securing individual transactions such as currency deposits and withdrawals, wire transfers, and checks. Second is the individual or account layer. Multiple transactions are associated with specific individuals and bank accounts. Third is the business or organizational layer. Fourth is the “ring” layer which involves multiple businesses, accounts, and individuals in a money laundering scheme.
  • 1-to-Many and Many-to-1 Behavior Analysis: Individualized models track the behavior of each entity in the data, tracing their patterns to discover and identify anomalous activities
  • Dashboards and reports

KYC: Brighterion’s Watch List Screening Solution

In addition to AML monitoring, Brighterion offers a KYC solution that handles Watch List Screening with daily updates of sanction lists. It incorporates phonetic and string matching algorithms for accurate identification regardless of the quality of data entry, and includes AI-based algorithms for unsupervised learning and clustering to reduce false positives.

  • Customer screening with hundreds of worldwide watch lists (OFAC, etc.) and PEP list.
  • Customer identification program (entity resolution with clustering)
  • KYC scoring (customer onboarding and lifecycle score)
  • KYC dashboards and reports
  • Customer screening based on bank’s own “White and Black lists”
  • Screening of new and existing customers, transaction initiators, beneficiaries and intermediaries
  • Alert generation, prioritization, workflow and management

iSupervise: Brighterion’s Case Management Solution

iSupervise is Brighterion’s all-encompassing Alert Management platform which provides customers with more efficient and accurate alerts while providing surrounding contextual information and analysis. It offers:

  • Linking of customer relationships, by transaction flows, whether “Household”, explicit, implicit, or hidden, to optimize resource deployment and reduce “false positives”
  • Comprehensive drill-in/drill-out capability to complete investigations, in a timely and efficient manner
  • Transaction alert investigation, alert escalation, enhanced due diligence, relationship analysis.
  • Advanced work-flow and case management, covering a broad range of end users, products and services, with a facility for tracking, follow-up, resolution, reporting, and audit trail documentation
  • Accurate timely SARs/CTRs filing, whether electronically or manually filed, within regulator-prescribed windows

In conclusion, Brighterion helps financial institutions achieve compliance with regulations, mitigates operational and reputational risk, avoids fines and penalties, prevents losses, and minimizes exposure to money laundering and fraud. While many banks use outdated legacy systems and manual AML procedures, Brighterion provides next generation solutions in AML by leveraging unsupervised learning, behavioral profiling, and a platform creating customized models for your business needs. With Brighterion, institutions see lower back office costs, reduction in loss to money laundering, and peace of mind for their compliance department.

The post Next Generation Anti-Money Laundering & Compliance Powered by Business Rules, Artificial Intelligence & Machine Learning appeared first on Brighterion.

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