Analytics to streamline anti-money laundering

Analytics to streamline anti-money laundering

Risk professionals are always concerned about the known, unknown and unknowable across their firms – especially when it comes to money laundering activities within their firms. Money laundering – the process of hiding the illicit origin of money – has been on the rise for years.

If organisations fail to swiftly detect and curb money-laundering activities, its executives and board will risk negative publicity, reputation, along with aggressive fines and penalties. Over the past few years, numerous firms have been fined hundreds of millions – and in some cases, more than a billion dollars – over AML lapses, which excludes the remediation costs of increasing staff and hiring consulting firms to assist in the effort.

Identifying threats is getting harder. It requires increasingly sophisticated analytical tools and data visualisations that make it easier to identify and understand new and evolving threats.

In response to the growth in money laundering and terrorist financing activities worldwide, regulators have stepped up compliance mandates. Firms are quickly finding themselves under oversight scrutiny. Because of enhanced regulatory pressure to continuously evaluate the firm’s risks, identify emerging trends, report suspicious activity and expediently make changes, firms are seeking out new and aggressive approaches.

Today, it’s no longer enough to use standard technologies and controls and accept any undetected money laundering as part of doing business. Rather, regulators are requiring firms to be more proactive, innovative and thorough – for example, by using big data analytics and visualisations to uncover new and emerging risks. These technologies make it easier to pinpoint more indicators of risk – including indicators that may not have been visible before. They also eliminate guesswork and enable earlier detection.

But progress toward meeting these requirements is being hindered by many challenges. For example, data is typically scattered across different systems, with no sole source of truth readily available. This makes it difficult to analyse data using traditional AML tools; the process takes too much time and resources to get the job done. By the time anomalies indicating emerging risks are detected, damage has already been done.

In addition, risk managers typically rely on static spreadsheets and reporting, which aren’t designed to help people detect anomalies and quickly find “the needle in the haystack.” Plus, static reports and spreadsheets can’t be used to provide fast answers to the complex questions being raised by executives today.

To meet these demands, the AML industry has turned to analytical/statistical methodologies to improve monitoring programs by reducing false-positive alerts, increasing monitoring coverage, and reducing the rapidly escalating financial cost of maintaining an AML program.

Already struggling to control costs, firms are continuously scrutinising the economic cost to perform AML compliance. AML officers are judged not only on their ability to react to regulatory changes and quickly implement solutions, but also their accountability for AML program expenses. AML officers are increasingly required to play multiple roles, and it takes real leadership to balance and manage these expectations.

Segmentation is the logical first step.

A typical anti-money laundering (AML) transaction monitoring program has scenarios that monitor the customers and accounts that pose the most risk to the institution. The one-size-fits-all methodology isn’t very effective. That’s because customers transact differently based on many factors.

An effective AML transaction monitoring strategy begins with a sound foundation for monitoring customer activities − and a quality segmentation model provides just that foundation. Banks can begin with segmenting the customer base by analysing customer activity and risk characteristics.

Segmentation is the primary foundation for risk-based scenario threshold setting, and the quality of the segmentation model directly affects the transaction monitoring system’s ability to perform in an effective and efficient manner.

The SAS approach – The SAS approach to segmentation generally requires three primary activities:
1. Customer, account or external entity population segmentation (or a combination thereof).
2. Further refinement of individual segments into peer groups (only needed if anomaly detection will be performed).
3. Initial threshold setting (needed to assign the scenario threshold parameter values to use initially prior to the first scenario tuning and model verification project).

In addition, SAS adheres to the guiding principles of OCC 2011-12 when developing, implementing and validating segmentation and peer group models, including the process of initial threshold setting.

Clearly, financial institutions of all types and sizes need to beef up their BSA compliance efforts. The challenge is that high transaction volumes from online and mobile banking services give criminals considerable cover for money laundering schemes. Identifying a suspicious transaction is like finding a needle in a haystack.

Saying so, Chartis Research, a leading provider of research and analysis on the global market for Risk Technology has ranked SAS as the ‘Category Leaders’ for Anti-Money Laundering Solutions in the RiskTech Quadrant 2017.

The report states how SAS supports banks across a range of fraud and financial crime risks, via a comprehensive suite of solutions delivered through a global network of professional services and system integration partners. It also mentions the differentiating elements of SAS’s solution which include the significant investment in financial crime risk management that underpins it, and its enterprise approach as well as SAS’s data aggregation capabilities, combined with analytics and visualisation, to support a holistic view of financial crime risk management.

Rapidly increasing risk – combined with evolving government regulations – requires an advanced strategy when it comes to monitoring data for illicit activity. With SAS’s risk-based approach, firms can manage alerts easily, test scenarios and comply with industry regulations:

Get quick, accurate alerts: Know if suspicious activity is happening. Manage alerts from a centralised system, making it easier to preserve data security, minimise IT support costs and promote collaboration across the enterprise

Easily track flow of funds: Enables to see debits and credits, as well as variation in volume of funds between entities.

Be transparent. And compliant: Automatically monitor suspicious behavior, document the decision process and, if applicable, file pre-populated reports with the appropriate authorities.

Find the best scenario and take the best action: SAS high-performance visualisation tools significantly reduce the time required to analyse data, visualise patterns, hypothesise monitoring strategies and validate scenario deployments.

Instantly access the information you need: In an industry that moves fast, you don’t need complicated user interfaces slowing you down. SAS Anti-Money Laundering technology has an interface that’s designed to facilitate quick, accurate decisions – which means all the information you need is one or two clicks away.

SAS is Just In Time Group’s (JIT) strategic ICT Partner in Sri Lanka, who is dominant leader in ICT Systems and Solutions Integrator to the country. JIT’s 21+ Year success with their longstanding customers, strategic principals and committed employees have been the catalyst for JIT’s success and bringing many landmark projects with cutting edge technology to the country and making Sri Lanka’s mark in the global ICT map.

JIT’s key strengths are focused on systems integration, software solutions, network and infrastructure, hardware, mobility, outsourcing IT professionals, professional services, information security services, maintenance and support services, etc.

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