Use Cases

Striim Reduces Anti-Money Laundering Risk

Real-time Big Data Analytics Lets Banks Act with Agility and Immediacy to Reduce AML Exposure

Background

For more than a decade, financial institutions have faced more stringent regulations requiring detection, investigation, and reporting of suspected money laundering activities — including potential risks associated with politically exposed people and illicit financing. Regulations have forced banks globally to invest heavily in processes and technologies to monitor transactions, fulfill external and internal reporting requirements, and update and manage policies and procedures. In recent years, tighter regulations and increasing complexity of the risks have escalated anti-money laundering (AML) efforts to among the highest priorities for financial services companies: in a 2014 KPMG survey, 88% of banks said with AML regulations a top priority for senior management[1].

[1]Global Anti-Money Laundering Survey 2014.

Challenges

Even as it becomes an ever-larger priority, many banks feel it is no longer possible to meet all regulatory expectations. For one thing, the costs associated with AML fraud management are rising steeply. The challenge, banks report, is that demands on banks’ data systems outpace the systems’ capabilities, leading to dissatisfaction in the quality and accuracy of transaction monitoring.

The challenge is to identify “suspicious” activity indicative of money laundering which often requires a nuanced, several-step process involving correlating a number of various data streams in order to accurately flag a potential issue. Bringing together the right data from the right systems at the right time, to trigger alerts pose a significant challenge, especially in financial environments. Risk-averse cultures of banks and financial services companies have created “complex business processes and products, inflexible legacy systems architectures, [and] internal processes” that slow down the agility and speed with which institutions need to act in real time to detect sophisticated money laundering schemes.

Striim Solution

The Striim platform acquires data from multiple sources and brings them together in real time for analytics. To do this with accuracy and at the speed of reality, the platform can capture, aggregate, correlate, visualize and automate responses to event data of multiple formats and from many different internal and external systems — for example, associating transaction information via Change Data Capture (CDC), account information and personal identifying information from the bank’s own knowledge-based systems with historical data from other financial institutions, law enforcement or government databases.

Business (non-IT) users can examine dynamic, streaming and static data from across sources in real time. Financial institutions can design flexible easy to modify business logic that allows detection of potentially illegal or nefarious activity and immediate alerting and tracking of activities — so financial institutions can act rapidly to head off damage instead of cleaning up after it retroactively.

Benefits

Financial institutions can accelerate their real-time, anti-money laundering efforts with agility and flexibility — improving security without the heavy financial burden of risk in changing existing systems or architecture. Pull together data from inside and outside the organization into one place and allow rules-based real-time correlation and analysis — on evolving scenarios and threats. Banks are able to act more nimbly to advance their AML efforts and meet regulatory requirements to avoid financial penalties.

Industry

  • Financial Services

Realtime Use Cases

  • Anti-money laundering
  • Meet financial regulatory requirements
  • Fraud prevention

Business Impact

  • Increased regulatory compliance
  • Cost reduction
  • Real-time threat detection and risk management

Data Source

  • Bank transactions (account deposits and withdrawals, wire transfers, etc.)
  • ERP data
  • Government and law enforcement databases
  • Shared financial institution data
  • Personal identification data
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