Assistant Vice President of Corporate Security, Laurentian Bank
How the big picture helps fight financial crime
Laurentian Bank pulls together data to combat fraud and money laundering
Financial crime costs Canadian financial institutions more than $1 billion a year. And while banks’ analytic systems are often very effective at detecting and shutting down suspicious transactions through a single channel, patterns of criminal activity are often very sophisticated and involve more than one channel, account or identity. With myriad touch points, including: transactional accounts, credit card accounts, mortgages and other financial instruments, along with falsified addresses, phone numbers and other identification information, making connections between the players and their games very complex.
Laurentian Bank of Canada, which manages more than $33 billion in balance sheet assets and has 3,800 employees across Canada, is redefining the standards for managing suspicious banking activity by integrating three critical components of SAS® Enterprise Financial Crimes for Banking, which allows the bank to pull together the “big picture” of networks of fraudulent transactions and the identities associated with them. It is the first bank to bring together three financial crime solutions: SAS® Anti-Money Laundering, SAS® Fraud Network Analysis and SAS® Enterprise Case Management – to identify potential fraud and money laundering more quickly and effectively.
Cross-referencing across several cases and identities tells you you’re dealing with a criminal network.
Achieving a comprehensive view of fraud
Combining SAS Fraud Network Analysis into the mix provided the bank with the ability to uncover unknown relationships amongst its customers, accounts and businesses through the use of link analysis technology upon bank data. This solution enabled the visualization of the relationships identified between entities, uncovering unknown relationships while helping to identify the use of synthetic IDs that may use a common piece of identifying information – a popular tool for the analysis of the card application process to better understand and confirm the identity and creditworthiness of the applicant.
“SAS Fraud Network Analysis allowed us to move from a transaction and product view of fraud risk to a complete view of fraud risk at the customer level,” affirmed Isabel Rainville, Senior Manager of Fraud Prevention for Laurentian Bank.
Before the bank combined the power of all three crime fighting solutions it used a case management system developed in-house, referred to as GDI. While this system was effective in its own way, it had limitations, particularly with respect to associating identities and cases. For example, if a subject’s identity had multiple phone numbers, addresses or other identifying information, separate subjects had to be created for each instance. And subjects could only be associated with a single case; new subjects had to be manually created, with all that duplicate information, for each case the subject was associated with.
With SAS Enterprise Case Management, a single unique subject can have multiple addresses, phone numbers, spellings of names and so on, folded into a single identity. And that identity can be associated, in a variety of roles – subject, victim or witness – across the various cases or incidents that are relevant. “Cross-referencing across several cases and identities tells you you’re dealing with a criminal network,” says Robert Quevillon, Assistant Vice President of Corporate Security for Laurentian Bank.
By unifying that data under SAS Enterprise Case Management, analysts can view in a single window the relationships between subjects and cases. But the unprecedented marriage of the three solutions was challenging. SAS and Laurentian Bank worked hand-in-hand to bring the components together, creating a blueprint for other companies in the financial services sector.
A year into the project, measuring the return on investment is easier on the fraud side of the house than when dealing with money laundering crimes. Fraudulent traffic can be detected in near-real time – but by its very nature, money laundering can only be detected after the fact. “We’re not real-time,” says Celine Demers, Manager of Anti-Money Laundering Solutions at Laurentian Bank. But by getting suspicious transaction reports and unusual transaction reports – whether they’re automatically generated or flagged by bank staff – into SAS Enterprise Case Management faster, analysts can make the call on whether an account should be shut down faster. Such decisions took six to eight days using the previous system. Now they’re made in about two hours, limiting the bank’s exposure.
The efficiencies have bred more productivity for Laurentian Bank’s financial crimes crew. Because of the bank’s increased effectiveness at spotting irregular transactions, “the numbers are not staying still. They’re going up,” Quevillon says. More staff must be assigned to handle the increased volume of cases.
Combating fraud and money laundering.
- Visualize relationships between entities and uncover unknown relationships while helping to identify the use of synthetic IDs.
- Move from a transaction and product view of fraud risk to a view at the customer level.
- Spot criminal networks by cross-referencing across several cases and identities.
- Detect fraudulent transactions in near-real time.