In the last two decades, financial crimes have been steadily rising across the globe—not only in terms of volume, but also in complexity and sophistication. As a result, banks and financial institutions are grappling with the burden of direct losses and staggering remediation and compliance costs stemming from their inability to detect such crimes. To manage this risk, banks and financial institutions have therefore been investing heavily in technology for enhanced prevention, detection, investigation and reporting of financial crimes.
Analytics and statistical modeling are being increasingly deployed by banks to enhance the effectiveness of financial crimes risk management and compliance. While many banks worldwide have been advocating predictive and detective analytics-based transformation in anti-money laundering and anti-fraud compliance, entity analytics is emerging as a solution to challenges in know your customer (KYC) and customer due diligence capabilities as banks and financial institutions are plagued with siloed systems, and missing and outdated customer information.
The lack of an enterprise-wide, single customer view is one of the key challenges banks face in know your customer, case investigation for anti-money laundering and fraud control. Customers holding multiple relationships with a bank, as part of different lines of businesses, are onboarded through disparate systems and, more often than not, such information is inconsistent. Their transactions and account behaviors are also captured in various different platforms. This results in fragmented and mismatched customer data residing in multiple systems, thereby making it impossible for the bank to have a 360-degree view of a person’s overall profile and to capture transaction behavior across the bank.
An illustration of this scenario is provided below, in which multiple transactions executed by the same customer using accounts linked to different profiles do not roll up to a single record. This makes it difficult to have a single view of the customer across his multiple accounts.
As part of KYC and CDD, it is essential for banks and financial institutions to identify multiple profiles of a single customer, using more than one set of KYC attributes (e.g. name, address, phone number, government/national ID and other such personal attributes). Entity analytics solution can help to determine when more than one disparate customer KYC profiles actually pertain to the same customer, even if the data is inconsistent. For example, by comparing names, addresses, phone numbers, national IDs, bank details, email IDs and other personal attributes across different profiles, this solution can discover that three customers in the above example— Yolande Tokell, Yoland Tokel and Y Tokell—are in fact the same person.
Analytics-based entity resolution solution is capable of generating single view of a customer maintaining multiple identities and relationships across the bank, without having to overhaul any of the disparate legacy systems of the bank that hold the customer data. Sophisticated analytics are used for such matching, sometimes augmenting bank’s data with that of external third-party data to arrive at accurate matches.
As entity analytics in the context of identity resolution—”who is who?”—for enterprise wide customer behavior aggregation gains traction, it is now being extended to network resolution—“who knows who?”—by using entity analytics to unearth hidden networks and suspicious relationships between banks customers and external parties. The entity analytics solution links the data available in the bank, such as unstructured customer data in the form of mails and chats, with that available in external sources, including social media and web-based information, to unearth suspicious criminal networks. Such networks can then be red-flagged by the bank, and subjected to greater scrutiny for detecting financial crimes.
Entity analytics is helping banks transform their financial crimes compliance landscape, through non-invasive solutions, enhanced compliance in terms of more effective operations and better decision making based on the analytical insights generated.