Posted January 10, 2017 by Nick Nichols
I don't need to tell you that the pace of regulatory reform is increasing. In the last 15 years, the mutual fund industry has responded to more than 30 reforms, 10 of which were either introduced or finalized in 2016 alone. A majority of these reforms were passed in response to some sort of crisis, triggering a reactive approach where mutual fund companies are frequently meeting their regulatory mandates by implementing surveillance and reporting solutions often strung together by disjointed systems and disparate - and disconnected - data sources. In such an environment, the focus is on keeping up rather than on how to implement a thoughtful and systemic effort to help prevent compliance-related events such as fraud. While the results may technically be in compliance with legal and regulatory requirements, they are usually less efficient and, worse, may not be as effective in identifying potential acts of fraud.
Unfortunately, we're seeing this happen over and over again. In April 2016 a records leak from a large law firm in Panama disclosed more than 200,000 companies that were involved in money laundering and sophisticated methods of concealing financial crime. These disclosures, which are referred to as the "Panama Papers," showed that some financial institutions were being used to facilitate illegal acts. Many financial services compliance departments are trying to determine if their firms have exposure to the companies denoted in the Panama Papers.
This is just one of more than a dozen stories that made headlines in 2016, revealing potential regulatory and reputational risk for financial institutions which aren't thinking proactively about their fraud detection efforts. As the pace of reform accelerates, we believe that asset management firms need to consider a holistic approach to fraud monitoring, detection and documentation.
Lessons from the field of data analytics show us there may be a way to more proactively manage fraud detection with enhanced efficiency and accuracy.
By integrating tools and processes into your compliance program that:
One example of how behavioral analytics might be used is in the investigation of portfolios held by your firm or your shareholders to assess concentrations. Data analysis can help to examine exposures as of a particular date, by a single security or security type by issuer, by country of origin, across all portfolios of the firm, or at account, branch, fund, or organizational level. Other firms are using behavioral analytics to create customized watch lists based on high-risk profiles generated by their analysis of their firm's data.
Asset management firms aren't the only institutions exploring data analytics to increase the efficiency and effectiveness of their work. The SEC's Office of Compliance Inspection and Examinations announced in 2016 that they would be integrating data analytics into the program to help them identify people and firms with a higher risk of misconduct in order to better leverage their limited examination resources. Among their priorities:
Want to talk more about leveraging behavioral analytics to help increase the efficiency and effectiveness of your fraud detection program? Join us for the annual DST Advance conference, hosted from February 20-22, 2017 at the J.W. Marriott Desert Ridge in Phoenix, AZ. Throughout the conference, we'll address questions like: