Banks and financial institutions are facing an increasing regulatory burden. Ever since the financial crisis of 2008, regulatory requirements have become stricter and more abundant. Banks are hiring thousands of employees and spending millions of dollars to comply with the ever-evolving regulatory rules, yet many still fail to do so. Those are forced to pay billions of dollars in fines, and suffer major reputational damage too.
These are all issues that could have been avoided, had the institutions implemented consistent data architectures and deployed appropriate analytics engines. In this three-part blog series we explore several common compliance breaches and how to resolve them using automated tools. In the first post of this series we will show how customised data analytics can help expose the hidden PEPs in the client base and how standard screening tools support in resolution of these breaches.
Step One – Treat the PEPs Properly
The first compliance violation that we address in this three-part blog is the failure to identify a person as a PEP (politically exposed person), or the failure to treat an identified PEP as such. While specific definitions and rules vary from one jurisdiction to another, most of them agree on the following. Persons, who are entrusted with some form of prominent public function, such as senior politicians, national leaders, senior executives of state-owned companies and their close family members, are formally classified as a PEP and should undergo stricter due diligence. An organisation must assess the risk such a relationship would bring, then implement corresponding adequate safety measures. Failure to do so is punishable by both national and international compliance authorities and, in some cases, can have severe reputational impact.
Usually, a PEP flag resides in a separate field of the KYC software and database and accepts binary values: yes or no. It is possible that other KYC fields on a said individual, such as their educational background or business activities, also hold some information regarding the person’s political exposure, but in a form of free text. If there is incoherence between these fields, the KYC is not compliant. However, this breach does not necessarily appear as a result of deliberate malicious intent. Human error, KYC software bugs, data corruption – all these are also viable causes of the breach. Regardless of the specific scenario, it is difficult and time-consuming to identify all potential violation cases across a financial institution’s entire client base. Luckily, data analytics and Natural Language Processing (NLP) allow for an automatic and scalable solution to this problem.
Step Two – Expose hidden PEPs Leveraging Data Analytics
In order to distinguish PEPs from other individuals, we should identify the descriptor variables – such characteristics that are significantly different between the two client groups. Analysing the KYC data on existing client relationships, we come up with candidates for such characteristics and test their significance. Could it be that PEPs have more accounts on average? Could it be that PEPs share accounts with other PEPs? Or maybe their KYC data contain some specific words and phrases, such as “Ambassador”, “Political career”? Coming up with these characteristics requires substantial knowledge and experience of working with PEP individuals. Testing whether there is a significant difference between the two groups requires knowledge of statistics and mathematics.
Stemming from our experience with different clients, a set of defining characteristics could look as follows:
Data Analytics helps finding defining characteristics to expose PEPs across the client base
Each row in the above diagram represents a defining characteristic that, according to statistical tests, takes significantly different values between the two groups, PEP and other individuals. A combination of these characteristics forms a filter, which efficiently exposes PEPs. Namely, if an individual’s KYC profile satisfies a significant part of these characteristics and is not marked as a PEP, they are potentially missing the risk flag. Similarly, we could construct a filter for other risk flags, such as sensitive business area or complex business structure.
Such KYCs are a subject for immediate review by a compliance officer. The KYC solution from Finantix, an intelligent client screening tool powered by smartKYC, offers cross-source search possibilities combined with smart filtering and aggregation of the discovered information. Using this artificially intelligent tool, compliance specialists can efficiently collect recent news and information on the individual. Afterwards the expert can make an informed decision regarding the PEP status of the client and correct the data in the KYC database accordingly, thus eliminating the breach.
In this blog, we have explored how data analytics in combination with intelligent media screening offers automatic detection of risk flag-related breaches and support in resolution of such breaches. In the second of this three part blog series, we will examine another common compliance breach: undetected transactional activity with sanctioned countries.
Join us for a two-part webinar series in which we explore the concrete application of Artificial Intelligence to KYC and AML. Select the most convenient session for you and register using the links below. See you there!
Register here Part One – Using the power of semantic search to build comprehensive client risk profiles and automate continuous KYC monitoring
Register here Part Two – Applying advanced data analytics to implement effective transaction monitoring and improve the quality of KYC files