Digitization of customer relationship initiation processes, automatic assessment of the risk of cooperation with a customer and smart algorithms to analyse transactions. These and many other technological solutions can eliminate the biggest threat for banks in the area of compliance management and AML: the human factor. The “meshes of the digital net” designed to pick out scammers from the sea of banking customers will be getting smaller. In the age of digital banking, there will be less and less room for illegal operations involving money laundering and violation of international law. Is this the end of scandals such as those revealed in several Nordic banks in recent months? Adam J. Kępa, VP and Head of Growth at ITMAGINATION, talks on the best KYC and AML solutions for banks.
Banks have a duty to assess the risk of cooperation with customers and contracting parties (at the start of cooperation and periodically afterwards), as well as to screen all operations carried out by customers for any illegal transactions, financing of crime, terrorism, money laundering or circumventing economic sanctions. This field of banking risk management is called AML (Anti-Money Laundering). A financial institution may pay dearly for failing to fulfil its obligations in this area.
In the 10 years since the economic crisis of 2008, ineffective prevention of money laundering, circumventing economic sanctions and breaches in the area of KYC (Know Your Customer) cost financial institutions a total of approximately USD 26 billion. This is the sum of penalties imposed on banks around the world on that account, according to Fenergo. At the end of 2018, Estonian prosecutors detained 10 employees of a local branch of Danske Bank in connection with probably the biggest scandal in Europe, involving laundering of more than USD 230 billion. Investigation of this case is still underway. In the same year, US regulators imposed a penalty of USD 1.3 billion on one of the largest French banks, Societe Generale, for laundering almost USD 13 billion. Dutch ING, on the other hand, paid USD 900 million for breaching money laundering regulations last year.
The law requires banks to identify all customers using specific types of data (including first name, last name, personal identification number, citizenship, identity document number and address of residence). For companies, this means an obligation to confirm the name, organizational form, registered address or business address and tax ID number, but also to determine the ownership and control structure of the company, as well as its business objective and the nature of its operations. A bank is also required to collect and transmit information about any transaction with a value of over EUR 15,000 and alert the relevant services about circumstances that may suggest a suspicion of an offence of money laundering or terrorist financing.
Under traditional customer registration processes, all information identifying customers was entered into banking systems manually, e.g. by bank branch staff, based on paper forms or copies of documents. Today, these operations can be carried out 100% digitally and online, without human intervention. This is made possible by solutions such as barcode and QR code scanning, OCR, or automation of data acquisition for customer identification.
Digitization of bank account opening process is not only about convenience or economy. From the risk management perspective, it allows you to substantially improve the efficiency and effectiveness of your credibility assessment, right from the start of your bank’s relationship with a customer. With a well-programmed onboarding system, customer assessment can be fully automated and virtually unnoticeable to the person concerned, right at the first contact. It is an extremely effective protection for a bank. At ITMAGINATION we have developed and implement a Digital Onboarding & KYC Compliance class system in financial institutions.
How does it work? ITMAGINATION’s system allows a bank to automate as many elements of customer onboarding process as possible. Documents are processed in a digital form, through the use of OCR (Optical Character Recognition) technology and QR code and barcode scanners. AML assessment of customer risk level is made based on data derived from those documents, information collected automatically from public records, such as Central Registry and Information about Businesses and National Court Register, business intelligence firms, credit information agencies, sanctions lists, lists of politically exposed persons (PEP) and a database of documents reported as lost or stolen. Bank’s internal resources are also used, such as in-house databases of preferred or prohibited customers, and data from transactional, CRM or data warehouse systems.
Automation of customer data acquisition processes is just the beginning. Responsibilities in the area of AML and the protection of bank customers against phishing and theft, e.g. with the use of credit card data, also force banks to constantly screen transactions executed in their systems. Today, ‘red lights’ light up when customer activity goes beyond the standard (e.g. a customer suddenly attempts to make a payment to a country covered by OFAC sanctions) or a series of behaviors that indicate a potential violation can be noticed. This is where innovative data science and AI solutions enter the scene.
Real time data analytics based on artificial intelligence and machine learning make it possible to analyze millions of customer activities at the same time. A banking system can not only note that an AML incident has taken place, but it can also predict such situations based on collected information about circumstances suggesting a suspicion that a crime of money laundering or terrorist financing has been committed. All you need are cleverly used, relatively simple algorithms. Moreover, from a bank’s perspective, it is extremely important that the process is done automatically. Technology allows for millions of operations to be analyzed simultaneously, and makes it possible to reduce almost to zero manual interference in the analysis of such searches. A human only steps in where the situation is non-standard. Importantly, the system ‘learns’ and optimizes itself, so its forecasting accuracy keeps growing.
The development of IT solutions supporting the areas of AML and Compliance in banks will go towards further digitization of service processes, increasingly widespread use of data analytics from a variety of sources and automated event prediction. At the onboarding level, we are talking about introducing solutions such as face and emotion recognition using AI or searching the web for bad publicity (Adverse News and Ultimate Beneficial Owner (UBO)).
Learn it. Know it. Done.
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