Payment screening allows banks and financial institutions to check whether incoming and outgoing payments pose a risk or are in breach of compliance regulations. But how to strike the right balance between cost and risk?
Checking transactions data against sanctions and embargo lists costs money – but failing to detect a compliance risk can be even more expensive, resulting in severe penalties and reputational damage. What is to be done, particularly when financial regulators are constantly tightening the reins?
Financial regulators increasingly require the use of fuzzy matching. This means that it is still required to identify risky transactions even if the name in the transaction does not exactly match the name on the sanctions list, perhaps due to typing errors or even the deliberate misspelling of names. The problem is that a fuzzy search always produces more hits than an exact search, which increases the workload of compliance teams.
Financial institutions find themselves caught between the conflicting priorities of effectiveness and efficiency. This means:
Financial institutions are under extreme pressure to comply with regulatory frameworks. The work of compliance departments is dominated by the need to prevent financial crime. Payment screening has a vital role to play in this respect. It is a key component of risk management that protects financial institutions from legal consequences, penalties and damage to their reputation.
Real-time payments and other digital payment options mean that risk decisions have to be made faster than ever before. Banks have to strike a balance between due diligence, customer expectations and the cost situation.
During the screening process, the compliance software checks payment data against sanctions lists, embargo lists and other blacklists. This data matching is based on criteria such as first and last name, company name, alias name, alternative spellings, countries involved, banks, BICs, accounts, amount, keywords and whitelist exceptions. Financial institutions are under increasing pressure to ensure their systems are both efficient and effective.
If a hit, i.e. a potential match to a sanctions list entry, is discovered during payment screening, this process has to be clarified manually. Banks can’t afford to miss anything – failure to spot a suspicious payment could entail serious legal and financial consequences.
Banks usually carry out effectiveness tests every year using predefined test cases. These check whether the system is identifying the hits it has to find in order to ensure compliance. These tests are usually done using test data rather than real data to avoid confidentiality issues.
They are normally conducted by independent third parties such as management consultancies and auditing firms. They use benchmarks from several institutions to compare a bank’s payment screening performance against that of their peers.
The efficiency test examines how much work a hit generates when screening a payment. Every hit has to be reviewed by a member of the compliance team, who decides whether the name that appears on the payment is actually the person on the sanctions list. The percentage of the false positives, i.e. hits that do not represent a real threat, should be as low as possible. This is because unnecessary hits (false positives) increase the workload of compliance staff without posing an actual risk. The efficiency of a system depends on finding the fewest possible false positives.
The fact that sanctions lists are growing longer and banks are regularly screening transactions increases the likelihood of false positives, for example when customers have an identical or similar name to a sanctioned entity. Processing every hit requires time and money, which is why banks are working hard to improve the efficiency of their payment screening systems.
Striking the right balance between effectiveness and efficiency – a tricky business
Achieving the twin goals of effectiveness and efficiency in payment screening is rarely easy because it is always a trade-off between risk and cost. On the one hand, the system has to identify every risky transaction for compliance reasons. But on the other, banks want to save money by handling as few clarifications as possible.
A combination of Natural Language Processing and Machine Learning is the best solution to this dilemma: The best possible blend of effectiveness and efficiency is achieved by combining different matching algorithms with NLP and Machine Learning.
Why compliance teams are feeling stressed?
All over the world, financial institutions are investing in compliance staff to investigate the growing number of suspicious cases – but this comes at a price. Costs are soaring, and so is the danger of making wrong decisions. According to industry estimates, the projected total cost of financial crime compliance across the global finance sector amounts to around $180.9 billion. According to the study, Europe and the US are hardest hit by financial compliance costs because they have significantly higher numbers of financial institutions. On average, the global distribution of compliance costs stands at 57 percent for labour, 40 percent for technology and 3 percent for other factors. The study also finds that compliance teams feel stressed, and 67 percent of compliance decision-makers are concerned about the job satisfaction of their workforce.
The best solution: Using NLP and Machine Learning in payment screening.
Customers are increasingly communicating with their banks via digital channels, so they expect to have a fast and secure digital experience. This means that the payment screening system must be powerful enough to handle this shift in consumer behaviour and deliver the speed and reliability that is needed.
Natural Language Processing – a branch of Artificial Intelligence – allows unformatted, unstructured data to be read with contextual matching. So that it utilises a human-like intuitive processing to handle complex screening scenarios. Furthermore, Fuzzy matching makes it possible to match transactions more effectively. It also identifies anomalies in the event that names are reversed, falsified or misspelled, a letter is added, abbreviations are used, etc. But there is another side to the coin: a fuzzy search always produces more hits than an exact search. Therefore, the screening system has to be intelligent enough to flag only risky payments (true positives) and keep false positives to a minimum in order to avoid unnecessary investigation and alert review costs. Finally, Machine learning allows to automatically learn from past system actions and also follow-on user actions to build models and automatically treat the payment is a true hit or a false positive.Learning from the historical operator behaviour and simulates their approach to take operator like decisions. Furthermore, Continuous Learning facilitates continually learning from operator’s behaviour and acts as an experienced and smart human operator. It continually improves based on collective learning of all operators.
To learn more - https://www.pelican.ai/solutions/pelican-secure/sanctions-screening