It is a difficult time to be fighting financial crime. PWC’s 2018 survey titled ‘Pulling fraud out of the shadows: A spotlight on the Middle East’ highlights that fraud and economic crime has increased to 34% from 26% in 2016. This increase is a colossal impact to Islamic banks resulting in falling share prices, fines, reputational and client/partner relationship damage and such. It also means more pressure on systems and processes which increases operational costs and time spent to accurately identify financial crime. In this developing digital environment, where we live in a real-time world, fighting sophisticated financial crime techniques feels like an impossible challenge. Fadi Yazbeck explains the possibility of accurately outsmarting these criminals and maintaining efficiency.
Digital demands real-time fraud analysis
Islamic bank customers expect a banking experience that matches their digital lifestyles. Within the Middle East and parts of Northern Africa in particular, digital is a way of life, with the UAE having the highest smartphone penetration in the world at 82.2% according to Wiki. And the rest of the region is set to catch up: 463 million smartphones are expected to be in use by 2020 according to GSMA’s ‘The Mobile Economy Middle East and North Africa 2017’ report. These digital users are your customers. They expect to be able to transact instantly and this means that fraud needs to be identified immediately. But real-time transactions, however, make identifying illegal transactions a real challenge. This is because they are processed as they happen and cannot be reversed – there is no time for manual fraud review steps. Addressing fraud in real-time using just historical data alone is not sufficient. Artificial intelligence (AI) may hold the answer.
Accuracy through AI
One of AI’s key features is its ability to detect patterns and recognize small deviations that occur which seem irrelevant to create an intricate profile. This functionality is perfect to support financial crime mitigation. For example, location data can be collected from a customer’s phone on an ongoing basis, looking beyond just a one-time snapshot of a mobile payment transaction. This machine learning approach would enable a profile of habitual behavior for that customer’s mobile payment activity. As a result, the profile is more accurate with alert detection and false positives are reduced.
As well as increased profile accuracy, machine learning could also offer greater efficiency (and accuracy) when investigating possible fraudulent transactions. Many existing fraud systems have no automated decision workflow and rely mostly on manual review. This makes Islamic banks vulnerable to exploitation, eg hitting Islamic banks with a number of attacks in short time frames. This type of attack bombards the system, making it hard for an Islamic bank’s fraud department to address the multitude of cases and forcing them to act quickly to both protect their customers and themselves.
As machine learning capabilities develop, there is no reason why the current roles of human employees investigating these transactions could not be managed using this new capability, making an Islamic bank’s business operations more efficient. In addition, because manual investigation takes time (in many Islamic banks’ policies, as part of the investigation process, it may take 45 to 60 days to refund customers who have been defrauded), this often means poor customer experience. If the use of AI can shorten this period, then trust is increased and damage can be limited.
Reducing false positives
False positives are a major issue for banking operations in general. From talking to banks with standard financial crime software, between 5-7% of transactions are identified in error as related to financial crimes such as money laundering, fraud, sanctions and breach. That’s a lot of transactions to review (often manually). Potentially good customers may be treated unfairly unless they are identified as ‘false positives’ quickly and addressed effectively.
However, it is possible to bring down the false positive rate. Significantly reduced false positive incidents can be realized by using sophisticated algorithms and highly effective scanning methods. This combination of algorithms and methods allows for a higher threshold in fuzzy matching and, as a consequence, reduces the number of false positives without compromising on accuracy in hit detection and, ultimately, realizing enhanced efficiencies. Low false positive rates mean seamless transactions, and in today’s digital work, seamlessness is key.
Achieving real-time accuracy and efficiency
In PwC’s 2018 survey, 82% of respondents agreed that the use of continuous real-time monitoring assists their organization in combating economic crime. Systems that use AI and real-time behavior analysis build user and customer profiles to detect and stop suspicious transactions with elements such as unusual amounts, abnormal frequency, suspicious location and transactions to not‑seen‑before business partners. They often take into account elements such as transaction amount, currency, transaction type and frequency parameters, which can be combined and compared to the ‘usual behavior’ or predefined patterns.
Ultimately, in the fight against financial crime, success all comes down to automation. Working with an agile, real-time system that utilizes AI and sophisticated algorithms with advanced scanning methods provides Islamic banks with an essential function: the ability to accurately stop suspicious transactions before the funds are moved, and thereby outsmarting the criminals efficiently and allowing Islamic banks to focus on what’s important – their customers.