By Subhasis Bandyopadhya, Head of BFS Practice, Mindtree
As both businesses and society today continue to embrace digitisation at an uncontrollable rate; fighting financial crime, money laundering and the funding of criminal activity are all growing in importance.
The global phenomenon of digitisation has led to an increase in wire banking, thus raising pressure on banks and financial institutions to monitor and detect suspicious activity to prevent it coming to fruition. It is therefore crucial that the banking system today adopts the latest tools for a mature digital service, which include self-learning capabilities that are able to quickly adapt to the ever-changing banking environment.
The recent narrative has been primarily focused on regulators ramping up demands for greater scrutiny of transaction monitoring, as well as threatening to impose hefty fines on non-compliant banks.
In Australia, for example,The Sydney Morning Herald recently reported that the Commonwealth Bank has come under extensive scrutiny following an internal review of the bank’s breach of global anti-money laundering and counter-terrorism laws, with legal proceedings now being levelled against it.
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Having spent 25 years working with banks and organisations across the financial services industry, I have seen how growing data sets, disparate transaction data systems, and integration issues with monitoring systems, coupled with ever-evolving regulatory practices, require innovations that humans simply cannot deliver alone.
Unsurprisingly, artificial intelligence (AI) and machine-learning are driving significant technological developments in this space, as the digital era has required banks to move beyond traditional business models, and adopt dynamic predictive models. Doing so will enable real-time, transaction-based know your customer detection techniques.
New methods of machine-learning can focus on anti-money laundering detection, and delivering suspicious activity reports (SAR), meaning that industry experts are now, slowly but surely, beginning to see the full potential of analytics.
Regulators too are increasingly coming to the conclusion that mere rules alone are not an effective means of detection and prevention, and they too are progressively encouraging banks across the board to opt for greater adoption of said technology.
However these new technological methods can inevitably lead to implementation issues.
For example, setting up the appropriate threshold levels and parameters is an ongoing difficult to overcome. When thresholds are set too low, a system will populate itself with an unnecessarily high number of alerts, all of which require analysis. However, if these thresholds are set too high the amount of alerts will decrease as a result, meaning the company may be unable to detect all suspicious activities, thus risking failure to meet regulatory requirements, could result in reputational or financial exposure.
Identifying false positives in data, quickly and accurately, is an existential problem too. Due diligence and in-depth analysis of any alerts may be time-consuming, but detailed and thorough scrutiny that ensures compliance with existing governance processes is critical. Taking action on false positives at the right time, and removing them as quickly as possible is likely to be one of the biggest challenges facing banks and financial services firms.
Other challenges also present themselves in the form of compliance with global and regional laws and regulations, the need for accurate and timely reporting, and, streamlining operations to minimise costs, to name but a few.
In order to address these problems, turning to AI and machine-learning solutions is the key to progress. Banks and financial institutions need to have robust decision support systems in place, and this can be achieved by leveraging machine-learning powered predictive analytics platforms that can continuously evolve with new data points and user analysis.
Digital platforms will not only ensure high accuracy in decision-making but also provide detailed audit features too. Banks can then improve their predictions as well as driving operational efficiency, in parallel with current static rule-based applications to drive progress.
AI and machine-learning techniques have also been responsible for identifying significant spikes in value or volume of transactions; monitoring high risk jurisdictions, identifying rapid and unusual movement of funds, screening both sanctioned and politically exposed individuals, as well as monitoring for activity from known terrorist organisations. Thanks to such repetitive analysis, these developments will benefit banks and financial services immensely.
Machine-learning platforms can then create dynamic workflows; self-learning technology with the ability to identify only those transactions that represent genuine risks, real-time auditing capabilities, accurate confidence scores for enhanced business comfort, and, deliver deployment models faster to market than ever before.
By replacing legacy and age-old systems with leading-edge AI and machine-learning platforms, banks can not only substantially reduce operational costs, but more effectively cope with data imbalances, improve alert predictions, and deliver on promises of accuracy and compliance – welcoming a new age of finance.