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Which financial institutions are most likely to thrive and lead in the future? The smart money says it will be the ones that swiftly, successfully adopt and master new technologies. One perfect place this theory is playing out: the increasingly complex regulatory compliance landscape. While some banks persist in trying to keep up via laborious manual processes, others are getting a leg up by leveraging big data and advanced analytics techniques.
Banks have plenty of incentive to stay on the right side of regulators. Not only is it critical to avoid reputational harm, the costs of getting caught up in a financial crime world of increasing sophistication can be astronomical.
The result is an intense desire to comply with regulations for sanctions screening, anti-financial crime and bribery, and supply chain due diligence across which global regulators have dished out (according to EY) a whopping US$26 billion in fines over the past 10 years.
Regulatory reporting requires work—lots of it. The question for banks is, which parts of that reporting can they automate and where can they find cost reductions along the way? By putting machine learning tools into place, banks can lift the burden on employees who would rather focus on preventative elements of their job. As data volumes continue to grow exponentially and the sheer number of data sources similarly proliferates, the task is far from easy.
Big Data and Analytics Drive Efficiency in Compliance Process
How are financial institutions automating key tasks that were previously manual? By applying advanced data and analytics techniques—things like machine learning, artificial intelligence and natural language processing. The impact has been noticeable. In fact, PwC has suggested that by reducing handling time and increasing quality, banks using properly deployed technology can lower their compliance costs by some 30-50%.
Let’s look at how they’re doing it.
Sanctions screening is one of many legacy processes under duress these days. As regulatory demands grow in both volume and complexity, the risk detection capabilities provided by existing systems are often exposed as slow and error prone. False positives can be an especially acute problem.
To combat this, banks can use AI, machine learning and cognitive analytics to streamline their screening processes. These technologies can also play a part in enabling them to expand the overall number of sources. Machine learning helps ensure that the data being screened is as accurate as possible and fine-tunes filtering parameters over time, improving efficiency. Practitioners can combine machine learning techniques with predictive calculations, ensuring that investigators are able to better home in on true positives.
When it comes to transaction monitoring, banks can take their existing rule-based approach and augment it with advanced data analytics tools. Thanks to AI and machine learning algorithms, they can now analyze vast amounts of transaction information.
Data-driven insights are everything when it comes to addressing anti-bribery and corruption or supply chain due diligence needs. Banks can count on machine learning techniques to expedite and improve the way they collect and verify third-party data before running risk assessments and enhanced due diligence. Banks’ perennially elusive goal—to gain a single view of critical business partners and suppliers across as many internal and external systems and touchpoints as possible—has become even harder to achieve in recent years. But analytics can pave the way for enhancing compliance efforts while extending into supply chain optimization. Furthermore, the data that financial institutions collect can be fed into business intelligence systems, thereby enabling process visualizations and fueling additional efficiencies.
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“Adapt or die” may be a little strong here but reality is staring many financial institutions in the face. The truth is, the times of effectively building a compliance strategy around manual processes are over. The time to embrace technology is now and the twin spoils of efficiency and profitability will surely go to those banks that thoughtfully integrate AI and machine learning at the earliest possible moment.