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Wondering how to keep up with increasingly difficult regulatory compliance demands? See how banks are using AI and machine learning to streamline processes.
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 on three key fronts, 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.
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.
Know Your Customer (KYC)
Data-driven insights are everything when it comes to Know Your Customer (KYC) Compliance. Banks can count on machine learning techniques to expedite and improve the way they collect and verify customer data before running risk assessments. Banks’ perennially elusive goal—to gain a single view of a customer 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 customer segmentation and profiling that can assist compliance efforts while extending into things like personalized marketing. Furthermore, the data that financial institutions collect during KYC processes can be fed into business intelligence systems, thereby enabling process visualizations and fueling additional efficiencies.
Sanctions Screening and Transaction Monitoring
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.
“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.